Using lexical dependency and ontological knowledge to improve a detailed syntactic and semantic tagger of English

Authors:
Andrew Finch;Ezra Black;Young-Sook Hwang;Eiichiro Sumita
Affiliations:
NiCT-ATR, Kyoto, Japan;Epimenides Corp., New York;ETRI, Seoul, Korea;NiCT-ATR, Kyoto, Japan
Venue:
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Year:
2006

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Abstract

This paper presents a detailed study of the integration of knowledge from both dependency parses and hierarchical word ontologies into a maximum-entropy-based tagging model that simultaneously labels words with both syntax and semantics. Our findings show that information from both these sources can lead to strong improvements in overall system accuracy: dependency knowledge improved performance over all classes of word, and knowledge of the position of a word in an on-tological hierarchy increased accuracy for words not seen in the training data. The resulting tagger offers the highest reported tagging accuracy on this tagset to date.