Using Few Clues Can Compensate the Small Amount of Resources Available for Word Sense Disambiguation
Claude de Loupy, Marc El-Bèze · 2000
Word Sense Disambiguation (WSD) is considered as one of the most difficult tasks in Natural Language Processing.Probabilistic methods have shown their efficiency in many NLP tasks, but they imply a training phase and very few resources are available for WSD.This paper aims at showing how to make the most of size-limited resources in order to partially overcome the knowledge acquisition bottleneck.Experiments are performed within the SENSEVAL test framework in order to evaluate the advantage of a lemmatized or stemmed context over an original context (inflected forms as they are observed in the rough text).Then, we measure the precision improvement (about 6 %) when looking at the inflected form of the word to be disambiguated.Lastly, we show that it is possible to reduce the ambiguity if the word to be disambiguated has a particular inflected form or occurs as part of a compound.