Using Few Clues Can Compensate the Small Amount of Resources Available for Word Sense Disambiguation

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.