9507 lines
411 KiB
Plaintext
9507 lines
411 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a50898c4-de05-4d74-b3af-528cbe6b0a64",
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"metadata": {},
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"source": [
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"# Lab Work 2: Text Processing: Preparation of texts\n",
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"\n",
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"Use this notebook for the subsequence excecise's parts.\n",
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"\n",
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"**Please note, that you can only pass the intial checking, if you write Markdown documention about your findings (not code documentation). Any submission that does not adhere to that will lead to an immediate fail, without the chance of resubmission!**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fe962d49-11f0-4a11-80d7-0cc290cf1f6b",
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"metadata": {},
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"source": [
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"## 6.2.1 Load the data and CountVectorize them\n",
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"You will find a list of files in Ilias [sherlock.zip](https://www.ili.fh-aachen.de/goto_elearning_file_815003_download.html)\n",
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"Download the zip file and adapt your next line accordingly."
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]
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},
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{
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"cell_type": "code",
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"id": "29c253cc-3060-4da3-bc9b-1aa5f5874db1",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-15T14:01:01.846362881Z",
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"start_time": "2025-12-15T14:01:01.832960844Z"
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}
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},
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"filenames = [r\"./Sherlock.txt\", \n",
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" r\"./Sherlock_blanched.txt\",\n",
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" r\"./Sherlock_black.txt\",\n",
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" r\"./Sherlock_blue.txt\",\n",
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" r\"./Sherlock_card.txt\"]"
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],
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"outputs": [],
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"execution_count": 8
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},
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{
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"cell_type": "markdown",
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"id": "286d1d4f-78cb-40b4-aae8-d69df3460b4b",
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"metadata": {},
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"source": [
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"Now we create a count Vectorizer. The parameter given tells the CountVectorizer that its methods shall operate on a list of filenames."
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]
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},
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{
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"cell_type": "code",
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"id": "dee15bba-e43b-4e4b-b1a2-d798411820cb",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-15T14:01:01.904231100Z",
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"start_time": "2025-12-15T14:01:01.872546121Z"
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}
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},
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"vectorizer = CountVectorizer(input=\"filename\")"
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],
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"outputs": [],
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"execution_count": 9
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},
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{
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"cell_type": "markdown",
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"id": "cca48347-0f31-47e0-8432-183f61b38222",
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"metadata": {},
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"source": [
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"Now generate the Bag of Words with the CountVectorizer and check:\n",
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"* the total number of different words\n",
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"* the total number of words per document\n",
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"* the total number of occurences of each word"
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]
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},
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{
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"cell_type": "code",
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"id": "f94a5742-9b26-40ff-a093-b5f0f0bce12f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-15T14:01:02.160184470Z",
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"start_time": "2025-12-15T14:01:01.928765726Z"
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}
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},
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"source": [
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"# create the bag of words matrix\n",
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"bag_of_words = vectorizer.fit_transform(filenames)\n",
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"\n",
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"# count the total number of unique words in all documents, which corresponds to amount of columns in the matrix\n",
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"feature_names = vectorizer.get_feature_names_out()\n",
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"total_unique_words = len(feature_names)\n",
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"\n",
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"# count the total number of words in each document\n",
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"words_per_document = bag_of_words.sum(axis=1)\n",
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"words_per_doc_flat = np.asarray(words_per_document).flatten()\n",
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"\n",
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"# count the total number of each word in all documents\n",
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"word_counts = bag_of_words.sum(axis=0)\n",
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"word_counts_flat = np.asarray(word_counts).flatten()\n",
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"\n",
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"print(f\"Total number of different words: {total_unique_words}\")\n",
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"print()\n",
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"\n",
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"print(f\"{'Document-Name':<30} {'Word count'}\")\n",
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"print(\"-\" * 45)\n",
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"for filename, count in zip(filenames, words_per_doc_flat):\n",
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" print(f\"{filename:<30} {count}\")\n",
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"print()\n",
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"\n",
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"print(f\"{'Word':<30} {'Count'}\")\n",
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"print(\"-\" * 45)\n",
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"for word, count in list(zip(feature_names, word_counts_flat)):\n",
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" print(f\"{word:<30} {count}\")\n"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total number of different words: 8879\n",
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"\n",
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"Document-Name Word count\n",
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"---------------------------------------------\n",
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"./Sherlock.txt 107416\n",
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"./Sherlock_blanched.txt 7258\n",
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"./Sherlock_black.txt 7775\n",
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"./Sherlock_blue.txt 7497\n",
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"./Sherlock_card.txt 8242\n",
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"\n",
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"Word Count\n",
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"---------------------------------------------\n",
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"117 2\n",
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"12 1\n",
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"12s 1\n",
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"13 1\n",
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"131 2\n",
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"13th 1\n",
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"14th 1\n",
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"15 1\n",
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"16 2\n",
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"1840 1\n",
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"1872 1\n",
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"1881 1\n",
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"1883 10\n",
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"1884 3\n",
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"1887 1\n",
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"1888 1\n",
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"1892 1\n",
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"1893 1\n",
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"1894 4\n",
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"1895 1\n",
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"18th 1\n",
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"1900 1\n",
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"1901 2\n",
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"1903 1\n",
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"1904 1\n",
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"1926 1\n",
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"1st 2\n",
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"20th 1\n",
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"22 1\n",
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"221b 1\n",
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"22d 1\n",
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"22nd 2\n",
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"23rd 1\n",
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"249 1\n",
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"26 1\n",
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"30 5\n",
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"3rd 1\n",
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"426 1\n",
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"427 3\n",
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"45 2\n",
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"46 2\n",
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"50 1\n",
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"6d 1\n",
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"6th 1\n",
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"7s 1\n",
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"83 2\n",
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"87 1\n",
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"95 7\n",
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"97 1\n",
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"aback 3\n",
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"abandon 7\n",
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"abandoned 1\n",
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"abandoning 1\n",
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"abbey 11\n",
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"abduction 4\n",
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"abe 8\n",
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"abergavenny 1\n",
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"abernetty 1\n",
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"aberration 1\n",
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"abetted 1\n",
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"abhorrent 2\n",
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"abhors 1\n",
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"abide 1\n",
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"ability 1\n",
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"able 44\n",
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"abnormally 2\n",
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"aboard 2\n",
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"abode 1\n",
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"abominable 1\n",
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"abortive 1\n",
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"about 211\n",
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"above 27\n",
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"abrasion 2\n",
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"abreast 1\n",
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"abroad 4\n",
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"abrupt 2\n",
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"abruptly 1\n",
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"absconding 2\n",
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"absence 9\n",
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"absent 8\n",
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"absolute 18\n",
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"absolutely 19\n",
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"absorbed 9\n",
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"abstracted 4\n",
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"abstruse 2\n",
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"absurd 13\n",
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"absurdly 3\n",
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"abuse 1\n",
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"abyss 3\n",
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"academic 1\n",
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"accent 1\n",
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"accept 4\n",
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"acceptable 1\n",
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"acceptance 1\n",
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"accepted 2\n",
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"accepting 2\n",
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"access 1\n",
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"accessory 1\n",
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"accident 10\n",
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"accidental 1\n",
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"accidentally 1\n",
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"accompanied 2\n",
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"accompaniments 1\n",
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"accompany 2\n",
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"accomplice 4\n",
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"accomplish 1\n",
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"accord 1\n",
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"accordance 1\n",
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"according 3\n",
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"account 51\n",
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"accounted 2\n",
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"accounts 6\n",
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"accumulating 1\n",
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"accumulation 1\n",
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"accuracy 2\n",
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"accurately 1\n",
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"accursed 1\n",
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"accusation 1\n",
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"accuse 4\n",
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"accused 2\n",
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"accuser 1\n",
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"accustomed 10\n",
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"acid 1\n",
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"acknowledge 3\n",
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"acknowledged 1\n",
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"acquaintance 4\n",
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"acquaintances 2\n",
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"acquainted 1\n",
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"acquiescence 3\n",
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"acquired 3\n",
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"acquiring 1\n",
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"acquitted 1\n",
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"acres 1\n",
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"acrobat 1\n",
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"across 54\n",
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"act 12\n",
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"acted 11\n",
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"acting 2\n",
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"action 27\n",
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"actions 3\n",
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"active 12\n",
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"actively 1\n",
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"activity 1\n",
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"actors 3\n",
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"actress 1\n",
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"acts 2\n",
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"actual 4\n",
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"actually 10\n",
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"acumen 1\n",
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"acute 1\n",
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"adair 14\n",
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"adaptable 1\n",
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"adapted 4\n",
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"add 8\n",
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"added 4\n",
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"adding 1\n",
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"addition 5\n",
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"additional 1\n",
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"addleton 1\n",
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"address 17\n",
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"addressed 6\n",
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"addressing 1\n",
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"adelaide 4\n",
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"adequate 2\n",
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"adhere 1\n",
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"adhesive 1\n",
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"adjectives 1\n",
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"adjoining 1\n",
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"adjusted 1\n",
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"adler 1\n",
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"administration 1\n",
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"admirable 10\n",
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"admirably 1\n",
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"admiralty 1\n",
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"admiration 8\n",
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"admire 1\n",
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"admirer 2\n",
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"admirers 1\n",
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"admit 21\n",
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"admits 1\n",
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"admitted 4\n",
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"adopt 1\n",
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"adorned 2\n",
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"advance 3\n",
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"advanced 11\n",
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"advances 1\n",
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"advancing 1\n",
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"advantage 6\n",
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"advantages 1\n",
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"adventure 30\n",
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"adventures 6\n",
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"adventurous 1\n",
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"adversary 1\n",
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"advertise 2\n",
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"advertisement 6\n",
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"advertisements 2\n",
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"advertising 1\n",
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"advice 11\n",
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"advise 3\n",
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"advised 3\n",
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"adviser 1\n",
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"advises 1\n",
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"affair 31\n",
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"affairs 12\n",
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"affect 1\n",
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"affected 1\n",
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"affection 3\n",
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"affectionate 1\n",
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"affectionately 1\n",
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"affections 1\n",
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"affluent 1\n",
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"afford 8\n",
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"afforded 1\n",
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"afghan 1\n",
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"afoot 1\n",
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"afraid 11\n",
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"africa 10\n",
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"african 6\n",
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"after 127\n",
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"afternoon 15\n",
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"afterwards 21\n",
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"again 134\n",
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"against 77\n",
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"agatha 2\n",
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"age 16\n",
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"aged 8\n",
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"agencies 1\n",
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"agency 2\n",
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"agent 10\n",
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"agents 8\n",
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"ages 2\n",
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"aggressive 2\n",
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"aggressively 1\n",
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"agile 1\n",
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"agitated 7\n",
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"agitation 8\n",
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"ago 43\n",
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"agonies 1\n",
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"agony 6\n",
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"agree 5\n",
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"agreeable 1\n",
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"agreed 4\n",
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"agreement 4\n",
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"ah 34\n",
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"aha 1\n",
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"ahead 5\n",
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"aid 11\n",
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"aided 5\n",
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"aim 1\n",
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"aimed 1\n",
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"aimlessly 1\n",
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"air 29\n",
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"airgun 1\n",
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"ajar 2\n",
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"akimbo 2\n",
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"alarm 17\n",
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"alarmed 4\n",
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"alas 2\n",
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"albert 2\n",
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"aldridge 1\n",
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"alec 3\n",
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"alert 16\n",
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"alertness 1\n",
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"alexandria 1\n",
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"alexandrian 1\n",
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"alexis 3\n",
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"algar 2\n",
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"alias 1\n",
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"alibi 2\n",
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"alight 1\n",
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"alighted 3\n",
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"alighting 2\n",
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"alike 1\n",
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"alive 9\n",
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"all 462\n",
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"allardyce 2\n",
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"alley 1\n",
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"allow 8\n",
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"allowance 2\n",
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"allowed 10\n",
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"allowing 2\n",
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"allows 2\n",
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"allude 4\n",
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"alluded 2\n",
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"allusion 3\n",
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"allusions 2\n",
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"ally 1\n",
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"almost 16\n",
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"alone 27\n",
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"along 35\n",
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"aloud 8\n",
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"alpha 7\n",
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"alphabet 2\n",
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"already 61\n",
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"also 81\n",
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"alter 1\n",
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"alteration 1\n",
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"alterations 1\n",
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"altered 1\n",
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"alternately 1\n",
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"alternative 16\n",
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"alternatives 2\n",
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"although 11\n",
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"altogether 8\n",
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"always 64\n",
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"am 239\n",
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"amateur 5\n",
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"amaze 1\n",
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"amazed 3\n",
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"amazement 28\n",
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"amazing 6\n",
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"amber 1\n",
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"ambitions 2\n",
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"ambuscade 2\n",
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"amends 2\n",
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"america 6\n",
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"american 11\n",
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"amiability 1\n",
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"amiable 1\n",
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"amid 11\n",
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"amidst 1\n",
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"amiss 3\n",
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"among 50\n",
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"amongst 2\n",
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"amount 5\n",
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"amounted 4\n",
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"amounts 1\n",
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"amoy 1\n",
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"ample 1\n",
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"amply 1\n",
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"amuse 1\n",
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"amused 6\n",
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"amusement 4\n",
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"amusing 2\n",
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"an 519\n",
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"anaemic 2\n",
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"analysis 5\n",
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"analyze 1\n",
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"anarchist 1\n",
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"anatomical 1\n",
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"ancestors 1\n",
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"ancient 6\n",
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"and 3819\n",
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"andaman 1\n",
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"anderson 1\n",
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"anecdote 2\n",
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"anerley 1\n",
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"angel 2\n",
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"anger 10\n",
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"angle 4\n",
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"anglia 1\n",
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"anglo 1\n",
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"angry 7\n",
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"anguish 1\n",
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"angular 1\n",
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"animal 2\n",
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"aniseed 2\n",
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"ankle 3\n",
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"ankles 2\n",
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"anna 4\n",
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"annals 1\n",
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"announce 1\n",
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"announced 2\n",
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"announcement 1\n",
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"annoyance 4\n",
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"annoyed 3\n",
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"annoys 1\n",
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"anoints 1\n",
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"another 61\n",
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"answer 43\n",
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"answerable 1\n",
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"answered 27\n",
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"answering 3\n",
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"answers 1\n",
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"antagonist 1\n",
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"ante 1\n",
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"boat 9\n",
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"bob 3\n",
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"boer 3\n",
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"boers 1\n",
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"bones 1\n",
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"bonnet 2\n",
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"bonniest 1\n",
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"bonny 2\n",
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"bony 1\n",
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"book 31\n",
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"booking 1\n",
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"both 62\n",
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"bottom 12\n",
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"bound 13\n",
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"boxes 1\n",
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"boy 38\n",
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"bracket 4\n",
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"bread 1\n",
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"breadth 1\n",
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"breakfasted 3\n",
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"breaks 1\n",
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"breathless 2\n",
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"bred 3\n",
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"brewing 1\n",
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"brick 3\n",
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"brickwork 1\n",
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"bridegroom 2\n",
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"bridge 5\n",
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"bright 10\n",
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"brightened 1\n",
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"brighter 4\n",
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"brightest 3\n",
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"brightly 1\n",
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"brightness 1\n",
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"brighton 1\n",
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"brilliant 7\n",
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"brilliantly 1\n",
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"brim 1\n",
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"brimmed 1\n",
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"brims 1\n",
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"brindled 2\n",
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"bring 40\n",
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"bringing 5\n",
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"brings 1\n",
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"brink 2\n",
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"brisk 2\n",
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"briskest 1\n",
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"briskly 1\n",
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"bristled 4\n",
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"britain 5\n",
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"british 11\n",
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"briton 1\n",
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"brixton 10\n",
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"broad 28\n",
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"broadened 2\n",
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"broader 1\n",
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"broke 24\n",
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"broken 24\n",
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"broker 4\n",
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"brotherhood 2\n",
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"brought 54\n",
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"brow 13\n",
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"browbeat 1\n",
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"brown 12\n",
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"browner 12\n",
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"browners 1\n",
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"bruise 1\n",
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"brushed 2\n",
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"brutality 1\n",
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"bucket 1\n",
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"buckets 1\n",
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"budding 1\n",
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"build 3\n",
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"buildings 1\n",
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"built 3\n",
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"bulbous 1\n",
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"bulky 4\n",
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"bullet 19\n",
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"bullying 1\n",
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"bundle 4\n",
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"bundles 2\n",
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"bungler 1\n",
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"burden 2\n",
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"bureau 12\n",
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"burgle 2\n",
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"burgled 1\n",
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"buried 9\n",
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"burn 2\n",
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"burned 10\n",
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"burning 5\n",
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"burst 17\n",
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"burying 1\n",
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"bus 1\n",
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"bush 1\n",
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"bushes 14\n",
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"busied 2\n",
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"busiest 1\n",
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"business 58\n",
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"bust 32\n",
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"bustled 2\n",
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"bustling 1\n",
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"busts 23\n",
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"busy 13\n",
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"busybody 2\n",
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"but 884\n",
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"butcher 4\n",
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"butler 12\n",
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"butt 4\n",
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"butter 2\n",
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"buttoned 3\n",
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"buttoning 1\n",
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"buttons 3\n",
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"buy 6\n",
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"buzzing 1\n",
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"by 484\n",
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"bye 6\n",
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"byways 1\n",
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"cab 27\n",
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"cabby 2\n",
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"cabin 20\n",
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"cabinet 6\n",
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"cable 4\n",
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"cabled 1\n",
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"cablegram 1\n",
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"cabman 1\n",
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"cabul 1\n",
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"cadaverous 1\n",
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"cairns 8\n",
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"calculate 1\n",
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"calculated 2\n",
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"calculation 1\n",
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"calculations 1\n",
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"call 33\n",
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"called 38\n",
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"calling 2\n",
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"calls 2\n",
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"calm 5\n",
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"calmly 1\n",
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"cam 1\n",
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"cambridge 11\n",
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"camden 1\n",
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"came 202\n",
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"camera 1\n",
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"camp 1\n",
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"campaign 4\n",
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"campden 1\n",
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"camped 1\n",
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"can 327\n",
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"canadian 2\n",
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"canal 1\n",
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"canary 2\n",
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"candelabra 1\n",
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"candidate 2\n",
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"candle 12\n",
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"candles 2\n",
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"cane 1\n",
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"canine 1\n",
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"cannot 57\n",
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"cantering 1\n",
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"canters 1\n",
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"cap 9\n",
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"capable 9\n",
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"capacities 1\n",
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"capacity 2\n",
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"cape 1\n",
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"capital 7\n",
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"caprice 1\n",
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"capricious 2\n",
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"captain 31\n",
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"captive 3\n",
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"capture 1\n",
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"captured 2\n",
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"carafe 1\n",
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"caravan 1\n",
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"carbolic 1\n",
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"carbuncle 5\n",
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"carcass 1\n",
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"card 22\n",
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"cardboard 4\n",
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"cardinal 2\n",
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"cards 9\n",
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"care 22\n",
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"cared 4\n",
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"career 18\n",
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"careful 5\n",
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"carefully 21\n",
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"careless 2\n",
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"carelessly 2\n",
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"carey 48\n",
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"cargo 1\n",
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"caricature 1\n",
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"carlton 1\n",
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"carotid 1\n",
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"carpet 24\n",
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"carriage 20\n",
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"carriages 3\n",
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"carried 25\n",
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"carries 1\n",
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"carruthers 31\n",
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"carry 10\n",
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"carrying 8\n",
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"carstairs 1\n",
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"carston 2\n",
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"cart 12\n",
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"cartilage 1\n",
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"cartridge 2\n",
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"cartridges 1\n",
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"carve 1\n",
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"carved 1\n",
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"carving 1\n",
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"case 184\n",
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"cases 23\n",
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"cashed 1\n",
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"casket 1\n",
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"cast 7\n",
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"caste 1\n",
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"castle 1\n",
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"casts 5\n",
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"casual 1\n",
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"cat 4\n",
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"cataract 1\n",
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"catastrophe 5\n",
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"catch 7\n",
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"catching 1\n",
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"category 1\n",
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"cathcart 1\n",
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"catherine 2\n",
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"catholic 1\n",
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"cattle 3\n",
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"catullus 1\n",
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"caught 21\n",
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"caunter 1\n",
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"cause 16\n",
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"caused 20\n",
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"causes 2\n",
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"caution 2\n",
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"cautious 2\n",
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"cautiously 1\n",
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"cavendish 2\n",
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"cavity 1\n",
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"ceased 6\n",
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"ceaseless 1\n",
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"ceiling 3\n",
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"celebrities 1\n",
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"cell 4\n",
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"cells 1\n",
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"central 11\n",
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"centre 21\n",
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"centuries 1\n",
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"century 5\n",
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"ceremony 1\n",
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"certain 42\n",
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"certainly 98\n",
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"certainty 3\n",
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"cetera 1\n",
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"chaff 1\n",
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"chaffed 1\n",
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"chagrined 1\n",
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"chain 16\n",
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"chair 79\n",
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"chairs 2\n",
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"chalk 6\n",
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"challenged 2\n",
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"chamber 5\n",
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"chanced 3\n",
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"change 15\n",
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"changed 5\n",
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"changes 1\n",
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"changing 2\n",
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"channel 1\n",
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"channels 1\n",
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"chap 4\n",
|
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"chapter 1\n",
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"character 19\n",
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"characteristic 3\n",
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"characteristics 4\n",
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"characters 4\n",
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"charasiab 1\n",
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"charcoal 2\n",
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"charge 18\n",
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"charged 1\n",
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"charing 7\n",
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"charles 7\n",
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"charlington 14\n",
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"charm 3\n",
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"charmed 1\n",
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"charming 6\n",
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"charred 3\n",
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"chart 2\n",
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"charters 1\n",
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"charts 2\n",
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"chased 1\n",
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"chasing 1\n",
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"chasm 3\n",
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"chastened 1\n",
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"chat 1\n",
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"chatham 6\n",
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"chatted 1\n",
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"chattering 2\n",
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"chatting 2\n",
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"cheap 1\n",
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"cheating 1\n",
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"check 12\n",
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"checks 4\n",
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"cheek 2\n",
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"cheeks 13\n",
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"cheer 1\n",
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"cheerful 1\n",
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"cheerfully 1\n",
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"cheerily 1\n",
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"cheery 3\n",
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"cheese 1\n",
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"chemical 4\n",
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"chemicals 1\n",
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"cherished 1\n",
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"chess 1\n",
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"chest 8\n",
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"chested 2\n",
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"chesterfield 4\n",
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"chesterton 1\n",
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"chestnuts 1\n",
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"chevron 1\n",
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"chicago 3\n",
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"chief 4\n",
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"child 10\n",
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"childish 5\n",
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"children 4\n",
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"chill 1\n",
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"chilled 1\n",
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"chiltern 2\n",
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"chimed 2\n",
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"chimes 2\n",
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"chimneys 1\n",
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"chin 9\n",
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"china 1\n",
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"chins 1\n",
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"chip 1\n",
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"chipped 1\n",
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"chips 1\n",
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"chiselhurst 2\n",
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"chiswick 4\n",
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"chivalrous 1\n",
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"choice 4\n",
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"choke 1\n",
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"choked 1\n",
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"choking 2\n",
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"choose 5\n",
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"chooses 1\n",
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"choosing 3\n",
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"chose 3\n",
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"chosen 3\n",
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"christ 1\n",
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"christmas 9\n",
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"chronicle 3\n",
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"chronicled 1\n",
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"chronicler 1\n",
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"chronicles 1\n",
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"chubb 1\n",
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"chuckle 4\n",
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"chuckled 9\n",
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"chum 1\n",
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"church 6\n",
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"churches 1\n",
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"cigar 12\n",
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"cigarette 20\n",
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"cigarettes 8\n",
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"cigars 1\n",
|
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"cipher 1\n",
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"ciphers 1\n",
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"circle 6\n",
|
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"circles 1\n",
|
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"circulated 1\n",
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"circulation 1\n",
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"circumlocution 1\n",
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"circumstance 1\n",
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"circumstances 15\n",
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"circus 1\n",
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"citizens 2\n",
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"city 12\n",
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"civil 2\n",
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"civilization 1\n",
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"civilly 1\n",
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"clad 5\n",
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"claim 6\n",
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"claimed 1\n",
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"claiming 1\n",
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"clang 2\n",
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"clapped 5\n",
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"clapping 1\n",
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"claret 2\n",
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"clasp 3\n",
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"clasped 3\n",
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"clasping 1\n",
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"claspings 1\n",
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"class 8\n",
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"classes 1\n",
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"classic 2\n",
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"classical 1\n",
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"clatter 4\n",
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"clattered 1\n",
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"claw 2\n",
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"clawed 2\n",
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"clawing 2\n",
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"clay 7\n",
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"clean 13\n",
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"cleaned 3\n",
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"cleaners 1\n",
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"clear 74\n",
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"cleared 13\n",
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"clearer 2\n",
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"clearing 9\n",
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"clearly 27\n",
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"clearness 1\n",
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"clenched 9\n",
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"clergyman 4\n",
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"clerical 1\n",
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"clerk 1\n",
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"clever 4\n",
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"cleverly 1\n",
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"cleverness 1\n",
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"click 7\n",
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"clicked 1\n",
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"client 32\n",
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"clients 4\n",
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"cliff 4\n",
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"climb 3\n",
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"climbed 3\n",
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"climbing 1\n",
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"climbs 1\n",
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"cling 1\n",
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"clinking 2\n",
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"clips 2\n",
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"clock 35\n",
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"close 31\n",
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"closed 23\n",
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"closely 2\n",
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"closer 3\n",
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"closest 1\n",
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"clumsily 1\n",
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"cock 9\n",
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"cocked 1\n",
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"cocksure 1\n",
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"cocoa 1\n",
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"cocoanut 4\n",
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"code 1\n",
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"coffee 5\n",
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"coherent 1\n",
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"coil 1\n",
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"coins 1\n",
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"cold 33\n",
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"colder 1\n",
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"coldly 3\n",
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"collecting 1\n",
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"collector 2\n",
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"colonel 36\n",
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"colonial 1\n",
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"colonies 1\n",
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"colonna 1\n",
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"colossal 1\n",
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"colour 16\n",
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"coloured 6\n",
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"colouring 2\n",
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"colourless 1\n",
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"column 2\n",
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"columns 2\n",
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"combination 6\n",
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"come 217\n",
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"comely 2\n",
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"comes 15\n",
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"comfort 1\n",
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"comfortable 1\n",
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"coming 36\n",
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"command 7\n",
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"commanded 5\n",
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"commands 1\n",
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"commence 2\n",
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"commenced 1\n",
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"commend 5\n",
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"commented 1\n",
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"commercial 2\n",
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"commissionaire 4\n",
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"commit 3\n",
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"common 12\n",
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"communicate 4\n",
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"communicated 5\n",
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"communications 1\n",
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"communicative 1\n",
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"community 3\n",
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"commuting 1\n",
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"companions 1\n",
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"comparatively 2\n",
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"compare 3\n",
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"compared 1\n",
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"comparison 2\n",
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"compartments 1\n",
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"compel 1\n",
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"compelled 9\n",
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"compete 2\n",
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"competent 1\n",
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"complete 31\n",
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"completed 4\n",
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"completely 6\n",
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"complexion 3\n",
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"complicated 3\n",
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"complication 1\n",
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"complications 1\n",
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"compliment 1\n",
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"composed 2\n",
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"composition 1\n",
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"composure 4\n",
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"compressed 2\n",
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"compromise 3\n",
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"compunction 1\n",
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"comrade 4\n",
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"comrades 1\n",
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"comradeship 1\n",
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"conan 5\n",
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"concave 1\n",
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"concerns 3\n",
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"conciliatory 1\n",
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"concise 2\n",
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"concluded 3\n",
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"conclusive 3\n",
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"conclusively 1\n",
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"concocted 1\n",
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"condemned 3\n",
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"conditions 2\n",
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"condoned 1\n",
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"conduct 12\n",
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"conducted 5\n",
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"confide 3\n",
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"confidence 24\n",
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"connect 4\n",
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"connected 10\n",
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"continuous 2\n",
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"contracted 3\n",
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"contrary 4\n",
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"contrived 1\n",
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"conversing 1\n",
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"conveying 2\n",
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"convincing 3\n",
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"convolution 1\n",
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"convulsively 1\n",
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"cook 2\n",
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"cool 4\n",
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"cooler 1\n",
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"coolly 1\n",
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"coolness 1\n",
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"cooperation 2\n",
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"copied 3\n",
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"copies 1\n",
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"coping 1\n",
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"copious 1\n",
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"copper 2\n",
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"coptic 1\n",
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"copy 14\n",
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"coram 9\n",
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"cord 10\n",
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"cords 1\n",
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"cork 5\n",
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"corkscrew 1\n",
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"corn 1\n",
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"cornelius 5\n",
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"corner 28\n",
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"corners 6\n",
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"cornwall 2\n",
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"corporal 1\n",
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"corps 1\n",
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"correctly 1\n",
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"corresponding 5\n",
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"corridor 11\n",
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"corridors 1\n",
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"corroborate 1\n",
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"corroborated 1\n",
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"corroborates 1\n",
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"corroboration 2\n",
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"corroborative 1\n",
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"cosmopolitan 3\n",
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"cost 3\n",
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"costa 2\n",
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"costs 1\n",
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"costume 1\n",
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"cosy 1\n",
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"cottage 5\n",
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"cottages 2\n",
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"couch 4\n",
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"couched 1\n",
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"coughing 1\n",
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"could 430\n",
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"couldn 18\n",
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"council 1\n",
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"counsel 3\n",
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"countenance 1\n",
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"counter 4\n",
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"counterfeited 1\n",
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"counterfoil 2\n",
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"counterfoils 2\n",
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"countess 10\n",
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"counties 1\n",
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"countries 1\n",
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"country 49\n",
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"countryman 2\n",
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"countryside 4\n",
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"county 10\n",
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"couple 14\n",
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"courage 6\n",
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"courageous 1\n",
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"course 76\n",
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"courses 1\n",
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"court 16\n",
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"courteous 1\n",
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"courtesy 2\n",
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"courting 1\n",
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"courtly 1\n",
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"courts 1\n",
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"courtyard 3\n",
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"cousin 3\n",
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"covent 3\n",
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"coventry 1\n",
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"cover 15\n",
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"covered 19\n",
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"covering 2\n",
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"coverings 1\n",
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"cow 3\n",
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"coward 1\n",
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"cowardly 2\n",
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"cowered 1\n",
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"cowering 2\n",
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"cows 4\n",
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"crabbed 1\n",
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"crack 3\n",
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"cracked 3\n",
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"crackle 1\n",
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"crackled 2\n",
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"crackling 3\n",
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"craft 3\n",
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"crafty 1\n",
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"cram 1\n",
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"crammed 1\n",
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"craned 1\n",
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"crash 1\n",
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"cravats 3\n",
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"craved 1\n",
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"crawl 1\n",
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"crawled 3\n",
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"crawling 1\n",
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"crazed 1\n",
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"crazy 1\n",
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"creak 3\n",
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"creaked 1\n",
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"creaking 1\n",
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"cream 3\n",
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"creation 1\n",
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"creature 13\n",
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"creatures 2\n",
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"credit 4\n",
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"credited 1\n",
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"creditor 2\n",
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"creditors 4\n",
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"creeping 2\n",
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"creole 1\n",
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"crept 7\n",
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"crew 4\n",
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"cricket 2\n",
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"cried 80\n",
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"cries 4\n",
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"crime 83\n",
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"crimean 2\n",
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"crimes 4\n",
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"criminal 41\n",
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"criminals 4\n",
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"crimson 5\n",
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"cringe 1\n",
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"cringing 1\n",
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"crisis 4\n",
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"crisp 3\n",
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"crisply 1\n",
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"critical 1\n",
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"criticism 1\n",
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"croaking 1\n",
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"crocker 6\n",
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"crocodile 1\n",
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"crocuses 1\n",
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"crook 1\n",
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"crooks 1\n",
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"crooksbury 2\n",
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"crop 10\n",
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"crosby 1\n",
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"cross 25\n",
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"crossed 7\n",
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"crossing 2\n",
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"crosspiece 1\n",
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"crouched 4\n",
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"crouching 5\n",
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"crowd 4\n",
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"crowded 1\n",
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"crown 1\n",
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"crowning 1\n",
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"croydon 10\n",
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"cruel 2\n",
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"cruelty 1\n",
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"cruet 1\n",
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"crumbling 2\n",
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"crumbs 1\n",
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"crumpled 4\n",
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"crush 1\n",
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"crushed 3\n",
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"crushing 3\n",
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"cry 15\n",
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"crying 1\n",
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"crystal 1\n",
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"crystallized 1\n",
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"crystals 1\n",
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"cubic 1\n",
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"cubitt 24\n",
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"cuddled 1\n",
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"cudgel 1\n",
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"cue 2\n",
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"cuff 1\n",
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"cuffs 1\n",
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"culminate 1\n",
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"culminated 1\n",
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"culpable 2\n",
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"culprit 2\n",
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"cultivated 1\n",
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"cumulative 1\n",
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"cunning 14\n",
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"cunningly 1\n",
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"cup 1\n",
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"cupboard 6\n",
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"curable 1\n",
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"cure 1\n",
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"curiosity 6\n",
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"curled 3\n",
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"curling 1\n",
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"cursed 2\n",
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"curt 1\n",
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"curtain 11\n",
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"curtained 2\n",
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"curtly 1\n",
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"curving 3\n",
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"cushing 28\n",
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"cushion 2\n",
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"custody 2\n",
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"custom 3\n",
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"customer 2\n",
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"cut 34\n",
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"cutlets 1\n",
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"cuts 1\n",
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"cutter 1\n",
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"cutting 3\n",
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"cuttings 1\n",
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"cycle 4\n",
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"cycling 1\n",
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"cyclist 13\n",
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"cylinder 1\n",
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"cynical 1\n",
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"cyril 10\n",
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"dabbled 1\n",
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"dacre 1\n",
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"dagger 2\n",
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"daily 6\n",
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"dainty 2\n",
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"rained 1\n",
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"than 147\n",
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"that 2754\n",
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"these 170\n",
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"thick 11\n",
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"through 125\n",
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"throws 1\n",
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"thus 3\n",
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"walsham 2\n",
|
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"wander 2\n",
|
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"wandering 2\n",
|
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"waning 2\n",
|
|
"want 47\n",
|
|
"wanted 22\n",
|
|
"wanting 3\n",
|
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"wanton 1\n",
|
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"wants 2\n",
|
|
"war 13\n",
|
|
"ward 1\n",
|
|
"wardrobe 1\n",
|
|
"wares 1\n",
|
|
"warm 9\n",
|
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"warmed 1\n",
|
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"warmly 2\n",
|
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"warmth 2\n",
|
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"warn 6\n",
|
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"warned 3\n",
|
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"warning 4\n",
|
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"warp 2\n",
|
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"warrant 7\n",
|
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"wary 1\n",
|
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"was 2406\n",
|
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"washed 2\n",
|
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"wasn 8\n",
|
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"waste 7\n",
|
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"wasted 5\n",
|
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"wasting 1\n",
|
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"watch 17\n",
|
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"watched 16\n",
|
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"watchers 1\n",
|
|
"watching 4\n",
|
|
"water 19\n",
|
|
"waterbeach 1\n",
|
|
"watercourse 2\n",
|
|
"waterford 1\n",
|
|
"watering 1\n",
|
|
"waterloo 2\n",
|
|
"waterpipe 1\n",
|
|
"waterproof 1\n",
|
|
"waters 1\n",
|
|
"watson 249\n",
|
|
"wave 1\n",
|
|
"waved 2\n",
|
|
"waving 2\n",
|
|
"wax 8\n",
|
|
"waxed 1\n",
|
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"waxen 1\n",
|
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"way 158\n",
|
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"ways 15\n",
|
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"wayside 2\n",
|
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"wayward 1\n",
|
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"we 854\n",
|
|
"weak 7\n",
|
|
"weakening 1\n",
|
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"weakling 1\n",
|
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"weakness 3\n",
|
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"weald 2\n",
|
|
"wealth 8\n",
|
|
"wealthiest 1\n",
|
|
"wealthy 5\n",
|
|
"weaned 1\n",
|
|
"weapon 14\n",
|
|
"weapons 3\n",
|
|
"wear 3\n",
|
|
"wearer 2\n",
|
|
"wearied 1\n",
|
|
"wearily 2\n",
|
|
"wearing 4\n",
|
|
"weary 6\n",
|
|
"weather 10\n",
|
|
"weaving 1\n",
|
|
"web 2\n",
|
|
"wedded 1\n",
|
|
"wedding 5\n",
|
|
"wedged 1\n",
|
|
"wednesday 3\n",
|
|
"week 35\n",
|
|
"weeklies 1\n",
|
|
"weeks 13\n",
|
|
"weigh 1\n",
|
|
"weight 10\n",
|
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"weird 2\n",
|
|
"welcome 7\n",
|
|
"well 387\n",
|
|
"wells 2\n",
|
|
"welted 1\n",
|
|
"went 82\n",
|
|
"were 534\n",
|
|
"west 5\n",
|
|
"western 1\n",
|
|
"westminster 7\n",
|
|
"westward 1\n",
|
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"wet 6\n",
|
|
"whale 2\n",
|
|
"whaler 4\n",
|
|
"what 476\n",
|
|
"whatever 18\n",
|
|
"wheel 4\n",
|
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"wheeled 2\n",
|
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"wheeler 6\n",
|
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"wheels 5\n",
|
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"when 415\n",
|
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"whence 4\n",
|
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"where 157\n",
|
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"whereabouts 1\n",
|
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"whereas 1\n",
|
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"wherever 4\n",
|
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"whether 28\n",
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"which 938\n",
|
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"whiff 2\n",
|
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"while 49\n",
|
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"whilst 2\n",
|
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"whim 1\n",
|
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"whimper 1\n",
|
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"whimsical 3\n",
|
|
"whine 1\n",
|
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"whined 3\n",
|
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"whip 2\n",
|
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"whipped 2\n",
|
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"whirl 1\n",
|
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"whirling 1\n",
|
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"whisk 1\n",
|
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"whisked 1\n",
|
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"whiskers 6\n",
|
|
"whisky 4\n",
|
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"whisper 3\n",
|
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"whispered 10\n",
|
|
"whispers 1\n",
|
|
"whist 2\n",
|
|
"whistle 5\n",
|
|
"whistled 3\n",
|
|
"white 50\n",
|
|
"whitehall 4\n",
|
|
"whitened 1\n",
|
|
"whiteness 1\n",
|
|
"whiter 1\n",
|
|
"whitewashed 2\n",
|
|
"whitish 1\n",
|
|
"whiz 1\n",
|
|
"whizzing 1\n",
|
|
"who 361\n",
|
|
"whoever 6\n",
|
|
"whole 68\n",
|
|
"wholesale 2\n",
|
|
"wholly 1\n",
|
|
"whom 67\n",
|
|
"whose 45\n",
|
|
"why 100\n",
|
|
"wicked 4\n",
|
|
"wickedness 1\n",
|
|
"wide 6\n",
|
|
"widens 1\n",
|
|
"wider 2\n",
|
|
"widespread 4\n",
|
|
"widow 4\n",
|
|
"widower 1\n",
|
|
"wife 62\n",
|
|
"wigmore 1\n",
|
|
"wild 18\n",
|
|
"wilder 18\n",
|
|
"wilderness 1\n",
|
|
"wildly 4\n",
|
|
"wildness 1\n",
|
|
"wilful 2\n",
|
|
"will 419\n",
|
|
"willful 1\n",
|
|
"williamson 11\n",
|
|
"willing 2\n",
|
|
"willingly 2\n",
|
|
"willoughby 9\n",
|
|
"wilson 4\n",
|
|
"wimpole 1\n",
|
|
"winced 4\n",
|
|
"wind 11\n",
|
|
"windfall 1\n",
|
|
"windigate 3\n",
|
|
"windings 1\n",
|
|
"window 126\n",
|
|
"windowed 1\n",
|
|
"windows 22\n",
|
|
"winds 2\n",
|
|
"wine 6\n",
|
|
"wineglasses 2\n",
|
|
"wing 5\n",
|
|
"winner 1\n",
|
|
"winning 2\n",
|
|
"winnings 1\n",
|
|
"winter 9\n",
|
|
"wiped 1\n",
|
|
"wire 11\n",
|
|
"wired 5\n",
|
|
"wires 1\n",
|
|
"wiring 2\n",
|
|
"wiry 1\n",
|
|
"wise 3\n",
|
|
"wisely 1\n",
|
|
"wiser 8\n",
|
|
"wish 32\n",
|
|
"wished 15\n",
|
|
"wishes 3\n",
|
|
"wishing 3\n",
|
|
"wistful 1\n",
|
|
"wit 4\n",
|
|
"with 1146\n",
|
|
"withdraw 1\n",
|
|
"withdrawn 2\n",
|
|
"withdrew 4\n",
|
|
"wither 1\n",
|
|
"withered 1\n",
|
|
"withheld 1\n",
|
|
"within 58\n",
|
|
"without 84\n",
|
|
"witness 10\n",
|
|
"witnessed 3\n",
|
|
"witnesses 1\n",
|
|
"wits 3\n",
|
|
"witted 2\n",
|
|
"wizard 3\n",
|
|
"wizened 2\n",
|
|
"woke 1\n",
|
|
"wolf 1\n",
|
|
"woman 89\n",
|
|
"womanly 2\n",
|
|
"women 11\n",
|
|
"won 19\n",
|
|
"wonder 14\n",
|
|
"wondered 1\n",
|
|
"wonderful 9\n",
|
|
"wonderfully 3\n",
|
|
"wont 1\n",
|
|
"wood 22\n",
|
|
"woodcock 1\n",
|
|
"wooden 11\n",
|
|
"woodley 26\n",
|
|
"woodman 8\n",
|
|
"woods 6\n",
|
|
"woodwork 4\n",
|
|
"word 75\n",
|
|
"words 41\n",
|
|
"wore 6\n",
|
|
"work 63\n",
|
|
"worked 9\n",
|
|
"workers 1\n",
|
|
"working 12\n",
|
|
"workingman 1\n",
|
|
"workman 5\n",
|
|
"workmanlike 2\n",
|
|
"workpeople 1\n",
|
|
"works 9\n",
|
|
"world 37\n",
|
|
"worlds 2\n",
|
|
"worn 11\n",
|
|
"worried 2\n",
|
|
"worrying 1\n",
|
|
"worse 12\n",
|
|
"worship 1\n",
|
|
"worst 11\n",
|
|
"worth 14\n",
|
|
"worthy 6\n",
|
|
"would 528\n",
|
|
"wouldn 5\n",
|
|
"wound 9\n",
|
|
"wounded 9\n",
|
|
"wounds 1\n",
|
|
"woven 1\n",
|
|
"wrapped 2\n",
|
|
"wrapper 1\n",
|
|
"wreck 2\n",
|
|
"wreckage 1\n",
|
|
"wrenched 1\n",
|
|
"wrestling 1\n",
|
|
"wretched 8\n",
|
|
"wriggle 1\n",
|
|
"wright 3\n",
|
|
"wring 1\n",
|
|
"wrings 1\n",
|
|
"wrinkled 2\n",
|
|
"wrist 4\n",
|
|
"wrists 4\n",
|
|
"write 15\n",
|
|
"writer 2\n",
|
|
"writhing 3\n",
|
|
"writing 25\n",
|
|
"writings 2\n",
|
|
"written 18\n",
|
|
"wrong 22\n",
|
|
"wrote 28\n",
|
|
"wrought 3\n",
|
|
"wrung 3\n",
|
|
"xxx 2\n",
|
|
"yacht 6\n",
|
|
"yard 27\n",
|
|
"yards 20\n",
|
|
"yarn 2\n",
|
|
"yarned 2\n",
|
|
"yawns 1\n",
|
|
"year 39\n",
|
|
"yearned 1\n",
|
|
"years 69\n",
|
|
"yell 7\n",
|
|
"yelled 2\n",
|
|
"yellow 12\n",
|
|
"yeomanry 1\n",
|
|
"yes 179\n",
|
|
"yesterday 36\n",
|
|
"yet 122\n",
|
|
"yew 2\n",
|
|
"yielded 5\n",
|
|
"yonder 8\n",
|
|
"york 2\n",
|
|
"you 2137\n",
|
|
"young 132\n",
|
|
"younger 7\n",
|
|
"youngest 2\n",
|
|
"youngster 2\n",
|
|
"your 522\n",
|
|
"yours 20\n",
|
|
"yourself 37\n",
|
|
"yourselves 2\n",
|
|
"youth 8\n",
|
|
"youthful 1\n",
|
|
"youths 1\n",
|
|
"yoxley 7\n",
|
|
"zealous 1\n",
|
|
"zenith 1\n",
|
|
"zest 1\n",
|
|
"zigzag 1\n",
|
|
"zoo 1\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 10
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "293e4b38",
|
|
"metadata": {},
|
|
"source": [
|
|
"### > Dokumenatation <\n",
|
|
"Es werden über alle Dokumente hinweg alle einzigartigen Features, in diesem Fall die unterschiedlichen Wörter gezählt. Dies gibt uns die `bag_of_words`.\n",
|
|
"\n",
|
|
"Der Aufbau der Matrix ist wie folgt\n",
|
|
"$$\n",
|
|
"\\begin{array}{cc}\n",
|
|
" % Obere Zeile: Leerecke links, Pfeil für Axis 1 rechts\n",
|
|
" & \\xrightarrow{\\hspace{1cm} \\text{axis 1 (Wörter)} \\hspace{1cm}} \\\\\n",
|
|
" % Untere Zeile: Pfeil für Axis 0 links, Matrix rechts\n",
|
|
" \\begin{array}{c} \\text{axis 0} \\\\ \\text{(Docs)} \\\\ \\downarrow \\end{array} &\n",
|
|
" \\begin{array}{|c|cccc|}\n",
|
|
" \\hline\n",
|
|
" & \\text{Wort}_1 & \\text{Wort}_2 & \\text{Wort}_3 & \\dots \\\\\n",
|
|
" \\hline\n",
|
|
" \\text{Dok}_1 & n_{1,1} & n_{1,2} & n_{1,3} & \\dots \\\\\n",
|
|
" \\text{Dok}_2 & n_{2,1} & n_{2,2} & n_{2,3} & \\dots \\\\\n",
|
|
" \\vdots & \\vdots & \\vdots & \\vdots & \\ddots \\\\\n",
|
|
" \\hline\n",
|
|
" \\end{array}\n",
|
|
"\\end{array}\n",
|
|
"$$\n",
|
|
"#### Gesamtzahl der (einzigartigen) Wörter\n",
|
|
"Die Anzahl der Spalten gibt uns die Anzahl der Wörter über alle Texte hinweg, da jede Spalte ein Wort darstellt, d.h. ein Wort wird nicht doppelt gelistet werden.\n",
|
|
"So erhalten wir eine Anzahl von **8879** Wörtern, die in den Texten vorkommen.\n",
|
|
"#### Anzahl der Wörter pro Dokument\n",
|
|
"Durch das Aufsummieren der Werte in einer Zeile (axis=1), ermitelt man die Anzahl aller Wörter in einem Dokument. Die Ausgabe ist nach der Reihenfolge der Dokumentennamen im Array `filenames` sortiert.\n",
|
|
"\n",
|
|
"| | Anzahl der Wörter |\n",
|
|
"|-------------------|-------------------|\n",
|
|
"| Sherlock | 107416 |\n",
|
|
"| Sherlock_blanched | 7258 |\n",
|
|
"| Sherlock_black | 7775 |\n",
|
|
"| Sherlock_blue | 7497 |\n",
|
|
"| Sherlock_card | 8242 |\n",
|
|
"\n",
|
|
"#### Vorkommen jedes Wortes\n",
|
|
"Durch das Aufsummieren der Werte in einer Spalte (axis=0), ermitelt man die Häufigkeit eines Worts in allen Dokumenten. Die Ausgabe ist alphabetisch, da `bag_of_words` beim Erzeugen durch `vercotorizer` alphabetisch sortiert wird.\n",
|
|
"Da die Liste sehr lang ist, hier ein Auszug\n",
|
|
"\n",
|
|
"| Word | count |\n",
|
|
"|-----------|-------|\n",
|
|
"| all | 462 |\n",
|
|
"| allardyce | 2 |\n",
|
|
"| alley | 1 |\n",
|
|
"| allow | 8 |\n",
|
|
"| allowance | 2 |\n",
|
|
"| allowed | 10 |\n",
|
|
"| allowing | 2 |\n",
|
|
"| allows | 2 |\n",
|
|
"| allude | 4 |"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "582e1e0d-66c6-4c1c-9788-eee97d7c79f4",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 6.2.2 Which word is occuring the most?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "89936ba9-3403-4973-aa2c-029fcf463084",
|
|
"metadata": {},
|
|
"source": [
|
|
"This must be done in three steps. Reason is, that the vectorizer.vocabulary_ is organized as a dictonary with the value indicating the position of the word in the array\n",
|
|
"1. Find out the highest count of a word\n",
|
|
"2. Find out the position of this count\n",
|
|
"3. Find out the word at this position"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "a43e2e80",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-15T14:01:02.198425286Z",
|
|
"start_time": "2025-12-15T14:01:02.167549542Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"# find the highest count\n",
|
|
"count_max = np.max(word_counts)\n",
|
|
"\n",
|
|
"# find the index of the highest count\n",
|
|
"count_max_index = np.argmax(word_counts)\n",
|
|
"\n",
|
|
"# get the word with the highest count\n",
|
|
"count_max_word = feature_names[count_max_index]\n",
|
|
"\n",
|
|
"print(f\"Häufigstes Wort: '{count_max_word}'\")\n",
|
|
"print(f\"Anzahl: {count_max}\")\n",
|
|
"print(f\"Index: {count_max_index}\")\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Häufigstes Wort: 'the'\n",
|
|
"Anzahl: 7975\n",
|
|
"Index: 7921\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 11
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "75066e48",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### > Dokumentation <\n",
|
|
"Das Wort, was am häufigsten vorkommt, ist \"the\" an der Stelle 7921 in der Liste `word_counts` mit einer Häufigkeit von 7975, was nicht verwunderlich ist, da es sich um englische Texte handelt. Alles andere wäre bei der Textgröße ungewöhnlich und würde darauf deuten, dass es sich nicht um einen Text mit der für Englisch typischen Wortverteilung handelt."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5f8b5880-4542-47fa-9d16-94b458b967cf",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 6.3 Improving using stop word, ngrams and tf-idf\n",
|
|
"The feature space is vast with nearly 9000 dimensions. Hence we should try to reduce the number of dimensions by:\n",
|
|
"\n",
|
|
"1. use only words that have a mimimum occurence in all documents (minimal document frequency) min_df\n",
|
|
"2. remove stop words (like 'a', 'and', 'the') as they don't give valuable information for classification and/or \n",
|
|
"3. remove words that occur in many documents (maximum document frequency) max_df \n",
|
|
"\n",
|
|
"Experiment with the values of min_df and max_df and see how the size of the vocabulary is changing.\n",
|
|
"\n",
|
|
"Implement all three options and check for their separate outcome an their combinations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "b0de993a-7aad-4126-938d-86bc4bd26d8e",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-15T14:01:04.058166213Z",
|
|
"start_time": "2025-12-15T14:01:02.203172264Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"def improver(_stop_words=None, _min_df=1, _max_df=len(filenames)):\n",
|
|
" vectorizer = CountVectorizer(input=\"filename\", stop_words=_stop_words, min_df=_min_df, max_df=_max_df)\n",
|
|
" bag_of_words = vectorizer.fit_transform(filenames)\n",
|
|
" total_unique_words = len(vectorizer.get_feature_names_out())\n",
|
|
" print(f\"unique words: {total_unique_words:<4} min_df: {_min_df}, max_df: {_max_df}, stop_words: {_stop_words}\")\n",
|
|
" return bag_of_words, vectorizer\n",
|
|
"\n",
|
|
"print(\"only max_df\")\n",
|
|
"for i in range(1,6):\n",
|
|
" improver(_max_df=i)\n",
|
|
"\n",
|
|
"print(\"\\nonly max_df\")\n",
|
|
"for i in range(1,6):\n",
|
|
" improver(_min_df=i)\n",
|
|
"\n",
|
|
"print(\"\\nonly stop_words\")\n",
|
|
"improver(_stop_words=[\"the\"])\n",
|
|
"improver(_stop_words=[\"and\"])\n",
|
|
"improver(_stop_words=[\"a\"])\n",
|
|
"improver(_stop_words=[\"I\"])\n",
|
|
"improver(_stop_words=[\"i\"])\n",
|
|
"improver(_stop_words=[\"the\", \"and\", \"a\"])\n",
|
|
"improver(_stop_words=\"english\")\n",
|
|
"\n",
|
|
"print(\"\\ncombination\")\n",
|
|
"bag_of_words, vectorizer_combined = improver(_stop_words=\"english\", _min_df=2, _max_df=4)\n",
|
|
"\n",
|
|
"\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"only max_df\n",
|
|
"unique words: 5508 min_df: 1, max_df: 1, stop_words: None\n",
|
|
"unique words: 7349 min_df: 1, max_df: 2, stop_words: None\n",
|
|
"unique words: 8079 min_df: 1, max_df: 3, stop_words: None\n",
|
|
"unique words: 8455 min_df: 1, max_df: 4, stop_words: None\n",
|
|
"unique words: 8879 min_df: 1, max_df: 5, stop_words: None\n",
|
|
"\n",
|
|
"only max_df\n",
|
|
"unique words: 8879 min_df: 1, max_df: 5, stop_words: None\n",
|
|
"unique words: 3371 min_df: 2, max_df: 5, stop_words: None\n",
|
|
"unique words: 1530 min_df: 3, max_df: 5, stop_words: None\n",
|
|
"unique words: 800 min_df: 4, max_df: 5, stop_words: None\n",
|
|
"unique words: 424 min_df: 5, max_df: 5, stop_words: None\n",
|
|
"\n",
|
|
"only stop_words\n",
|
|
"unique words: 8878 min_df: 1, max_df: 5, stop_words: ['the']\n",
|
|
"unique words: 8878 min_df: 1, max_df: 5, stop_words: ['and']\n",
|
|
"unique words: 8879 min_df: 1, max_df: 5, stop_words: ['a']\n",
|
|
"unique words: 8879 min_df: 1, max_df: 5, stop_words: ['I']\n",
|
|
"unique words: 8879 min_df: 1, max_df: 5, stop_words: ['i']\n",
|
|
"unique words: 8877 min_df: 1, max_df: 5, stop_words: ['the', 'and', 'a']\n",
|
|
"unique words: 8601 min_df: 1, max_df: 5, stop_words: english\n",
|
|
"\n",
|
|
"combination\n",
|
|
"unique words: 2856 min_df: 2, max_df: 4, stop_words: english\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 12
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### > Dokumentation <\n",
|
|
"#### min_df und max_df\n",
|
|
"Wenn man min_df und max_df isoliert laufen lässt, so erhält man folgenden Werte für Gesamtzahl der Wörter\n",
|
|
"\n",
|
|
"| n | min_df(n) | max_df(n) |\n",
|
|
"|---|-----------|-----------|\n",
|
|
"| 1 | 8879 | 5508 |\n",
|
|
"| 2 | 3371 | 7349 |\n",
|
|
"| 3 | 1530 | 8079 |\n",
|
|
"| 4 | 800 | 8455 |\n",
|
|
"| 5 | 424 | 8879 |\n",
|
|
"\n",
|
|
"\n",
|
|
"Man kann klar erkennen, dass\n",
|
|
"- min_df schon beim Wert 2 mehr als die Hälfte aller ursprünglichen Wörter entfernt, da es sich dabei um Wörter handelt die nur in einem Dokument vorkommen und somit auch ein einziges Mal über alle Dokumente hinweg vorkommen\n",
|
|
"- mit max_df eine merkliche Reduktion erst bei einem Wert von 3 bzw. 2 auftritt. Beim Wert 1 haben wir dann nur noch Wörter die nur noch in einem Dokument vorkommen.\n",
|
|
"\n",
|
|
"Eine wichtige Beobachtung\n",
|
|
"```\n",
|
|
"min_df(2) + max_df(1) = 8879\n",
|
|
"3371 + 5508 = 8879\n",
|
|
"```\n",
|
|
"\n",
|
|
"Man kann also allgemein sagen, dass gilt\n",
|
|
"```\n",
|
|
"min_df(n+1) + max_df(n) = initial_total_unique_words\n",
|
|
"```\n",
|
|
"\n",
|
|
"#### stop_words\n",
|
|
"Wenn man stop_words isoliert laufen lässt, so erhält man folgenden Werte für Gesamtzahl der Wörter\n",
|
|
"\n",
|
|
"| wert | stop_words(wert) |\n",
|
|
"|-------------|------------------|\n",
|
|
"| the | 8878 |\n",
|
|
"| and | 8878 |\n",
|
|
"| a | 8879 |\n",
|
|
"| i | 8879 |\n",
|
|
"| I | 8879 |\n",
|
|
"| the, and, a | 8877 |\n",
|
|
"| english | 8601 |\n",
|
|
"\n",
|
|
"Man kann erkennen,dass wenn man einzlne Wörter übergibt, auch je ein Wort entfernt wird.\n",
|
|
"\n",
|
|
"Eine wichtige Beobachtung\n",
|
|
"\n",
|
|
"Das Wort \"a\" und andere Wörter im Englischen, die nur aus einem Buchstaben bestehen, scheinen keine Auswirkungen zuhaben, was mich darauf schließen lässt, dass eine Mindestwortlänge von 2 Buchstaben erforderlich ist.\n",
|
|
"> aus der Doku der Fkt: If 'english', a built-in stop word list for English is used.\n",
|
|
"\n",
|
|
"### Kombination\n",
|
|
"\n",
|
|
"Man entfernt die häufigsten Wörter der englischen Sprache mit `stop_words=\"english\"`, die seltensten Wörter mit `min_df=2` und die häufigsten Wörter mit `max_df=4` ohne dabei zu sehr zu entfernen und erhält anschließend eine Menge von 2856 Wörtern.\n",
|
|
"\n",
|
|
"unique words: 2856 min_df: 2, max_df: 4, stop_words: english\n"
|
|
],
|
|
"id": "bc6ee0265d4f595d",
|
|
"attachments": {
|
|
"a1c7631f-3a99-4cc5-91da-f93bf0d162ef.png": {
|
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"image/png": "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"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cb3b2e2e-cda0-4abe-894a-ece8a7cf2d7a",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 6.4 Rescaling the data using term frequency inverse document frequency\n",
|
|
"Here, term frequency is the number of occurences of a term (word) $t$ in a document $d$. \n",
|
|
"\n",
|
|
"$\\operatorname{tf}(t, d) = f_{t, d}$ \n",
|
|
"\n",
|
|
"Sometimes tf gets normalized to the length of $d$\n",
|
|
"The inverse document frequency idf is a measure on the amount of information a term t carries. Rare occurences of t leads to a high amount of information common occurence to a low amount of information. The idf is computed as \n",
|
|
"\n",
|
|
"$\\text{idf}(t) = \\log{\\frac{1 + n}{1+\\text{df}(t)}} + 1$\n",
|
|
"\n",
|
|
"where $n$ is the total number of documents and $\\text{df}(t)$ is the number of documents that contain the term $t$. Hence, the tf-idf is the product of the two terms:\n",
|
|
"\n",
|
|
"$\\text{tf-idf(t,d)}=\\text{tf(t,d)} \\cdot \\text{idf(t)}$\n",
|
|
"\n",
|
|
"scikit-learn supports this in the `TfidfTransformer`, when using the following parameters: `TfidfTransformer(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)`. Refer to the scikit documentation for the parameter sets and how this changes the formula.\n",
|
|
"\n",
|
|
"Combining Bag of Words and tf-idf can be done using the `TfidfVectorizer`"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-15T14:01:04.103470898Z",
|
|
"start_time": "2025-12-15T14:01:04.073400774Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"from sklearn.feature_extraction.text import TfidfTransformer\n",
|
|
"\n",
|
|
"# initialize transformer with given params\n",
|
|
"tfidf_transformer = TfidfTransformer(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)\n",
|
|
"\n",
|
|
"# perform transformation on bag_of_words from 6.3\n",
|
|
"tfidf_matrix = tfidf_transformer.fit_transform(bag_of_words)\n",
|
|
"\n",
|
|
"# check results\n",
|
|
"print(\"Transformation finished.\")\n",
|
|
"print(f\"{tfidf_matrix}\")"
|
|
],
|
|
"id": "cff65dbc718a4179",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Transformation finished.\n",
|
|
"<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
|
|
"\twith 7104 stored elements and shape (5, 2856)>\n",
|
|
" Coords\tValues\n",
|
|
" (0, 0)\t0.009933797940553895\n",
|
|
" (0, 1)\t0.003973519176221558\n",
|
|
" (0, 2)\t0.001986759588110779\n",
|
|
" (0, 3)\t0.004947569788408338\n",
|
|
" (0, 4)\t0.001986759588110779\n",
|
|
" (0, 5)\t0.001986759588110779\n",
|
|
" (0, 6)\t0.001986759588110779\n",
|
|
" (0, 7)\t0.009933797940553895\n",
|
|
" (0, 8)\t0.003973519176221558\n",
|
|
" (0, 9)\t0.011920557528664675\n",
|
|
" (0, 10)\t0.01986759588110779\n",
|
|
" (0, 11)\t0.048557269601616326\n",
|
|
" (0, 12)\t0.001986759588110779\n",
|
|
" (0, 13)\t0.001986759588110779\n",
|
|
" (0, 14)\t0.005960278764332337\n",
|
|
" (0, 15)\t0.001986759588110779\n",
|
|
" (0, 16)\t0.015894076704886233\n",
|
|
" (0, 17)\t0.013907317116775453\n",
|
|
" (0, 18)\t0.02308865901257225\n",
|
|
" (0, 19)\t0.01803555728060035\n",
|
|
" (0, 20)\t0.009895139576816677\n",
|
|
" (0, 21)\t0.003973519176221558\n",
|
|
" (0, 22)\t0.005960278764332337\n",
|
|
" (0, 23)\t0.001986759588110779\n",
|
|
" (0, 24)\t0.01788083629299701\n",
|
|
" :\t:\n",
|
|
" (4, 2801)\t0.01587505840558671\n",
|
|
" (4, 2802)\t0.013177732529343691\n",
|
|
" (4, 2803)\t0.026355465058687383\n",
|
|
" (4, 2804)\t0.01587505840558671\n",
|
|
" (4, 2807)\t0.011085524037007195\n",
|
|
" (4, 2808)\t0.026355465058687383\n",
|
|
" (4, 2813)\t0.11085524037007195\n",
|
|
" (4, 2814)\t0.013177732529343691\n",
|
|
" (4, 2815)\t0.039533197588031074\n",
|
|
" (4, 2816)\t0.013177732529343691\n",
|
|
" (4, 2817)\t0.03175011681117342\n",
|
|
" (4, 2824)\t0.01587505840558671\n",
|
|
" (4, 2825)\t0.013177732529343691\n",
|
|
" (4, 2827)\t0.02217104807401439\n",
|
|
" (4, 2832)\t0.01587505840558671\n",
|
|
" (4, 2833)\t0.013177732529343691\n",
|
|
" (4, 2835)\t0.01587505840558671\n",
|
|
" (4, 2839)\t0.013177732529343691\n",
|
|
" (4, 2840)\t0.011085524037007195\n",
|
|
" (4, 2844)\t0.026355465058687383\n",
|
|
" (4, 2845)\t0.02217104807401439\n",
|
|
" (4, 2850)\t0.052710930117374766\n",
|
|
" (4, 2851)\t0.039533197588031074\n",
|
|
" (4, 2853)\t0.02217104807401439\n",
|
|
" (4, 2854)\t0.03175011681117342\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 13
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "559eab9f-91c5-4a2c-9106-86126c0b8d78",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 6.4.1 Find maximum value for each of the features over dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "1cff3622-5a62-49bb-903f-4f98d9b044fb",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-15T14:01:04.139311264Z",
|
|
"start_time": "2025-12-15T14:01:04.107288791Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Find max TF-IDF value per feature across all documents\n",
|
|
"max_tfidf_values = tfidf_matrix.max(axis=0)\n",
|
|
"\n",
|
|
"# Convert sparse matrix to flat array\n",
|
|
"max_val_array = max_tfidf_values.toarray().flatten()\n",
|
|
"\n",
|
|
"# Get feature names\n",
|
|
"feature_names = vectorizer_combined.get_feature_names_out()\n",
|
|
"\n",
|
|
"# Create DataFrame for better overview\n",
|
|
"df_tfidf_max = pd.DataFrame({\n",
|
|
" 'Word': feature_names,\n",
|
|
" 'Max_TFIDF': max_val_array\n",
|
|
"})\n",
|
|
"\n",
|
|
"# Sort descending to find highest scores\n",
|
|
"df_sorted = df_tfidf_max.sort_values(by='Max_TFIDF', ascending=False)\n",
|
|
"\n",
|
|
"print(df_sorted)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Word Max_TFIDF\n",
|
|
"1076 godfrey 0.533000\n",
|
|
"1200 hopkins 0.442111\n",
|
|
"260 bird 0.374464\n",
|
|
"1449 lestrade 0.365126\n",
|
|
"1790 peter 0.358469\n",
|
|
"... ... ...\n",
|
|
"192 avoided 0.011631\n",
|
|
"1275 indication 0.011631\n",
|
|
"242 belief 0.011631\n",
|
|
"1343 iron 0.011631\n",
|
|
"1118 habit 0.011631\n",
|
|
"\n",
|
|
"[2856 rows x 2 columns]\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 14
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### > Dokumenatation <\n",
|
|
"\n",
|
|
"Die Analyse der maximalen TF-IDF-Werte pro Feature zeigt\n",
|
|
"- Diskriminierende Features: An der Spitze der Liste stehen Begriffe wie \"godfrey\", \"hopkins\" oder \"bird\" mit hohen Scores. Diese Wörter sind stark charakteristisch für einzelne Geschichten und ermöglichen einem Algorithmus eine eindeutige Zuordnung des Textes.\n",
|
|
"- Gemeinsame Features: Am unteren Ende der Liste finden sich Wörter mit sehr niedrigen Scores. Diese kommen in fast allen Dokumenten vor und tragen kaum zur Unterscheidung bei."
|
|
],
|
|
"id": "6a7eea9735a61e3f"
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".env",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.14.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|