Name
Title | ||
|---|---|---|
Robust and Efficient Chinese Word Dependency Analysis with Linear Kernel Support Vector Machines |
Abstract | ||
|---|---|---|
Data-driven learning based on shift reduce pars- ing algorithms has emerged dependency parsing and shown excellent performance to many Tree- banks. In this paper, we investigate the extension of those methods while considerably improved the runtime and training time efficiency via L2- SVMs. We also present several properties and constraints to enhance the parser completeness in runtime. We further integrate root-level and bot- tom-level syntactic information by using sequen- tial taggers. The experimental results show the positive effect of the root-level and bottom-level features that improve our parser from 81.17% to 81.41% and 81.16% to 81.57% labeled attach- ment scores with modified Yamada's and Nivre's method, respectively on the Chinese Treebank. In comparison to well-known parsers, such as Malt- Parser (80.74%) and MSTParser (78.08%), our methods produce not only better accuracy, but also drastically reduced testing time in 0.07 and 0.11, respectively. |
Year | Venue | Keywords |
|---|---|---|
2008 | COLING (Posters) | dependency parsing,dependence analysis,support vector machine |
Field | DocType | Volume |
Computer science,Dependency grammar,Natural language processing,Artificial intelligence,Syntax,Kernel (linear algebra),Pattern recognition,Support vector machine,Speech recognition,Treebank,Parsing,Parser combinator,Completeness (statistics) | Conference | C08-2 |
Citations | PageRank | References |
1 | 0.35 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Chieh Wu | 1 | 247 | 23.16 |
| Jie-Chi Yang | 2 | 350 | 43.91 |
| Yue-Shi Lee | 3 | 543 | 41.14 |