Minimal-Supervision Part-of-Speech Tagging for Assamese Language: An Evidence-Gated Cascade with Classifier Backfill

Abstract

Part-of-speech (POS) tagging for low-resource languages is limited more by the expense of generating labeled training data than by algorithmic constraints. This study evaluates the extent to which a length-stratified, minimally-supervised pipeline can recover tagging accuracy for the Assamese language using a labeled seed of only 3 to 5 sentences per distinct sentence length (142 to 228 sentences in total), compared to a weak-supervision classifier trained on a conventional 80% corpus split (2,840 sentences). An evidence-gated cascade is constructed, comprising Brown-style word clustering, a seed-ambiguity audit with context-based disambiguation, confidence-gated cluster labeling, and empirically validated rule fallbacks. This approach achieves 94.9% to 95.0% accuracy but only 19.8% to 20.0% token coverage from its minimal seed. To address the coverage gap, every remaining token is backfilled using a classifier trained on the pipeline’s accumulated evidence, resulting in 100% coverage at 84.7% to 86.0% accuracy (depending on seed size), as evaluated against a fixed, shared test set for direct comparison with the larger-budget baseline (90.8% accuracy, same coverage, same test set). Three negative results are reported alongside the positive findings: Brown-style clustering provides negligible benefit at this scale, Viterbi sequence decoding does not transfer to the backfill classifier from the larger-budget setting, and naive self-training fails due to confirmation bias. Additionally, through five replicated random seed draws per condition, it is demonstrated that the residual seed-size effect (3 versus 5 sentences per length, closing approximately 21% of the accuracy gap to the larger-budget baseline) is statistically significant rather than the result of a single favorable draw.