Paper: Using Lexical Dependency And Ontological Knowledge To Improve A Detailed Syntactic And Semantic Tagger Of English

ACL ID P06-2028
Title Using Lexical Dependency And Ontological Knowledge To Improve A Detailed Syntactic And Semantic Tagger Of English
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

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 im- provements in overall system accuracy: dependency knowledge improved perfor- manceoverallclassesofword,andknowl- edge 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.