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Stem-based PoS tagging for agglutinative languages
Bolucu, Necva Can, Burcu
Bolucu, Necva
Can, Burcu
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2017-06-29
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Sondan Eklemeli Dillerde Gövde Tabanlı Sözcük
Türü ˙I¸saretleme
Abstract
Words are made up of morphemes being glued together in agglutinative languages. This makes it difficult to perform part-of-speech tagging for these languages due to sparsity. In this paper, we present two Hidden Markov Model based Bayesian PoS tagging models for agglutinative languages. Our first model is word-based and the second model is stem-based where the stems of the words are obtained from other two unsupervised stemmers: HPS stemmer and Morfessor FlatCat. The results show that stemming improves the accuracy in PoS tagging. We present the results for Turkish as an agglutinative language and English as a morphologically poor language.
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Bölücü, N. and Can, B. (2017) Stem-based PoS tagging for agglutinative languages, 2017 25th Signal Processing and Communications Applications Conference (SIU), 15-18 May 2017, Antalya, Turkey.
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This is an accepted manuscript of an article published by IEEE in 2017 25th Signal Processing and Communications Applications Conference (SIU) on 29/06/2017, available online: https://ieeexplore.ieee.org/document/7960386
The accepted version of the publication may differ from the final published version.
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2165-0608
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9781509064946