The importance of high-quality input for WSD: an application-oriented comparison of part-of-speech taggers

In this paper, we present an applicationoriented evaluation of three Part-ofSpeech (PoS) taggers in a word sense disambiguation (WSD) system. Following the intuition that high quality input is likely to influence the final results of a complex system, we test whether the more accurate taggers also produce better results when integrated into the WSD system. For this purpose, a stand-alone evaluation of the PoS taggers is used to assess which tagger is the most accurate. The results of the WSD task, computed on the training section of the Dutch Senseval-2 data, including the PoS information from all three taggers show that the most accurate PoS tags do indeed lead to the best results, thereby verifying our hypothesis. A surprising result, however, is the fact that the performance of the complex WSD system with the different PoS tags included does not necessarily reflect the stand-alone accuracy of the PoS taggers.