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multiplayer_crosswords/data/dictionary_generation.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "e048da07",
"metadata": {},
"source": [
"# Create Dictionaies for crossword clues\n",
"\n",
"this notebook creates dictionaries for crossword clues.\n",
"\n",
"The final dictionaries will be saved as json file, containing a list of entries in the following format:\n",
"\n",
"```json\n",
"{\n",
" \"<unique_word_entry>\": {\n",
" \"word\": \"<word>\",\n",
" \"senses\": [\n",
" \"<definition_1>\",\n",
" \"<definition_2>\",\n",
" \"...\" \n",
" ],\n",
" \"synonyms\": [\n",
" \"<synonym_1>\",\n",
" \"<synonym_2>\",\n",
" \"...\" \n",
" ],\n",
" \"antonyms\": [\n",
" \"<antonym_1>\",\n",
" \"<antonym_2>\",\n",
" \"...\" \n",
" ],\n",
" \"word_frequency\": <frequency_value (from 0 to 100)>\n",
"\n",
" },\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "28040681",
"metadata": {},
"source": [
"### Install some dependencies for that notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f0aecff7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"# install dependencies for this notebooks\n",
"\n",
"!pip install tqdm pandas requests ipywidgets pydantic nltk"
]
},
{
"cell_type": "markdown",
"id": "a0964dfc",
"metadata": {},
"source": [
"### Import Libraries and define Constants and source urls"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e7d3d24f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_177453/1748613008.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
" from tqdm.autonotebook import tqdm\n"
]
}
],
"source": [
"# import necessary libraries\n",
"import pandas as pd\n",
"import requests\n",
"from tqdm.autonotebook import tqdm \n",
"from pathlib import Path\n",
"import json\n",
"# some constants\n",
"\n",
"CACHE_DIR = Path(\"./.cache\")\n",
"CACHE_DIR.mkdir(exist_ok=True)\n",
"\n",
"CRYPTICS_CROSSWORDS_DB_URL = \"https://cryptics.georgeho.org/data/clues.csv?_stream=on&_size=max\"\n",
"CRYPTICS_CROSSWORDS_DB_CSV = CACHE_DIR / \"cryptics_clues.csv\"\n",
"\n",
"# german wictionary data:\n",
"\n",
"\n",
"COMPRESSED_DE_WIKTIONARY_DUMP_URL = \"https://kaikki.org/dewiktionary/raw-wiktextract-data.jsonl.gz\"\n",
"COMPRESSED_DE_WIKTIONARY_DUMP = CACHE_DIR / \"de_wiktionary.jsonl.gz\""
]
},
{
"cell_type": "markdown",
"id": "61263a61",
"metadata": {},
"source": [
"## Download External Data\n",
"\n",
"* Crypticts DB (\"https://cryptics.georgeho.org/\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "806a5c51",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n",
"[nltk_data] Downloading package omw-1.4 to /home/jonas/nltk_data...\n",
"[nltk_data] Package omw-1.4 is already up-to-date!\n",
"[nltk_data] Downloading package omw to /home/jonas/nltk_data...\n",
"[nltk_data] Package omw is already up-to-date!\n"
]
}
],
"source": [
"# download the cryptics crosswords database if not already cached \n",
"if not CRYPTICS_CROSSWORDS_DB_CSV.exists():\n",
" response = requests.get(CRYPTICS_CROSSWORDS_DB_URL)\n",
" with open(CRYPTICS_CROSSWORDS_DB_CSV, \"wb\") as f:\n",
" f.write(response.content)\n",
"\n",
"# download wordnet from nltk\n",
"import nltk\n",
"nltk.download('wordnet')\n",
"nltk.download('omw-1.4') # optional, extra languages / lemmas\n",
"nltk.download('omw') # try the older omw package\n",
"\n",
"# download the german wiktionary dump if not already cached\n",
"if not COMPRESSED_DE_WIKTIONARY_DUMP.exists():\n",
" response = requests.get(COMPRESSED_DE_WIKTIONARY_DUMP_URL, stream=True)\n",
" with open(COMPRESSED_DE_WIKTIONARY_DUMP, \"wb\") as f:\n",
" for chunk in tqdm(response.iter_content(chunk_size=8192), desc=\"Downloading de wiktionary dump\"):\n",
" f.write(chunk)"
]
},
{
"cell_type": "markdown",
"id": "26060068",
"metadata": {},
"source": [
"## Define our Datastructures"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8c81708c",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel\n",
"import re \n",
"\n",
"class WordEntry(BaseModel):\n",
" word: str\n",
" senses: list[str]\n",
" synonyms: list[str]\n",
" antonyms: list[str]\n",
" word_frequency: int # frequency rank of the word (0% - 100%)\n",
" source: str # source of the word entry (e.g., \"cryptics\", \"wordnet\", etc.)\n",
" categories: list[str] # categories or tags associated with the word entry\n",
"\n",
"class Dictionary(BaseModel):\n",
" entries: dict[str, WordEntry] # mapping from word to WordEntry\n",
" def add_entry(self, entry: WordEntry):\n",
" if entry.word not in self.entries: \n",
" self.entries[entry.word] = entry\n",
" else:\n",
" if entry.source == self.entries[entry.word].source:\n",
" # merge entries if word already exists\n",
" existing_entry = self.entries[entry.word]\n",
" existing_entry.senses = list(set(existing_entry.senses) | set(entry.senses))\n",
" existing_entry.synonyms = list(set(existing_entry.synonyms) | set(entry.synonyms))\n",
" existing_entry.antonyms = list(set(existing_entry.antonyms) | set(entry.antonyms))\n",
" existing_entry.categories = list(set(existing_entry.categories) | set(entry.categories))\n",
" existing_entry.word_frequency = max(existing_entry.word_frequency, entry.word_frequency)\n",
" else:\n",
" # create a new entry\n",
" word = entry.word\n",
" i = 1\n",
" while f\"{word}_{i}\" in self.entries:\n",
" i += 1 \n",
" self.entries[f\"{word}_{i}\"] = entry\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"id": "b1397355",
"metadata": {},
"source": [
"## Parse Data (EN)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "56988415",
"metadata": {},
"outputs": [],
"source": [
"en_db = Dictionary (entries={})"
]
},
{
"cell_type": "markdown",
"id": "d5b35c1b",
"metadata": {},
"source": [
"### Parse cryptics DB"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e9e1ee43",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75b431216f3249c7879d87fe33f7817a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/42 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# csv structure: we will use the column clue as senses and the column answer (lowercase) as word.\n",
"# words that have spaces will be skipped\n",
"\n",
"# read the file in batches to avoid memory issues \n",
"batch_size = 1000\n",
"\n",
"# Calculate total lines safely\n",
"try:\n",
" total_lines = sum(1 for line in open(CRYPTICS_CROSSWORDS_DB_CSV))\n",
"except Exception as e:\n",
" print(f\"Error counting lines: {e}\")\n",
" total_lines = 0\n",
"\n",
"cryptics_cols = \"rowid\tclue\tanswer\tdefinition\tclue_number\tpuzzle_date\tpuzzle_name\tsource_url\tsource\".split()\n",
"\n",
"for start_row in tqdm(range(1, total_lines, batch_size)):\n",
" try:\n",
" # Use on_bad_lines='skip' to handle rows with too many fields\n",
" df = pd.read_csv(CRYPTICS_CROSSWORDS_DB_CSV, skiprows=start_row, nrows=batch_size, names=cryptics_cols, on_bad_lines='skip')\n",
" except Exception as e:\n",
" print(f\"Error reading batch starting at {start_row}: {e}\")\n",
" continue\n",
"\n",
" for index, row in df.iterrows():\n",
" # Check if answer is a string (handles NaN)\n",
" if not isinstance(row['answer'], str):\n",
" continue\n",
"\n",
" word = row['answer'].lower()\n",
" if ' ' in word:\n",
" continue\n",
" \n",
" # Check if clue is a string\n",
" if not isinstance(row['clue'], str):\n",
" continue\n",
"\n",
" clue = row['clue']\n",
" # replace - and _ with empty string\n",
" word = word.replace(\"-\", \"\").replace(\"_\", \"\")\n",
" word = word.lower()\n",
"\n",
" # remove numbers in parentheses or brackets (e.g. (5), [4], (3,4), [1-9])\n",
" clue = re.sub(r'\\s*[(\\[][\\d,\\-\\s]+[)\\]]$', '', clue).strip()\n",
"\n",
" # if the word is not alphabetic, skip it\n",
" if not word.isalpha():\n",
" continue \n",
" en_db.add_entry(WordEntry(\n",
" word=word,\n",
" senses=[clue],\n",
" synonyms=[],\n",
" antonyms=[],\n",
" word_frequency=-1, # placeholder frequency\n",
" source=\"cryptics\",\n",
" categories=[\"cryptic_clue\"]\n",
" ))"
]
},
{
"cell_type": "markdown",
"id": "62734af4",
"metadata": {},
"source": [
"### Parse Wordnet Data\n",
"\n",
"* import necessary stuff:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "32280691",
"metadata": {},
"outputs": [],
"source": [
"from nltk.corpus import wordnet as wn\n"
]
},
{
"cell_type": "markdown",
"id": "e13bf2f3",
"metadata": {},
"source": [
"* Parse synsets"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b21721e2",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f8d4c77558e478b9cf214d851fd503e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/117659 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_synsets = wn.all_synsets()\n",
"\n",
"def frequency_metric(lemma, word):\n",
" # derive a frequency metric, based on polysemy and lemma count:\n",
" # 1. SemCor frequency count for this specific sense\n",
" semcor_count = lemma.count()\n",
" # 2. Polysemy: number of synsets for this word\n",
" polysemy_count = len(wn.synsets(word))\n",
" \n",
" return semcor_count + polysemy_count\n",
" \n",
"\n",
"for synset in tqdm(list(all_synsets)):\n",
" #print(synset.name(), synset.definition() )\n",
"\n",
" # find the first \"good\" lemma name (only alphabetic characters) \n",
" # WE NEED LEMMA OBJECTS NOW, NOT JUST NAMES\n",
" good_lemmas = [lemma for lemma in synset.lemmas() if lemma.name().isalpha()] \n",
" if not good_lemmas:\n",
" continue \n",
"\n",
" target_lemma = good_lemmas[0]\n",
" word = target_lemma.name().lower()\n",
" clue = synset.definition()\n",
"\n",
" # Calculate frequency\n",
" raw_metric = frequency_metric(target_lemma, word)\n",
" \n",
" # Normalize to 0-100 range\n",
" # Values can range from 1 to >200 for very common words.\n",
" # We apply a factor and clamp.\n",
" # Using factor 1.0 means 100 count -> 100 frequency.\n",
" word_frequency = min(100, int(raw_metric))\n",
"\n",
" en_db.add_entry(WordEntry(\n",
" word=word,\n",
" senses=[clue],\n",
" synonyms=[],\n",
" antonyms=[],\n",
" word_frequency=word_frequency,\n",
" source=\"wordnet\",\n",
" categories=[\"wordnet\"]\n",
" ))\n",
" \n",
" #break"
]
},
{
"cell_type": "markdown",
"id": "1e246fd0",
"metadata": {},
"source": [
"## Parse German Data\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "63953ce6",
"metadata": {},
"outputs": [],
"source": [
"de_db = Dictionary (entries={})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "435d0b78",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76b0adb1fab244feaa5ab51c985fbe5f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing German Wiktionary entries: 0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed 78859 entries from German Wiktionary dump.\n"
]
}
],
"source": [
"# inspect data first. File contains jsonl.gz entries per line\n",
"\n",
"import gzip\n",
"import json\n",
"import difflib\n",
"\n",
"# define a helper function to fince the similarity between words. Used to sort out glosses with words too similar to the search word.\n",
"def _similarity_ratio(word1, word2):\n",
" return difflib.SequenceMatcher(None, word1.lower(), word2.lower()).ratio()\n",
"\n",
"def _ascii_word(word):\n",
" word = word.lower()\n",
" word = word.replace(\"ä\", \"ae\")\n",
" word = word.replace(\"ö\", \"oe\")\n",
" word = word.replace(\"ü\", \"ue\")\n",
" word = word.replace(\"ß\", \"ss\")\n",
"\n",
" return word \n",
"\n",
"def _only_acscii_chars_in_word(word):\n",
" # returns true if only the ascii alphabet is in the word\n",
" return all(c.isascii() and c.isalpha() for c in word)\n",
"\n",
"\n",
"def get_best_gloss_for_sesne(word, sense): \n",
" normalized_word = word.lower()\n",
"\n",
" glosses = sense.get(\"glosses\", [])\n",
" for g in glosses:\n",
" if normalized_word in g.lower():\n",
" continue\n",
" \n",
" # Use similarity ratio to skip glosses that are too close to the word itself (e.g. simple variations).\n",
" # check each word in the gloss\n",
" gloss_words = re.findall(r'\\b\\w+\\b', g.lower()) \n",
" found_similar = False \n",
" for gw in gloss_words:\n",
" if _similarity_ratio(normalized_word, gw) > 0.8:\n",
" #print(\"too similar:\", normalized_word, gw, \"->\", _similarity_ratio(normalized_word, gw), g)\n",
" found_similar = True \n",
" break \n",
" \n",
" if found_similar:\n",
" continue \n",
" return g\n",
"\n",
" return None\n",
"\n",
"def calculate_frequency_score(json_data):\n",
" # A heuristic to estimate word frequency/commonality based on available data\n",
" score = 0\n",
" \n",
" # 1. Number of senses (polysemy): Common words usually have multiple meanings\n",
" senses = json_data.get(\"senses\", [])\n",
" score += len(senses) * 2\n",
" \n",
" # 2. Number of translations: Common words are translated into many languages\n",
" translations = json_data.get(\"translations\", [])\n",
" score += len(translations) * 0.5\n",
" \n",
" # 3. Has audio pronunciation? Common words usually do.\n",
" sounds = json_data.get(\"sounds\", [])\n",
" if sounds:\n",
" score += 5\n",
" \n",
" # 4. Check for \"rare\", \"obsolete\", \"archaic\" tags in senses\n",
" # If a word is ONLY archaic, it should be low frequency.\n",
" # But usually we want to just boost the \"normal\" ones.\n",
" \n",
" # Normalize heavily. \n",
" # A word like \"Haus\" might have huge scores.\n",
" # We want a 0-100 scale.\n",
" \n",
" return min(100, int(score))\n",
"\n",
"def process_entry(json_data, min_freq_score=10):\n",
" senses = json_data.get(\"senses\", []) \n",
" processed_senses = []\n",
" tags = set()\n",
" for sense in senses:\n",
" glosses = sense .get(\"glosses\", [])\n",
" topic_labels = sense.get(\"topics\", [])\n",
" best_gloss = get_best_gloss_for_sesne(json_data.get(\"word\", \"\"), sense)\n",
" for topic in topic_labels:\n",
" tags.add(topic)\n",
" if best_gloss:\n",
" text = best_gloss\n",
" if topic_labels and len(topic_labels) > 0:\n",
" text = \"\" + \", \".join(topic_labels) + \": \" + text\n",
" processed_senses.append(text)\n",
" \n",
" # Calculate Frequency\n",
" freq = calculate_frequency_score(json_data)\n",
"\n",
" if freq < min_freq_score:\n",
" return [] # skip low frequency words\n",
"\n",
" if not _only_acscii_chars_in_word(_ascii_word(json_data.get(\"word\", \"\"))):\n",
" return [] # skip non-ascii words\n",
"\n",
" if len(processed_senses) == 0:\n",
" return [] # skip entries with no valid senses \n",
" \n",
" de_db.add_entry(WordEntry(\n",
" word=_ascii_word(json_data.get(\"word\", \"\").lower()),\n",
" senses=processed_senses,\n",
" synonyms=[],\n",
" antonyms=[],\n",
" word_frequency=freq, \n",
" source=\"de_wiktionary\",\n",
" categories=list(tags)\n",
" )) \n",
"\n",
" return processed_senses\n",
"\n",
"def parse_entry(json_line):\n",
"\n",
" \n",
" #print(\"\\n\")\n",
" #print(\"Parsing entry:\", json_line)\n",
" json_data = json.loads(json_line) \n",
" lang_code = json_data.get(\"lang_code\", \"unknown\").lower()\n",
" if lang_code != \"de\":\n",
" return False \n",
" #print(\"word:\", json_data.get(\"word\"))\n",
" senses = json_data.get(\"senses\", []) \n",
" processed_senses = process_entry(json_data)\n",
" if len(processed_senses) == 0:\n",
" #print(\"No valid senses found, skipping.\")\n",
" return False \n",
" #print(\"Senses / glosses:\", processed_senses)\n",
" return True\n",
"\n",
"# read file in unzipping on the fly using gzip module\n",
"# \"rt\" mode opens it as text, handling newlines correctly after decompression\n",
"with gzip.open(COMPRESSED_DE_WIKTIONARY_DUMP, \"rt\", encoding=\"utf-8\") as f:\n",
" i = 0\n",
" for _, line in enumerate(tqdm(f, desc=\"Processing German Wiktionary entries\" ) ):\n",
" #if i >= 10: \n",
" # break\n",
" if line.strip():\n",
" if parse_entry(line.strip()):\n",
" i += 1\n",
" print(f\"Processed {i} entries from German Wiktionary dump.\")"
]
},
{
"cell_type": "markdown",
"id": "be16b393",
"metadata": {},
"source": [
"### Save extracted databases\n",
"\n",
"Dump the db to disk as json"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "69b67091",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6d1490c57118467fb95cdc111114f926",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/96407 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wrote 74357 entries to en.json\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "daad07fbef564f06a7079d99a3291125",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/77291 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wrote 77291 entries to de.json\n"
]
}
],
"source": [
"EN_PATH = Path(\"./en.json\")\n",
"DE_PATH = Path(\"./de.json\") \n",
"\n",
"# file, db tuples\n",
"FILES_DBS = [\n",
" (EN_PATH, en_db),\n",
" (DE_PATH, de_db)\n",
"] \n",
"\n",
"import json\n",
"\n",
"# entries to include:\n",
"INCLUDED_SOURCES = {\n",
" #\"cryptics\",\n",
" \"wordnet\",\n",
" \"de_wiktionary\"\n",
"} \n",
"\n",
"for FILE_PATH, DB in FILES_DBS: \n",
" with open(FILE_PATH, \"w\") as f:\n",
" f.write(\"{\\n\")\n",
" i = 0\n",
" for key, value in tqdm( DB.entries.items()):\n",
"\n",
" if value.source not in INCLUDED_SOURCES:\n",
" continue \n",
" \n",
" # dump json\n",
" if i > 0:\n",
" f.write(\",\\n\")\n",
" d_value = value.model_dump()\n",
" as_json = json.dumps(\n",
" d_value, indent=4\n",
" )\n",
" as_json = \"\\n \".join(as_json.split(\"\\n\"))\n",
" as_json = \" \\\"\" + key + \"\\\": \" + as_json\n",
" f.write(as_json)\n",
" i += 1\n",
" \n",
" f.write(\"\\n}\\n\")\n",
" print(f\"Wrote {i} entries to {FILE_PATH}\" )\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f91aee6f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "multiplayer-crosswords-W02cfZ32-py3.12",
"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": "3.12.3"
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"nbformat": 4,
"nbformat_minor": 5
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