Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46687-3_52 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Cluster (physics),S-attributed grammar,Pattern recognition,Information retrieval,Computer science,Bottom-up parsing,Dependency grammar,Natural language processing,Artificial intelligence,Parsing,Syntax,Distributed representation | Conference | 9947 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuming Qiao | 1 | 0 | 0.34 |
| Hailong Cao | 2 | 14 | 5.64 |
| Tiejun Zhao | 3 | 643 | 102.68 |
| Kehai Chen | 4 | 43 | 16.34 |