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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bansal, M., Gimpel, K., Livescu, K.: Tailoring continuous word representations for dependency parsing. In: Proceedings of the ACL 2014, pp. 809–815 (2014)
Dredze, M., Blitzer, J., Talukdar, P.P., Ganchev, K., Graca, J.a., Pereira, F.: Frustratingly hard domain adaptation for dependency parsing. In: Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL 2007, pp. 1051–1055 (2007)
Firth, J.: A Synopsis of Linguistic Theory 1930–1955. Studies in Linguistic Analysis, pp. 1–32 (1957)
Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)
Hogenhout, W.R., Matsumoto, Y., Fast, J.S.W.: A preliminary study of word clustering based on syntactic behavior. In: Proceedings of the Computational Natural Language Learning, pp. 16–24 (1997)
Kim, J.D., Ohta, T., Tateisi, Y., Tsujii, J.: Genia corpus-semantically annotated corpus for bio-textmining. Bioinformatics 19(Suppl 1), i180–i182 (2003)
Koo, T., Carreras, X., Collins, M.: Simple semi-supervised dependency parsing. In: Proceedings of ACL-08: HLT, pp. 595–603 (2008)
Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 302–308 (2014)
Ma, X., Xia, F.: Dependency parser adaptation with subtrees from auto-parsed target domain data. In: Proceedings of the 51st Annual Meeting of the ACL, pp. 585–590 (2013)
McClosky, D., Charniak, E.: Self-training for biomedical parsing. In: Proceedings of ACL-08: HLT, Short Papers, pp. 101–104 (2008)
McDonald, R., Crammer, K., Pereira, F.: Online large-margin training of dependency parsers. In: Proceedings of the 43rd Annual Meeting on ACL 2005, pp. 91–98 (2005)
Mcdonald, R., Pereira, F.: Online learning of approximate dependency parsing algorithms. In: Proceedings of EACL, pp. 81–88 (2006)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. CoRR abs/1309.4168 (2013)
Ng, D., Bansal, M., Curran, J.R.: Web-scale surface and syntactic n-gram features for dependency parsing. CoRR abs/1502.07038 (2015)
Nivre, J., Hall, J., Nilsson, J., Chanev, A., Eryiğit, G., Kübler, S., Marinov, S., Marsi, E.: MaltParser: a language-independent system for data-driven dependency parsing. Nat. Lang. Eng. 13(2), 95–135 (2007)
Plank, B., van Noord, G.: Effective measures of domain similarity for parsing. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 1566–1576 (2011)
Sagae, K., Gordon, A.S.: Clustering words by syntactic similarity improves dependency parsing of predicate-argument structures. In: Proceedings of the 11th International Conference on Parsing Technologies, pp. 192–201 (2009)
Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web WWW 2010, pp. 1177–1178. ACM (2010)
Zhang, C., Zhao, T.: Bilingual lexicon extraction with forced correlation from comparable corpora. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9490, pp. 528–535. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26535-3_60
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-46687-3_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46686-6
Online ISBN: 978-3-319-46687-3
eBook Packages: Computer ScienceComputer Science (R0)