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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Words</th>\n",
" <th>Frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>有</td>\n",
" <td>51227728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>我</td>\n",
" <td>43798085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>一</td>\n",
" <td>43159170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>的</td>\n",
" <td>40916482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>你</td>\n",
" <td>30897176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133207</th>\n",
" <td>黎明網</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133208</th>\n",
" <td>黎錦華</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133209</th>\n",
" <td>墨包</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133210</th>\n",
" <td>點晒穴</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133211</th>\n",
" <td>齋頂</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>133212 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Words Frequency\n",
"0 有 51227728\n",
"1 我 43798085\n",
"2 一 43159170\n",
"3 的 40916482\n",
"4 你 30897176\n",
"... ... ...\n",
"133207 黎明網 12\n",
"133208 黎錦華 12\n",
"133209 墨包 12\n",
"133210 點晒穴 12\n",
"133211 齋頂 12\n",
"\n",
"[133212 rows x 2 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Load Excel file and convert to dictionary\n",
"df = pd.read_excel('CyberCan.xlsx')\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"with open(\"CyberCan.dict\", \"w+\") as output_file:\n",
" for index, row in df.iterrows():\n",
" word = str(row['Words']).strip()\n",
" if not \" \" in word:\n",
" output_file.write(word + \" \" + str(row['Frequency']) + \"\\n\")\n",
" output_file.flush()\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total words: 132895\n"
]
}
],
"source": [
"puncts = [\"，\", \"。\", \"！\", \"？\", \"「\", \"」\", \"：\"]\n",
"cybercan_words = set()\n",
"\n",
"for word in list(df['Words'].values) + puncts:\n",
" cybercan_words.add(word)\n",
"\n",
"print(\"Total words: {}\".format(len(cybercan_words)))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"import jieba\n",
"jieba.set_dictionary(\"CyberCan.dict\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total filtered lines: 140590\n"
]
}
],
"source": [
"import re\n",
"\n",
"alnum = re.compile(\"[a-zA-Z0-9]\")\n",
"filtered_lines = []\n",
"\n",
"with open(\"train/lihkg.can\", \"r\") as input_file:\n",
" for line in input_file.read().splitlines():\n",
" line = line.replace(\" \", \"\")\n",
" if len(line) < 10:\n",
" continue\n",
" if len(line) >= 64:\n",
" continue\n",
" if alnum.search(line):\n",
" continue\n",
" tokens = list(jieba.cut(line))\n",
" found_rare_word = False\n",
" for token in tokens:\n",
" if not token in cybercan_words:\n",
" found_rare_word = True\n",
" # print(\"Found rare word: {}\".format(token))\n",
" break\n",
" if found_rare_word:\n",
" continue\n",
" filtered_lines.append(line)\n",
"\n",
"print(\"Total filtered lines: {}\".format(len(filtered_lines)))\n",
"\n",
"with open(\"train/lihkg.filtered.can\", \"w+\") as output_file:\n",
" for line in filtered_lines:\n",
" output_file.write(line + \"\\n\")\n",
" output_file.flush()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.6"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
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