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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ugfrgjxxffftblvvdh5ko6pyum" style="color: black;">Natural Language Engineering</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/s135132490500402x">doi:10.1017/s135132490500402x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/acn6h2n5rfhthjspvzeu2yebtu">fatcat:acn6h2n5rfhthjspvzeu2yebtu</a> </span>
more »... . 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.
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