ancient-scripts-datasets / verify_wiktionary.py
Alvin
Add complete dataset: all sources, metadata, scripts, docs, and phylo enrichment
26786e3
"""
Wiktionary Verification Script (v2 - with corrected title building)
Verifies that entries sourced from Wiktionary actually exist on Wiktionary
by querying the MediaWiki API.
Key fixes from v1:
- Preserve trailing dashes for reconstruction forms (PIE, Semitic, etc.)
- Use "Proto-Hellenic" instead of "Proto-Greek" for grk-pro
- Batch queries (up to 50 titles per API call) for efficiency
"""
import csv
import json
import random
import sys
import time
from pathlib import Path
# Force UTF-8 output on Windows
if sys.platform == "win32":
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
sys.stderr.reconfigure(encoding="utf-8", errors="replace")
import requests
LEXICON_DIR = Path(r"C:\Users\alvin\hf-ancient-scripts\data\training\lexicons")
API_URL = "https://en.wiktionary.org/w/api.php"
SAMPLE_SIZE = 20
RATE_LIMIT = 0.5 # seconds between API requests
# Wiktionary requires a proper User-Agent header per their API policy
SESSION = requests.Session()
SESSION.headers.update({
"User-Agent": "AncientScriptsVerifier/1.0 (https://github.com/; academic research) python-requests",
})
# Mapping from ISO code to Wiktionary reconstruction namespace language name
# NOTE: grk-pro uses "Proto-Hellenic" on Wiktionary, NOT "Proto-Greek"
RECONSTRUCTION_LANGS = {
"ine-pro": "Proto-Indo-European",
"sem-pro": "Proto-Semitic",
"ccs-pro": "Proto-Kartvelian",
"dra-pro": "Proto-Dravidian",
"grk-pro": "Proto-Hellenic",
}
# Human-readable names for all languages
LANG_NAMES = {
"hit": "Hittite",
"uga": "Ugaritic",
"phn": "Phoenician",
"ave": "Avestan",
"peo": "Old Persian",
"xpg": "Phrygian",
"xle": "Lemnian",
"cms": "Messapic",
"elx": "Elamite",
"ett": "Etruscan",
"xcr": "Carian",
"xlc": "Lycian",
"xld": "Lydian",
"xlw": "Luwian",
"xrr": "Raetic",
"xur": "Urartian",
"txb": "Tocharian B",
"xto": "Tocharian A",
"xhu": "Hurrian",
"ine-pro": "Proto-Indo-European",
"sem-pro": "Proto-Semitic",
"ccs-pro": "Proto-Kartvelian",
"dra-pro": "Proto-Dravidian",
"grk-pro": "Proto-Greek (Proto-Hellenic on Wiktionary)",
}
def load_wiktionary_entries(lang_code: str) -> list[dict]:
"""Load entries from a TSV where Source contains 'wiktionary'."""
tsv_path = LEXICON_DIR / f"{lang_code}.tsv"
entries = []
with open(tsv_path, "r", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for row in reader:
src = row.get("Source", "")
if "wiktionary" in src.lower():
entries.append(row)
return entries
def build_wiktionary_title(word: str, lang_code: str) -> str:
"""Build the Wiktionary page title for a given word and language.
For reconstruction languages, uses Reconstruction: namespace.
IMPORTANT: Trailing dashes are PRESERVED for reconstruction forms,
as Wiktionary uses them in page titles (e.g., h₃rewk- not h₃rewk).
"""
clean = word.strip()
if lang_code in RECONSTRUCTION_LANGS:
lang_name = RECONSTRUCTION_LANGS[lang_code]
# Remove leading * only. Keep trailing - (Wiktionary uses it!)
stem = clean.lstrip("*").strip()
return f"Reconstruction:{lang_name}/{stem}"
# For regular languages, use the word directly
# Remove leading hyphens (morphological prefix markers)
clean = clean.lstrip("-")
return clean
def query_wiktionary_batch(titles: list[str]) -> dict[str, bool]:
"""Query Wiktionary API for up to 50 titles at once.
Returns dict mapping title -> exists (bool).
"""
results = {}
for i in range(0, len(titles), 50):
batch = titles[i:i + 50]
params = {
"action": "query",
"titles": "|".join(batch),
"format": "json",
}
try:
resp = SESSION.get(API_URL, params=params, timeout=15)
resp.raise_for_status()
data = resp.json()
pages = data.get("query", {}).get("pages", {})
# Track normalization mapping
normalized = {}
for n in data.get("query", {}).get("normalized", []):
normalized[n["to"]] = n["from"]
for page_id, page_info in pages.items():
api_title = page_info.get("title", "")
exists = int(page_id) != -1 and "missing" not in page_info
orig_title = normalized.get(api_title, api_title)
results[orig_title] = exists
results[api_title] = exists
except Exception as e:
for t in batch:
results[t] = False
print(f" API error for batch: {e}")
if i + 50 < len(titles):
time.sleep(RATE_LIMIT)
return results
def verify_language(lang_code: str, rng: random.Random) -> dict:
"""Verify a sample of entries for one language."""
entries = load_wiktionary_entries(lang_code)
if not entries:
return {
"lang": lang_code,
"lang_name": LANG_NAMES.get(lang_code, lang_code),
"total_entries": 0,
"sampled": 0,
"verified": 0,
"not_found": 0,
"verification_rate": 0,
"results": [],
}
sample_size = min(SAMPLE_SIZE, len(entries))
sample = rng.sample(entries, sample_size)
# Build titles
word_to_title = {}
for entry in sample:
word = entry["Word"]
title = build_wiktionary_title(word, lang_code)
word_to_title[word] = title
# Batch query
unique_titles = list(set(word_to_title.values()))
existence_map = query_wiktionary_batch(unique_titles)
# Build results
results = []
for entry in sample:
word = entry["Word"]
title = word_to_title[word]
exists = existence_map.get(title, False)
results.append({
"original_word": word,
"query_title": title,
"exists": exists,
})
verified = sum(1 for r in results if r["exists"])
not_found = sum(1 for r in results if not r["exists"])
return {
"lang": lang_code,
"lang_name": LANG_NAMES.get(lang_code, lang_code),
"total_entries": len(entries),
"sampled": sample_size,
"verified": verified,
"not_found": not_found,
"verification_rate": verified / sample_size * 100 if sample_size > 0 else 0,
"results": results,
}
def main():
rng = random.Random(42)
# Discover all languages with wiktionary sources
all_langs = []
for tsv in sorted(LEXICON_DIR.glob("*.tsv")):
lang_code = tsv.stem
with open(tsv, "r", encoding="utf-8") as f:
content = f.read()
if "\twiktionary" in content.lower():
all_langs.append(lang_code)
print(f"Found {len(all_langs)} languages with Wiktionary sources: {', '.join(all_langs)}")
print(f"Sampling up to {SAMPLE_SIZE} entries per language")
print("=" * 100)
all_results = []
total_sampled = 0
total_verified = 0
total_not_found = 0
flagged_words = []
for i, lang_code in enumerate(all_langs):
print(f"\n[{i+1}/{len(all_langs)}] Verifying {lang_code} ({LANG_NAMES.get(lang_code, '?')})...")
result = verify_language(lang_code, rng)
all_results.append(result)
total_sampled += result["sampled"]
total_verified += result["verified"]
total_not_found += result["not_found"]
for r in result["results"]:
if not r["exists"]:
flagged_words.append({
"lang": lang_code,
"lang_name": LANG_NAMES.get(lang_code, lang_code),
"word": r["original_word"],
"query_title": r["query_title"],
})
# Progress
print(f" Total entries: {result['total_entries']}, Sampled: {result['sampled']}, "
f"Verified: {result['verified']}, Not found: {result['not_found']} "
f"({result['verification_rate']:.0f}% verified)")
if result["not_found"] > 0:
missing = [r for r in result["results"] if not r["exists"]]
for m in missing[:5]:
print(f" NOT FOUND: '{m['original_word']}' (queried as: '{m['query_title']}')")
if len(missing) > 5:
print(f" ... and {len(missing) - 5} more")
# === SUMMARY ===
print("\n" + "=" * 100)
print("SUMMARY")
print("=" * 100)
print(f"\nTotal languages checked: {len(all_langs)}")
print(f"Total entries sampled: {total_sampled}")
print(f"Total verified on Wiktionary: {total_verified}")
print(f"Total NOT found: {total_not_found}")
if total_sampled > 0:
print(f"Overall verification rate: {total_verified / total_sampled * 100:.1f}%")
print("\n" + "-" * 100)
print("PER-LANGUAGE BREAKDOWN (sorted by verification rate)")
print("-" * 100)
header = f"{'Lang':<10} {'Name':<42} {'Total':<8} {'Sampled':<9} {'Verified':<9} {'Not Found':<10} {'Rate':<8}"
print(header)
print("-" * 100)
for r in sorted(all_results, key=lambda x: x["verification_rate"]):
print(f"{r['lang']:<10} {r['lang_name']:<42} {r['total_entries']:<8} {r['sampled']:<9} "
f"{r['verified']:<9} {r['not_found']:<10} {r['verification_rate']:.0f}%")
# Categorize results
high_confidence = [r for r in all_results if r["verification_rate"] >= 80]
medium_confidence = [r for r in all_results if 40 <= r["verification_rate"] < 80]
low_confidence = [r for r in all_results if r["verification_rate"] < 40]
print("\n" + "=" * 100)
print("INTERPRETATION")
print("=" * 100)
if high_confidence:
print(f"\nHIGH CONFIDENCE (>=80% verified) - {len(high_confidence)} languages:")
print(" These entries are strongly validated as real Wiktionary content.")
for r in sorted(high_confidence, key=lambda x: -x["verification_rate"]):
print(f" {r['lang']:<10} {r['lang_name']:<40} {r['verification_rate']:.0f}%")
if medium_confidence:
print(f"\nMEDIUM CONFIDENCE (40-79% verified) - {len(medium_confidence)} languages:")
print(" Many entries verified, but some may use different transliteration/spelling conventions.")
print(" The unverified entries may still be real but use non-standard forms.")
for r in sorted(medium_confidence, key=lambda x: -x["verification_rate"]):
print(f" {r['lang']:<10} {r['lang_name']:<40} {r['verification_rate']:.0f}%")
if low_confidence:
print(f"\nLOW CONFIDENCE (<40% verified) - {len(low_confidence)} languages:")
print(" These may be from Wiktionary appendices/categories rather than individual pages,")
print(" or use significantly different transliteration conventions than Wiktionary page titles.")
for r in sorted(low_confidence, key=lambda x: -x["verification_rate"]):
print(f" {r['lang']:<10} {r['lang_name']:<40} {r['verification_rate']:.0f}%")
if flagged_words:
print("\n" + "-" * 100)
print(f"ALL FLAGGED WORDS NOT FOUND ON WIKTIONARY ({len(flagged_words)} total)")
print("-" * 100)
current_lang = None
for fw in flagged_words:
if fw["lang"] != current_lang:
current_lang = fw["lang"]
print(f"\n [{fw['lang']}] {fw['lang_name']}:")
print(f" word='{fw['word']}' queried='{fw['query_title']}'")
# Save detailed results
output_path = Path(r"C:\Users\alvin\hf-ancient-scripts\wiktionary_verification_results.json")
with open(output_path, "w", encoding="utf-8") as f:
json.dump({
"summary": {
"total_languages": len(all_langs),
"total_sampled": total_sampled,
"total_verified": total_verified,
"total_not_found": total_not_found,
"overall_verification_rate": total_verified / total_sampled * 100 if total_sampled > 0 else 0,
},
"per_language": all_results,
"flagged_words": flagged_words,
}, f, indent=2, ensure_ascii=False)
print(f"\nDetailed results saved to: {output_path}")
if __name__ == "__main__":
main()