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Will Very Large Corpora Play For Semantic Disambiguation The Role That Massive Computing Power Is Playing For Other AI-Hard Problems?
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Will Very Large Corpora Play For Semantic Disambiguation The Role That Massive Computing Power Is Playing For Other AI-Hard Problems?
In this paper we formally analyze the relation between the amount of (possibly noisy) examples provided to a word-sense classification algorithm and the performance of the classifier. In the first part of the paper, we show that Computational Learning Theory provides a suitable theoretical framework to establish one such relation. In the second part of the paper, we will apply our theoretical results to the case of a semantic disambiguation algorithm based on syntactic similarity.
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