Designing HMM-based part-of-speech tagger for Lithuanian language

Abstract

This paper describes a preliminary experiment in designing a Hidden Markov Model (HMM)-based part-of-speech tagger for the Lithuanian language. Part-of-speech tagging is the problem of assigning to each word of a text the proper tag in its context of appearance. It is accomplished in two basic steps: morphological analysis and disambiguation. In this paper, we focus on the problem of disambiguation, i.e., on the problem of choosing the correct tag for each word in the context of a set of possible tags. We constructed a stochastic disambiguation algorithm, based on supervised learning techniques, to learn hidden Markov model's parameters from hand-annotated corpora. The Viterbi algorithm is used to assign the most probable tag to each word in the text.