Will Very Large Corpora Play For Semantic Disambiguation The Role That Massive Computing Power Is Playing For Other AI-Hard Problems?

Proceedings of the Second International Conference on Language Resources and Evaluation (LREC 2000)

Abstract

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.