Definition Extraction Using a Sequential Combination of Baseline Grammars and Machine Learning Classifiers

Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008)

Abstract

The paper deals with the task of definition extraction from a small and noisy corpus of instructive texts. Three approaches are presented: Partial Parsing, Machine Learning and a sequential combination of both. We show that applying ML methods with the support of a trivial grammar gives results better than a relatively complicated partial grammar, and much better than pure ML approach.