Getting Serious about Word Sense Disambiguation
Hwee Tou Ng · 1997
Recent advances in large-scale, broad coverage part-of-speech tagging and syntactic parsing have been achieved in no small part due to the availability of large amounts of online, human-annotated corpora. In this paper, I argue that a large, human sensetagged corpus is also critical as well as necessary to achieve broad coverage, high accuracy word sense disambiguation, where the sense distinction is at the level of a good desk-top dictionary such as WORD- NET. Using the sense-tagged corpus of 192,800 word occurrences reported in (Ng and Lee, 1996), I examine the effect of the number of training examples on the accuracy of an exemplar-based classifier versus the base-line, most-frequent-sense classio tier. I also estimate the amount of hu- man sense-tagged corpus and the manual annotation effort needed to build a largescale, broad coverage word sense disambiguation program which can significantly outperform the most-frequent-sense classifier.