Abstract
This paper proposes using linguistic knowledge from Wiktionary to improve lexical disambiguation in multiple languages, focusing on part-of-speech tagging in selected languages with various characteristics including English, Vietnamese, and Korean. Dictionaries and subsumption networks are first automatically extracted from Wiktionary. These linguistic resources are then used to enrich the feature set of training examples. A first-order discriminative model is learned on training data using Hidden Markov-Support Vector Machines. The proposed method is competitive with related contemporary works in the three languages. In English, our tagger achieves 96.37% token accuracy on the Brown corpus, with an error reduction of 2.74% over the baseline.
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Nguyen, KH., Ock, CY. (2012). Using Wiktionary to Improve Lexical Disambiguation in Multiple Languages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_20
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DOI: https://doi.org/10.1007/978-3-642-28604-9_20
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