Abstract
Word Sense Induction (WSI) is the task of identifying the different uses (senses) of a target word in a given text in an unsupervised manner, i.e. without relying on any external resources such as dictionaries or sense-tagged data. This paper presents a thorough description of the SemEval-2010 WSI task and a new evaluation setting for sense induction methods. Our contributions are two-fold: firstly, we provide a detailed analysis of the Semeval-2010 WSI task evaluation results and identify the shortcomings of current evaluation measures. Secondly, we present a new evaluation setting by assessing participating systems' performance according to the skewness of target words' distribution of senses showing that there are methods able to perform well above the Most Frequent Sense (MFS) baseline in highly skewed distributions.
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