Morphology Aware Reversible Semantic Tokenization and Hierarchical Word Composition for Tamil Language Models
Statistical subword tokenizers can process arbitrary text, but their units need not align with lexical or grammatical structure. This is especially important for Tamil, where a written word may encode stem changes, case, number, tense, agreement, voice, clitics, and linked verbs. We present a Tamil morphology system extending the open-source ThamizhiMorph analyzer and generator, together with a byte-exact semantic tokenizer and a learned hierarchical word composer. Twelve finite-state transducers analyze words into lemmas and grammatical features, while character and byte fallbacks preserve exact reconstruction. We compare a flat morphology tokenizer, a signal-preserving word composer, an...
| Authors | Anand Murugan |
|---|---|
| Primary category | cs.CL |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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