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003 — Cognate Pairs v2 Rebuild

Date: 2026-03-13 Scope: Complete rebuild of all cognate pair files, fixing 6 critical pipeline bugs

1. Objective

The original cognate pair extraction pipeline (v1) had 6 critical bugs that produced incorrect or incomplete data. This entry documents the complete rebuild from scratch using corrected v2 extraction scripts.

2. Bugs Fixed

# Bug Impact Root Cause
1 ABVD never read cognates.csv 291K expert cognate judgements missing Script read forms.csv:Cognacy column instead of the dedicated cognates.csv CognateTable
2 Multi-set cognacy truncation 37K lost set memberships Cognacy field like "1,2" was truncated to "1" instead of splitting
3 WOLD fabricated borrowing pairs False borrowing relationships Script generated pairs from concept co-occurrence instead of reading borrowings.csv donor-recipient table
4 Concept-aligned pairs mislabeled as inherited Inflated inherited count Pairs from concept alignment (no expert cognacy) were labeled expert_cognate
5 Sino-Tibetan Word field = Concept string Wrong word forms Word_A/Word_B contained concept labels, not actual word forms
6 Alphabetical 50-entry hard truncation Large families truncated Families with >50 entries in a concept set had entries past the 50th alphabetically discarded

3. Scripts Used

Script Purpose
scripts/extract_abvd_cognates_v2.py Read authoritative cognates.csv (291K entries), handle multi-set membership, include Doubt column
scripts/extract_wold_borrowings_v2.py Read authoritative borrowings.csv (21K donor-recipient events), extract Target_Form_ID + Source_Form_ID
scripts/extract_sinotibetan_cognates_v2.py Read sinotibetan_dump.tsv (6,159 entries with COGID), separate inherited vs borrowing by BORROWING column
scripts/extract_iecor_cognates.py Process IE-CoR CLDF CognateTable
scripts/extract_acd_cognates.py Process Austronesian Comparative Dictionary
scripts/merge_cognate_pairs.py Deduplicate across sources with priority ordering, produce 3 output files

4. Data Sources

Same 6 CLDF sources as 001, plus IE-CoR and ACD from 002.

5. Source Reputability

  • ABVD cognates.csv: 291,000 expert cognate judgements made by field linguists over 20+ years. Each entry has a Doubt column (certain/doubtful). This is the gold standard for Austronesian cognacy.
  • WOLD borrowings.csv: 21,000+ explicit donor→recipient borrowing events curated by 41 specialist authors. Each has a certainty score (1-5 scale).
  • IE-CoR: Expert cognate sets for Indo-European, curated by computational historical linguists at MPI-EVA.
  • All entries have Source_Record_ID for traceability back to original CLDF records.

6. Methodology

14-Column Output Schema 

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

Scoring

  • SCA-weighted Levenshtein: Normalized edit distance on Sound Class Alphabet (List 2012) encodings
  • Substitution cost: 0 for same class, 0.5 for similar classes (e.g., voiced↔voiceless stop), 1.0 for different classes
  • Gap penalty: 0.5
  • Score = 1.0 − (normalized_distance), range [0.0, 1.0], rounded to 4 decimal places

Deduplication Priority

When the same (Lang_A, Lang_B, Concept_ID) tuple appears in multiple sources:

  1. expert_cognate (priority 0) — keeps expert-labelled pair
  2. borrowing (priority 1)
  3. concept_aligned (priority 2)
  4. similarity_only (priority 3) — lowest priority, only kept if no better evidence

Pair key is order-independent: min(side_a, side_b) || max(side_a, side_b) || concept

ABVD v2 Extraction (Bug #1 + #2 fix)

  • Reads cognates.csv (CLDF CognateTable), not forms.csv:Cognacy
  • Splits multi-set membership: "1,2" → entries in both cognateset 1 and 2
  • Preserves Doubt column as Confidence field

WOLD v2 Extraction (Bug #3 fix)

  • Reads borrowings.csv directly: each row = one donor→recipient event
  • Extracts Target_Form_ID and Source_Form_ID, resolves to language/word/IPA
  • No fabricated pairs — only explicit borrowing relationships

7. Tests Performed

  • All 406 existing tests pass after rebuild
  • test_full_pipeline.py: End-to-end Ugaritic-Hebrew cognate detection verified
  • test_training_data.py: Schema validation on all 3 output files
  • Row count verification against source repos:
    • ABVD: wc -l cognates.csv matches expected extraction count
    • WOLD: wc -l borrowings.csv matches expected extraction count
  • Deduplication audit: Verified no duplicate (Lang_A, Lang_B, Concept_ID) tuples in output

8. Cross-Referencing

  • 20 random inherited pairs traced back to ABVD cognates.csv by Source_Record_ID
  • 10 random borrowing pairs traced back to WOLD borrowings.csv by Source_Record_ID
  • Verified WOLD borrowing pairs have correct Donor_Language by cross-referencing with source
  • Verified Sino-Tibetan Word fields now contain actual word forms (not concept labels)

9. Output Summary

File Rows Size Change from v1
cognate_pairs_inherited.tsv 22.9M 2.2 GB +291K expert cognates recovered
cognate_pairs_borrowing.tsv 17K 1.9 MB Completely rebuilt (v1 was fabricated)
cognate_pairs_similarity.tsv 465K 49.9 MB Relabeled (was falsely inherited in v1)

10. PRD Reference

Full specification: docs/prd/PRD_COGNATE_PAIRS_V2.md