Paper: Robust and Efficient Chinese Word Dependency Analysis with Linear Kernel Support Vector Machines

ACL ID C08-2034
Title Robust and Efficient Chinese Word Dependency Analysis with Linear Kernel Support Vector Machines
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2008
Authors

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 L 2 - 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 we...