Evaluation of a Generic Lexical Semantic Resource in Information Extraction

Abstract

We have created an information extraction system that allows users to train the system on a domain of interest. The system helps to maximize the effect of user training by applying WordNet to rule generation and validation. The results show that, with careful control, WordNet is helpful in generating useful rules to cover more instances and hence improve the overall performance. This is particularly true when the training set is small, where F-measure is increased from 65% to 72%. However, the impact of WordNet diminishes as the size of training data increases. This paper describes our experience in applying WordNet to this system and gives an evaluation of such an effort.

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