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Phonological Fossils: Machine Learning Detection of Non-Mainstream Vocabulary in Sulawesi Basic Lexicon

Mukhlis Amien, Go Frendi Gunawan

Abstract

Basic vocabulary in many Sulawesi Austronesian languages includes forms resisting reconstruction to any proto-form with phonological patterns inconsistent with inherited roots, but whether this non-conforming vocabulary represents pre-Austronesian substrate or independent innovation has not been tested computationally. We combine rule-based cognate subtraction with a machine learning classifier trained on phonological features. Using 1,357 forms from six Sulawesi languages in the Austronesian Basic Vocabulary Database, we identify 438 candidate substrate forms (26.5%) through cognate subtraction and Proto-Austronesian cross-checking. An XGBoost classifier trained on 26 phonological features distinguishes inherited from non-mainstream forms with AUC=0.763, revealing a phonological fingerprint: longer forms, more consonant clusters, higher glottal stop rates, and fewer Austronesian prefixes. Cross-method consensus (Cohen's kappa=0.61) identifies 266 high-confidence non-mainstream candidates. However, clustering yields no coherent word families (silhouette=0.114; cross-linguistic cognate test p=0.569), providing no evidence for a single pre-Austronesian language layer. Application to 16 additional languages confirms geographic patterning: Sulawesi languages show higher predicted non-mainstream rates (mean P_sub=0.606) than Western Indonesian languages (0.393). This study demonstrates that phonological machine learning can complement traditional comparative methods in detecting non-mainstream lexical layers, while cautioning against interpreting phonological non-conformity as evidence for a shared substrate language.

Phonological Fossils: Machine Learning Detection of Non-Mainstream Vocabulary in Sulawesi Basic Lexicon

Abstract

Basic vocabulary in many Sulawesi Austronesian languages includes forms resisting reconstruction to any proto-form with phonological patterns inconsistent with inherited roots, but whether this non-conforming vocabulary represents pre-Austronesian substrate or independent innovation has not been tested computationally. We combine rule-based cognate subtraction with a machine learning classifier trained on phonological features. Using 1,357 forms from six Sulawesi languages in the Austronesian Basic Vocabulary Database, we identify 438 candidate substrate forms (26.5%) through cognate subtraction and Proto-Austronesian cross-checking. An XGBoost classifier trained on 26 phonological features distinguishes inherited from non-mainstream forms with AUC=0.763, revealing a phonological fingerprint: longer forms, more consonant clusters, higher glottal stop rates, and fewer Austronesian prefixes. Cross-method consensus (Cohen's kappa=0.61) identifies 266 high-confidence non-mainstream candidates. However, clustering yields no coherent word families (silhouette=0.114; cross-linguistic cognate test p=0.569), providing no evidence for a single pre-Austronesian language layer. Application to 16 additional languages confirms geographic patterning: Sulawesi languages show higher predicted non-mainstream rates (mean P_sub=0.606) than Western Indonesian languages (0.393). This study demonstrates that phonological machine learning can complement traditional comparative methods in detecting non-mainstream lexical layers, while cautioning against interpreting phonological non-conformity as evidence for a shared substrate language.

Paper Structure

This paper contains 38 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Data and Methods
  3. Data
  4. Rule-Based Baseline (E022)
  5. ML Classification (E027)
  6. Two-model design
  7. Feature engineering
  8. Classifiers
  9. Validation
  10. Interpretability
  11. Cross-Method Consensus (E028)
  12. Phonological Clustering (E029)
  13. Distance metric
  14. Clustering algorithms
  15. Cross-linguistic cognate test
  16. ...and 23 more sections

Figures (4)

  • Figure 1: SHAP beeswarm plot for Model B (XGBoost). Each dot represents one form; color indicates feature value (red = high, blue = low). Positive SHAP values push toward substrate classification.
  • Figure 2: Four-quadrant comparison of rule-based (E022) and ML (E027 Model B) substrate predictions. CS = Consensus Substrate, CA = Consensus Austronesian, RO = Rule-Only, MO = ML-Only.
  • Figure 3: Distribution of cross-linguistic pairwise Levenshtein distances for consensus substrate forms (solid line) vs. null distribution from 1,000 random concept draws (histogram). Substrate forms are not more similar across languages than expected by chance ($p$ = 0.569).
  • Figure 4: Predicted substrate rates (ML) vs. rule-based residual rates for 22 languages grouped by geographic region. Languages are ordered by ML substrate rate within each group.