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Semantic Pattern Learning Through Maximum Entropy-based WSD technique

Maximiliano Saiz-Noeda, Armando Suárez and Manuel Palomar

This paper describes a Natural Language Learning method that extracts knowledge in the form of semantic patterns with ontology elements associated to syntactic components in the text. The method combines the use of EuroWordNet's ontological concepts and the correct sense of each word assigned by a Word Sense Disambiguation (WSD) module to extract three sets of patterns: subject-verb, verb-direct object and verb-indirect object. These sets define the semantic behaviour of the main textual elements based on their syntactic role. On the one hand, it is shown that Maximum Entropy models applied to WSD tasks provide good results. The evaluation of the WSD module has revealed a accuracy rate of 64% in a preliminary test. On the other hand, we explain how an adequate set of semantic or ontological patterns can improve the success rate of NLP tasks such us pronoun resolution. We have implemented both modules in C++ and although the evaluation has been performed for English, their general features allow the treatment of other languages like Spanish.

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Last update: July 12, 2001.