Integrating knowledge for subjectivity sense labeling

Authors:
Yaw Gyamfi;Janyce Wiebe;Rada Mihalcea;Cem Akkaya
Affiliations:
University of Pittsburgh;University of Pittsburgh;University of North Texas;University of Pittsburgh
Venue:
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2009

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Abstract

This paper introduces an integrative approach to automatic word sense subjectivity annotation. We use features that exploit the hierarchical structure and domain information in lexical resources such as WordNet, as well as other types of features that measure the similarity of glosses and the overlap among sets of semantically related words. Integrated in a machine learning framework, the entire set of features is found to give better results than any individual type of feature.