mySENSEVAL: explaining WSD system performance using target word features

Authors:
Harri M. T. Saarikoski
Affiliations:
Department of General Linguistics, KIT Language Technology Doctorate School, Helsinki University, Helsinki, Finland
Venue:
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Year:
2005

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Abstract

Word sense disambiguation (WSD) is an unsolved problem in NLP. The field has produced a variety of methods but none of them potent enough to reach high, human-tagger accuracy in demanding NLP applications. Our contribution to WSD is mySENSEVAL, an error analyzer using SENSEVAL evaluation scores (in mySQL database) to find significant correlations between WSD system types and lexico-conceptual features (from WordNet and SUMO).