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Supervised word sense disambiguation (WSD) for truly polysemous words (in
contrast to homonyms) is difficult for machine learning, mainly due to two
problems: the lack of sense-tagged training data and the sparsity of the matrix
of observed instances vs. features. At the same time, high accuracy is necessary
for WSD to be beneficial for high-level applications, such as information
retrieval, question answering, and machine translation. This work addresses
the above two problems through combining rich linguistic knowledge
and machine learning methods. First, it proposes and demonstrates...