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Making fine-grained and coarse-grained sense distinctions, both manually and automatically

Published online by Cambridge University Press:  12 July 2006

MARTHA PALMER ,
HOA TRANG DANG  and
CHRISTIANE FELLBAUM
MARTHA PALMER
Affiliation:
Department of Linguistics, University of Colorado, CO, USA e-mail: mpalmer@colorado.edu
HOA TRANG DANG
Affiliation:
National Institute of Standards and Technology, Gaithersburg, MD, USA e-mail: hoa.dang@nist.gov
CHRISTIANE FELLBAUM
Affiliation:
Princeton University, NJ, USA e-mail: fellbaum@clarity.princeton.edu
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Abstract

In this paper we discuss a persistent problem arising from polysemy: namely the difficulty of finding consistent criteria for making fine-grained sense distinctions, either manually or automatically. We investigate sources of human annotator disagreements stemming from the tagging for the English Verb Lexical Sample Task in the SENSEVAL-2 exercise in automatic Word Sense Disambiguation. We also examine errors made by a high-performing maximum entropy Word Sense Disambiguation system we developed. Both sets of errors are at least partially reconciled by a more coarse-grained view of the senses, and we present the groupings we use for quantitative coarse-grained evaluation as well as the process by which they were created. We compare the system's performance with our human annotator performance in light of both fine-grained and coarse-grained sense distinctions and show that well-defined sense groups can be of value in improving word sense disambiguation by both humans and machines.

Type
Papers
Information
Natural Language Engineering , Volume 13 , Issue 2 , June 2007 , pp. 137 - 163
Copyright
2006 Cambridge University Press

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