Statistical language models of Lithuanian based on word clustering and morphological decomposition

Abstract

This paper describes our research on statistical language modeling of Lithuanian. The idea of improving sparse n-gram models of highly inflected Lithuanian language by interpolating them with complex n-gram models based on word clustering and morphological word decomposition was investigated. Words, word base forms and part-of-speech tags were clustered into 50 to 5000 automatically generated classes. Multiple 3-gram and 4-gram class-based language models were built and evaluated on Lithuanian text corpus, which contained 85 million words. Class-based models linearly interpolated with the 3-gram model led up to a 13% reduction in the perplexity compared with the baseline 3-gram model. Morphological models decreased out-of-vocabulary word rate from 1.5% to 1.02%.