A statistical model for parsing and word-sense disambiguation

Authors:
Daniel M. Bikel
Affiliations:
University of Pennsylvania, Philadelphia, PA
Venue:
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Year:
2000

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Abstract

This paper describes a first attempt at a statistical model for simultaneous syntactic parsing and generalized word-sense disambiguation. On a new data set we have constructed for the task, while we were disappointed not to find parsing improvement over a traditional parsing model, our model achieves a recall of 84.0% and a precision of 67.3% of exact synset matches on our test corpus, where the gold standard has a reported inter-annotator agreement of 78.6%.