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


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.

DOI: 10.1017/S135132490500402X

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@article{Palmer2007MakingFA, title={Making fine-grained and coarse-grained sense distinctions, both manually and automatically}, author={Martha Palmer and Hoa Trang Dang and Christiane Fellbaum}, journal={Natural Language Engineering}, year={2007}, volume={13}, pages={137-163} }