Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
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This paper presents a method for classifying Japanese polysemous verbs. We used a graph-based unsupervised clustering algorithm, which detects the spin configuration that minimizes the energy of the material. Comparing global and local minima of an energy function allows for the detection of spins (nodes) with more than one cluster. We applied the algorithm to cluster polysemies. Moreover, we used link analysis to detect subcategorization frames, which are used to calculate distributional similarity between verbs. Evaluation are made on a set collected from Japanese dictionary, and the results suggest that polysemy, rather than being an obstacle to word sense discovery and identification, may actually be of benefit.