docs/changelog/001_initial_dataset.md

001 — Initial Dataset Creation

Date: 2026-03-08 Scope: Full dataset construction from scratch

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1. Objective

Create a comprehensive multilingual lexicon and cognate pair dataset for training phonetic/linguistic models, covering 1,000+ languages with IPA transcriptions, sound class encodings, and cognate relationships.

2. Scripts Used

Script	Purpose
cognate_pipeline/src/cognate_pipeline/ingest/cldf_ingester.py	Ingest CLDF-formatted source repos (languages.csv, forms.csv, cognates.csv, borrowings.csv)
cognate_pipeline/src/cognate_pipeline/ingest/csv_ingester.py	Ingest CSV/TSV/.cog format files
cognate_pipeline/src/cognate_pipeline/ingest/wiktionary_ingester.py	Ingest Wiktionary JSONL pronunciation data
cognate_pipeline/src/cognate_pipeline/normalise/ipa_normaliser.py	Normalize IPA transcriptions (NFC, strip brackets, remove stress)
cognate_pipeline/src/cognate_pipeline/normalise/sound_class.py	Encode IPA → SCA (Sound Correspondence Alphabet, List 2012)
cognate_pipeline/src/cognate_pipeline/normalise/epitran_backend.py	Grapheme-to-phoneme conversion via Epitran
cognate_pipeline/src/cognate_pipeline/cognate/candidate_gen.py	Generate pairwise candidates within concept sets
cognate_pipeline/src/cognate_pipeline/cognate/baseline_levenshtein.py	SCA-weighted Levenshtein distance scoring
cognate_pipeline/src/cognate_pipeline/cognate/clustering.py	Union-Find connected components + UPGMA clustering
cognate_pipeline/src/cognate_pipeline/export/cldf_exporter.py	Export to CLDF Wordlist format

3. Data Sources

Source	Repository	License	Description
NorthEurAlex	https://github.com/lexibank/northeuralex	CC BY 4.0	Northern Eurasian lexical database, 1,016 concepts × 107 languages
IDS	https://github.com/lexibank/ids	CC BY 4.0	Intercontinental Dictionary Series, 1,310 concepts × 329 languages
ABVD	https://github.com/lexibank/abvd	CC BY 4.0	Austronesian Basic Vocabulary Database, 210 concepts × 1,380+ languages
WOLD	https://github.com/lexibank/wold	CC BY 4.0	World Loanword Database, 41 donor languages, 21K+ borrowing events
Sino-Tibetan	https://github.com/lexibank/sinotibetan	CC BY 4.0	Sino-Tibetan cognate database, 6,159 entries with COGIDs
WikiPron	https://github.com/CUNY-CL/wikipron	Apache 2.0	IPA pronunciations mined from Wiktionary

4. Source Reputability

• CLDF repositories (lexibank): Published by the Max Planck Institute for Evolutionary Anthropology. Cross-Linguistic Data Formats (CLDF) is the de facto standard for computational historical linguistics (Forkel et al. 2018, Scientific Data).
• NorthEurAlex: Curated by Dellert et al. (2020), University of Tübingen. Peer-reviewed in Language Resources and Evaluation.
• ABVD: Greenhill, Blust & Gray (2008). Maintained by ANU. 291K expert cognate judgements from field linguists.
• WOLD: Haspelmath & Tadmor (2009), Oxford University Press. 21K+ borrowing events with donor language and certainty metadata.
• IDS: Mary Ritchie Key & Bernard Comrie (2015). Max Planck Institute.
• Sino-Tibetan: Sagart et al. (2019). Published in PNAS.

5. Methodology

Lexicon Construction

1. Clone all CLDF source repos to sources/
2. Ingest via cldf_ingester.py: read languages.csv for ISO mapping, forms.csv for word forms, resolve IPA from Form/Value columns
3. Normalize IPA: NFC Unicode normalization, strip [/] brackets, remove suprasegmentals
4. Encode to SCA: ipa_to_sound_class() using List (2012) sound correspondence alphabet
5. Deduplicate: per-language, per-concept, prefer IPA-bearing entries

Cognate Pair Generation (v1)

1. Group entries by Concept_ID
2. Generate all pairwise combinations within each concept set
3. Score via SCA-weighted Levenshtein: substitution cost matrix from List (2012), gap penalty = 0.5
4. Label relationship type from source metadata (expert_cognate from ABVD cognatesets, borrowing from WOLD)

Validation Set Construction

• 9 language families (Germanic, Celtic, Balto-Slavic, Indo-Iranian, Italic, Hellenic, Semitic, Turkic, Uralic)
• 40 shared concepts across 6 semantic domains (body, kinship, nature, animals, verbs, other)
• Stratified by phylogenetic distance: L1 (same sub-branch), L2 (same branch), L3 (same family), L4 (cross-family)

6. Tests Performed

• 406 automated tests (232 unit + 174 expanded validation) via pytest
• test_full_pipeline.py: End-to-end on Ugaritic-Hebrew .cog format (ingest → score → cluster)
• test_validation_sets.py: Schema validation (14 columns), phylogenetic stratification checks
• test_training_data.py: Training data consistency (no empty IPA, valid ISO codes)
• test_expanded_validation.py: Per-family branch validation (Austronesian, Balto-Slavic, Celtic, etc.)
• Per-source CLDF validation tests: sources/abvd/test.py, sources/ids/test.py, sources/wold/test.py, sources/iecor/test.py

7. Cross-Referencing

• Each entry retains Source_Record_ID traceable to original CLDF record
• IPA transcriptions spot-checked against WikiPron for 20 random languages
• SCA encoding validated against known IPA→SCA mappings from List (2012) Table 2

8. Output Summary

File	Rows	Size
data/training/lexicons/ (per-language TSVs)	~580K entries	~195 MB total
data/training/cognate_pairs/cognate_pairs_inherited.tsv	~18.2M	~1.18 GB
data/training/cognate_pairs/cognate_pairs_borrowing.tsv	~116K	~8.0 MB
data/training/cognate_pairs/cognate_pairs_similarity.tsv	~170K	~16.2 MB
data/training/metadata/languages.tsv	1,177	39 KB
data/training/validation/*.tsv	9 families + expanded	~200 MB

9. Academic References

• Forkel, R., et al. (2018). "Cross-Linguistic Data Formats." Scientific Data, 5:180205.
• List, J.-M. (2012). "SCA: Phonetic alignment based on sound classes." New Directions in Logic, Language, and Computation, Springer.
• Greenhill, S.J., Blust, R. & Gray, R.D. (2008). "The Austronesian Basic Vocabulary Database." Evolutionary Bioinformatics, 4:271-283.
• Haspelmath, M. & Tadmor, U. (2009). Loanwords in the World's Languages. De Gruyter Mouton.
• Sagart, L., et al. (2019). "Dated language phylogenies shed light on the ancestry of Sino-Tibetan." PNAS, 116(21):10317-10322.