Abstract
This chapter 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 on the Brown corpus to show the portability of our approach in another domain.
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Acknowledgements
This research was partially supported by Grant-in-Aid for Specially Promoted Research 18002007.
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Hara, T., Miyao, Y., Tsujii, Ji. (2010). Evaluating the Impact of Re-training a Lexical Disambiguation Model on Domain Adaptation of an HPSG Parser. In: Bunt, H., Merlo, P., Nivre, J. (eds) Trends in Parsing Technology. Text, Speech and Language Technology, vol 43. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9352-3_15
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