Evaluating impact of re-training a lexical disambiguation model on domain adaptation of an HPSG parser

Abstract

This paper describes an effective approach to adapting an HPSG parser trained on the Penn Treebank to a biomedical domain. In this approach, we train probabilities of lexical entry assignments to words in a target domain and then incorporate them into the original parser. Experimental results show that this method can obtain higher parsing accuracy than previous work on domain adaptation for parsing the same data. Moreover, the results show that the combination of the proposed method and the existing method achieves parsing accuracy that is as high as that of an HPSG parser retrained from scratch, but with much lower training cost. We also evaluated our method in the Brown corpus to show the portability of our approach in another domain.