Bayesian nets in syntactic categorization of novel words

Authors:
Leonid Peshkin;Avi Pfeffer;Virginia Savova
Affiliations:
Harvard University, Cambridge, MA;Harvard University, Cambridge, MA;Johns Hopkins University, Cambridge, MA
Venue:
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Year:
2003

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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.