Abstract
This paper presents an application of a Dynamic Bayesian Network (DBN) to the task of assigning Part-of-Speech (PoS) tags to novel text. This task is particularly challenging for non-standard corpora, such as Internet lingo, where a large proportion of words are unknown. Previous work reveals that PoS tags depend on a variety of morphological and contextual features. Representing these dependencies in a DBN results into an elegant and effective PoS tagger.
Cite
CITATION STYLE
Peshkin, L., Pfeffer, A., & Savova, V. (2003). Bayesian nets in syntactic categorization of novel words. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics - Short Papers, HLT-NAACL 2003 (pp. 79–81). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1073483.1073510
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