@inproceedings{3d335a9c9a8842d28bb0dfa847a6692c,
title = "Improving dependency parsing on clinical text with syntactic clusters from web text",
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.",
author = "Xiuming Qiao and Hailong Cao and Tiejun Zhao and Kehai Chen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46687-3\_52",
language = "英语",
isbn = "9783319466866",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "470--478",
editor = "Kenji Doya and Kazushi Ikeda and Minho Lee and Akira Hirose and Seiichi Ozawa and Derong Liu",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "德国",
}