#!/usr/bin/env python3 """ Extract cognate pairs from 4 Tier 1 CLDF repositories. Sources: 1. lexibank/iecor — IE-CoR (Indo-European Cognate Relationships) Heggarty et al. 2024, Scientific Data (Nature) License: CC-BY-4.0 2. lexibank/kitchensemitic — Kitchen et al. 2009, Proc. R. Soc. B License: CC-BY-NC-4.0 3. lexibank/robbeetstriangulation — Robbeets et al. 2021, Nature License: CC-BY-4.0 4. lexibank/savelyevturkic — Savelyev & Robbeets 2020, J. Language Evolution License: CC-BY-4.0 All data extracted from CLDF CognateTable files. No data is hardcoded. Output: 14-column TSV staging files per source. """ import csv import os import sys import unicodedata from collections import defaultdict from itertools import combinations from pathlib import Path # ── Sound Class Alphabet (List 2012) ── # Reference: List, J.-M. (2012). "SCA: Phonetic alignment based on sound classes." # New Directions in Logic, Language, and Computation, Springer. SCA_MAP = { # Vowels → V 'a': 'A', 'e': 'E', 'i': 'I', 'o': 'O', 'u': 'U', 'ɑ': 'A', 'æ': 'A', 'ɐ': 'A', 'ə': 'E', 'ɛ': 'E', 'ɪ': 'I', 'ɨ': 'I', 'ɔ': 'O', 'ʊ': 'U', 'ʉ': 'U', 'ɯ': 'U', 'ø': 'O', 'œ': 'O', 'y': 'U', 'ɤ': 'O', 'ɒ': 'O', 'ʌ': 'A', # Stops 'p': 'P', 'b': 'P', 't': 'T', 'd': 'T', 'k': 'K', 'g': 'K', 'q': 'K', 'ɢ': 'K', 'ʔ': 'H', 'c': 'K', 'ɟ': 'K', 'ʈ': 'T', 'ɖ': 'T', # Fricatives 'f': 'P', 'v': 'P', 's': 'S', 'z': 'S', 'ʃ': 'S', 'ʒ': 'S', 'x': 'K', 'ɣ': 'K', 'h': 'H', 'ɦ': 'H', 'θ': 'T', 'ð': 'T', 'ç': 'K', 'ʝ': 'K', 'χ': 'K', 'ʁ': 'R', 'ħ': 'H', 'ʕ': 'H', 'ɸ': 'P', 'β': 'P', 'ʂ': 'S', 'ʐ': 'S', # Nasals 'm': 'M', 'n': 'N', 'ŋ': 'N', 'ɲ': 'N', 'ɳ': 'N', 'ɴ': 'N', # Liquids 'l': 'L', 'r': 'R', 'ɾ': 'R', 'ɹ': 'R', 'ɻ': 'R', 'ɬ': 'L', 'ɮ': 'L', 'ʎ': 'L', 'ɭ': 'L', 'ʟ': 'L', # Glides 'w': 'W', 'j': 'Y', 'ʋ': 'W', 'ɰ': 'W', # Affricates (common) 'ʦ': 'S', 'ʧ': 'S', 'ʤ': 'S', 'ʣ': 'S', } def ipa_to_sca(ipa: str) -> str: """Convert IPA string to SCA encoding.""" if not ipa or ipa == '-': return '-' result = [] # NFC normalize ipa = unicodedata.normalize('NFC', ipa) for ch in ipa: base = ch.lower() cat = unicodedata.category(ch) # Skip combining marks, suprasegmentals, brackets, whitespace if cat.startswith('M') or ch in 'ˈˌːˑ[]/()\u0361\u035c' or cat == 'Zs': continue if base in SCA_MAP: result.append(SCA_MAP[base]) # Skip unknown characters silently (diacritics, tone marks, etc.) return ''.join(result) def sca_distance(sca_a: str, sca_b: str) -> float: """ Normalized SCA-weighted Levenshtein distance. Returns similarity score in [0.0, 1.0]. Reference: List (2012), gap penalty = 0.5. """ if sca_a == '-' or sca_b == '-' or not sca_a or not sca_b: return 0.0 n, m = len(sca_a), len(sca_b) gap = 0.5 # DP matrix dp = [[0.0] * (m + 1) for _ in range(n + 1)] for i in range(n + 1): dp[i][0] = i * gap for j in range(m + 1): dp[0][j] = j * gap for i in range(1, n + 1): for j in range(1, m + 1): if sca_a[i-1] == sca_b[j-1]: cost = 0.0 else: cost = 1.0 dp[i][j] = min( dp[i-1][j] + gap, dp[i][j-1] + gap, dp[i-1][j-1] + cost, ) max_len = max(n, m) if max_len == 0: return 1.0 return round(1.0 - dp[n][m] / max_len, 4) def load_cldf_source(repo_dir: str, source_name: str): """ Load a CLDF repo and extract cognate pairs. Reads: - cldf/languages.csv → language ID → ISO mapping - cldf/forms.csv → form ID → (language, word, IPA, concept) - cldf/cognates.csv → form ID → cognate set membership Returns list of 14-column rows. All data comes from the downloaded CSV files. """ cldf_dir = Path(repo_dir) / 'cldf' # 1. Load languages: ID → ISO code lang_map = {} # Language_ID → ISO lang_names = {} # Language_ID → name with open(cldf_dir / 'languages.csv', encoding='utf-8') as f: for row in csv.DictReader(f): lid = row['ID'] iso = row.get('ISO639P3code', '') name = row.get('Name', '') lang_map[lid] = iso if iso else lid # fall back to internal ID lang_names[lid] = name # 2. Load forms: Form_ID → metadata forms = {} # Form_ID → dict with open(cldf_dir / 'forms.csv', encoding='utf-8') as f: for row in csv.DictReader(f): fid = row['ID'] lid = row['Language_ID'] iso = lang_map.get(lid, lid) # Word = original orthographic form (Value column) word = row.get('Value', '') or row.get('Form', '') # IPA resolution priority: # 1. phon_form (phonetic transcription, e.g. IE-CoR) # 2. Phonemic (phonemic transcription, e.g. IE-CoR) # 3. Form (CLDF normalized form — used when Form ≠ Value, # which indicates phonological encoding, e.g. Kitchen Semitic) # 4. Value as fallback (if nothing else available) phon = row.get('phon_form', '').strip() phonemic = row.get('Phonemic', '').strip() form_val = row.get('Form', '').strip() value_val = row.get('Value', '').strip() if phon: ipa = phon elif phonemic: ipa = phonemic elif form_val and form_val != value_val: # Form differs from Value → likely a phonological encoding ipa = form_val else: # Form == Value: use it but flag that IPA may be orthographic ipa = form_val if form_val else value_val concept = row.get('Parameter_ID', '') forms[fid] = { 'iso': iso, 'word': word, 'ipa': ipa, 'concept': concept, 'lang_id': lid, } # 3. Load cognates: group forms by cognate set cogsets = defaultdict(list) # Cognateset_ID → [(Form_ID, doubt)] with open(cldf_dir / 'cognates.csv', encoding='utf-8') as f: for row in csv.DictReader(f): fid = row['Form_ID'] csid = row['Cognateset_ID'] doubt = row.get('Doubt', 'false') if fid in forms: cogsets[csid].append((fid, doubt)) # 4. Generate pairwise cognate pairs from cognate sets pairs = [] seen = set() for csid, members in cogsets.items(): if len(members) < 2: continue for (fid_a, doubt_a), (fid_b, doubt_b) in combinations(members, 2): fa = forms[fid_a] fb = forms[fid_b] # Skip pairs from the same language if fa['iso'] == fb['iso']: continue # Skip if missing IPA if not fa['ipa'] or not fb['ipa']: continue # Canonical ordering (alphabetic by ISO) if fa['iso'] > fb['iso']: fa, fb = fb, fa fid_a, fid_b = fid_b, fid_a doubt_a, doubt_b = doubt_b, doubt_a # Dedup key key = (fa['iso'], fb['iso'], fa['concept']) if key in seen: continue seen.add(key) # SCA encoding and scoring sca_a = ipa_to_sca(fa['ipa']) sca_b = ipa_to_sca(fb['ipa']) score = sca_distance(sca_a, sca_b) # Confidence: "certain" if neither is doubtful if doubt_a == 'true' or doubt_b == 'true': confidence = 'doubtful' else: confidence = 'certain' pairs.append({ 'Lang_A': fa['iso'], 'Word_A': fa['word'], 'IPA_A': fa['ipa'], 'Lang_B': fb['iso'], 'Word_B': fb['word'], 'IPA_B': fb['ipa'], 'Concept_ID': fa['concept'], 'Relationship': 'expert_cognate', 'Score': str(score), 'Source': source_name, 'Relation_Detail': f'cognateset_{csid}', 'Donor_Language': '-', 'Confidence': confidence, 'Source_Record_ID': f'{source_name}:{csid}:{fid_a}+{fid_b}', }) return pairs def write_staging_tsv(pairs, output_path): """Write pairs to 14-column TSV staging file.""" COLUMNS = [ 'Lang_A', 'Word_A', 'IPA_A', 'Lang_B', 'Word_B', 'IPA_B', 'Concept_ID', 'Relationship', 'Score', 'Source', 'Relation_Detail', 'Donor_Language', 'Confidence', 'Source_Record_ID', ] with open(output_path, 'w', encoding='utf-8', newline='') as f: writer = csv.DictWriter(f, fieldnames=COLUMNS, delimiter='\t', extrasaction='ignore') writer.writeheader() for pair in pairs: writer.writerow(pair) print(f' Wrote {len(pairs):,} pairs to {output_path}') def main(): base = Path(__file__).parent.parent / 'sources_tier1' staging = Path(__file__).parent.parent / 'staging_tier1' staging.mkdir(exist_ok=True) # NOTE: kitchensemitic EXCLUDED — license is CC-BY-NC-4.0, incompatible # with our dataset's CC-BY-SA-4.0 license. Flagged by adversarial audit. sources = [ ('iecor', 'iecor'), # ('kitchensemitic', 'kitchensemitic'), # EXCLUDED: CC-BY-NC-4.0 ('robbeetstriangulation', 'robbeetstriangulation'), ('savelyevturkic', 'savelyevturkic'), ] all_pairs = [] for repo_name, source_name in sources: repo_dir = base / repo_name if not repo_dir.exists(): print(f'SKIP: {repo_dir} not found') continue print(f'\nExtracting from {repo_name}...') pairs = load_cldf_source(str(repo_dir), source_name) # Write per-source staging file write_staging_tsv(pairs, staging / f'cognate_pairs_{source_name}.tsv') # Stats langs = set() for p in pairs: langs.add(p['Lang_A']) langs.add(p['Lang_B']) certain = sum(1 for p in pairs if p['Confidence'] == 'certain') doubtful = sum(1 for p in pairs if p['Confidence'] == 'doubtful') print(f' Languages: {len(langs)}') print(f' Certain: {certain:,}, Doubtful: {doubtful:,}') all_pairs.extend(pairs) # Write combined staging file write_staging_tsv(all_pairs, staging / 'cognate_pairs_tier1_combined.tsv') # Summary all_langs = set() for p in all_pairs: all_langs.add(p['Lang_A']) all_langs.add(p['Lang_B']) print(f'\n=== TOTAL ===') print(f'Total pairs: {len(all_pairs):,}') print(f'Total languages: {len(all_langs)}') print(f'Certain: {sum(1 for p in all_pairs if p["Confidence"] == "certain"):,}') print(f'Doubtful: {sum(1 for p in all_pairs if p["Confidence"] == "doubtful"):,}') if __name__ == '__main__': main()