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6d2b534 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | #!/usr/bin/env python3
"""Extract Sino-Tibetan cognate pairs from sinotibetan_dump.tsv.
Fixes: filters out entries with BORROWING flag, uses IPA column (not concept).
Output: staging/cognate_pairs/sinotibetan_cognate_pairs.tsv (14-column schema)
"""
from __future__ import annotations
import csv
import io
import sys
from collections import defaultdict
from itertools import combinations
from pathlib import Path
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8")
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT / "cognate_pipeline" / "src"))
sys.path.insert(0, str(ROOT / "scripts"))
from cognate_pipeline.normalise.sound_class import ipa_to_sound_class # noqa: E402
SOURCE_FILE = ROOT / "sources" / "sinotibetan" / "sinotibetan_dump.tsv"
STAGING_DIR = ROOT / "staging" / "cognate_pairs"
STAGING_DIR.mkdir(parents=True, exist_ok=True)
HEADER = (
"Lang_A\tWord_A\tIPA_A\tLang_B\tWord_B\tIPA_B\tConcept_ID\t"
"Relationship\tScore\tSource\tRelation_Detail\tDonor_Language\t"
"Confidence\tSource_Record_ID\n"
)
# Map doculect names to ISO 639-3 codes
DOCULECT_MAP = {
"Old_Chinese": "och",
"Japhug": "jya",
"Tibetan_Written": "bod",
"Old_Burmese": "obr",
"Jingpho": "kac",
"Lisu": "lis",
"Naxi": "nxq",
"Khaling": "klr",
"Limbu": "lif",
"Pumi_Lanping": "pmi",
"Qiang_Mawo": "qxs",
"Tujia": "tji",
"Dulong": "duu",
"Hakha": "cnh",
"Bai_Jianchuan": "bca",
}
def sca_similarity(ipa_a: str, ipa_b: str) -> float:
"""Compute normalised Levenshtein similarity on SCA strings."""
try:
sca_a = ipa_to_sound_class(ipa_a)
sca_b = ipa_to_sound_class(ipa_b)
except Exception:
return 0.0
if not sca_a or not sca_b:
return 0.0
m, n = len(sca_a), len(sca_b)
if m == 0 or n == 0:
return 0.0
dp = list(range(n + 1))
for i in range(1, m + 1):
prev = dp[0]
dp[0] = i
for j in range(1, n + 1):
temp = dp[j]
if sca_a[i - 1] == sca_b[j - 1]:
dp[j] = prev
else:
dp[j] = 1 + min(prev, dp[j], dp[j - 1])
prev = temp
dist = dp[n]
return round(1.0 - dist / max(m, n), 4)
def main():
print("=" * 60)
print("Sino-Tibetan Cognate Extraction v2")
print("=" * 60)
if not SOURCE_FILE.exists():
print(f"ERROR: Source file not found: {SOURCE_FILE}")
sys.exit(1)
# Read source TSV
cogsets: dict[str, list[dict]] = defaultdict(list)
total_rows = 0
skipped_borrowing = 0
skipped_no_cogid = 0
skipped_unknown_doculect = 0
with open(SOURCE_FILE, "r", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for row in reader:
total_rows += 1
doculect = row.get("DOCULECT", "").strip()
iso = DOCULECT_MAP.get(doculect, "")
if not iso:
skipped_unknown_doculect += 1
continue
# Filter borrowings
borrowing = row.get("BORROWING", "").strip()
if borrowing:
skipped_borrowing += 1
continue
cogid = row.get("COGID", "").strip()
if not cogid:
skipped_no_cogid += 1
continue
ipa = row.get("IPA", "").strip()
concept = row.get("CONCEPT", "").strip()
if not ipa:
continue
cogsets[f"st_{cogid}"].append({
"iso": iso,
"word": ipa, # Use IPA as word (no orthographic form available)
"ipa": ipa,
"concept": concept,
})
print(f" Total rows: {total_rows}")
print(f" Skipped (borrowing): {skipped_borrowing}")
print(f" Skipped (no COGID): {skipped_no_cogid}")
print(f" Skipped (unknown doculect): {skipped_unknown_doculect}")
print(f" Cognate sets: {len(cogsets)}")
# Generate cross-language pairs
output_path = STAGING_DIR / "sinotibetan_cognate_pairs.tsv"
pair_count = 0
with open(output_path, "w", encoding="utf-8") as out:
out.write(HEADER)
for cogset_id, members in cogsets.items():
for a, b in combinations(members, 2):
if a["iso"] == b["iso"]:
continue
score = sca_similarity(a["ipa"], b["ipa"])
out.write(
f"{a['iso']}\t{a['word']}\t{a['ipa']}\t"
f"{b['iso']}\t{b['word']}\t{b['ipa']}\t"
f"{a['concept']}\texpert_cognate\t{score}\tsinotibetan\t"
f"inherited\t-\tcertain\t{cogset_id}\n"
)
pair_count += 1
print(f"\n Total pairs: {pair_count:,}")
print(f" Output: {output_path}")
print("=" * 60)
if __name__ == "__main__":
main()
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