Abstract
Treebanks for clinical text are not enough for supervised dependency parsing no matter in their scale or diversity, leading to still unsatisfactory performance. Many unlabeled text from web can make up for the scarceness of treebanks in some extent. In this paper, we propose to gain syntactic knowledge from web text as syntactic cluster features to improve dependency parsing on clinical text. We parse the web text and compute the distributed representation of each words base on their contexts in dependency trees. Then we cluster words according to their distributed representation, and use these syntactic cluster features to solve the data sparseness problem. Experiments on Genia show that syntactic cluster features improve the LAS (Labled Attachment Score) of dependency parser on clinical text by 1.62 %. And when we use syntactic clusters combining with brown clusters, the performance gains by 1.93 % on LAS.
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Acknowledgments
This work is supported by the by the project of National Natural Science Foundation of China (No. 91520204, No. 61572154) and the project of National High Technology Research and Development Program of China (863 Program) (No. 2015AA015405).
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Qiao, X., Cao, H., Zhao, T., Chen, K. (2016). Improving Dependency Parsing on Clinical Text with Syntactic Clusters from Web Text. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_52
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