FastSense: An Efficient Word Sense Disambiguation Classifier

Abstract

The task of Word Sense Disambiguation (WSD) is to determine the meaning of an ambiguous word in a given context. In spite of its importance for most NLP pipelines, WSD can still be seen to be unsolved. The reason is that we currently lack tools for WSD that handle big data – “big” in terms of the number of ambiguous words and in terms of the overall number of senses to be distinguished. This desideratum is exactly the objective of fastSense, an efficient neural network-based tool for word sense disambiguation introduced in this paper. We train and test fastSense by means of the disambiguation pages of the German Wikipedia. In addition, we evaluate fastSense in the context of Senseval and SemEval. By reference to Senseval and SemEval we additionally perform a parameter study. We show that fastSense can process huge amounts of data quickly and also surpasses state-of-the-art tools in terms of F-measure.

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