Designing HMM‐Based Part‐of‐Speech Tagger for Lithuanian Language
| Autor: | Gailius Raškinis, Jan Kuper, Vilma Griciūtė, Giedrė Pajarskaitė |
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| Rok vydání: | 2004 |
| Předmět: |
Computer science
business.industry Computer Science::Information Retrieval Applied Mathematics Speech recognition Supervised learning Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Context (language use) Lithuanian computer.software_genre Viterbi algorithm Part of speech language.human_language Set (abstract data type) symbols.namesake language symbols Artificial intelligence Hidden Markov model business computer Word (computer architecture) Natural language processing Information Systems |
| Zdroj: | Informatica. 15:231-242 |
| ISSN: | 1822-8844 0868-4952 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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