A Quantitative Assessment of SENSATIONAL with an Exploration of Its Applications

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms, support vector machines (SVM) and K means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K means by 2% and 17% respectively. In addition, we develop a polysemy based search engine and an experimental visualization application that utilizes SENSATIONAL clustering technique.

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