论文信息 - Chinese Dependency Parsing Based on An Improved Model of MST

Chinese Dependency Parsing Based on An Improved Model of MST

In this paper, a Chinese dependency parsing method was presented based on improved Maximum Spanning Tree algorithm. Within this method, Conditional Random Field (CRF) is adopted to establish sequence labeling model. Recognizing POS of head node is employed to modify the weights of directed edges in the MST model. Comparative experiments on CoNLL 2009 data set show that the new method shows better performance than current Chinese dependency methods, with precision reaching to 85.45%.

Yang Xiang | Qi Sun | Xiao Tu

[1] Li Zupeng,et al. An improved approach for Chinese parsing , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[2] Michael A. Covington,et al. A Fundamental Algorithm for Dependency Parsing , 2004 .

[3] Yuji Matsumoto,et al. Statistical Dependency Analysis with Support Vector Machines , 2003, IWPT.

[4] Joakim Nivre,et al. Integrating Graph-Based and Transition-Based Dependency Parsers , 2008, ACL.

[5] Liu Ting. Chinese Dependency Parsing Model Based on Lexical Governing Degree , 2006 .

[6] Koby Crammer,et al. Online Large-Margin Training of Dependency Parsers , 2005, ACL.

[7] Fernando Pereira,et al. Online Learning of Approximate Dependency Parsing Algorithms , 2006, EACL.

[8] Michael Collins,et al. Efficient Third-Order Dependency Parsers , 2010, ACL.

[9] Joakim Nivre,et al. Deterministic Dependency Parsing of English Text , 2004, COLING.

[10] Fernando Pereira,et al. Discriminative learning and spanning tree algorithms for dependency parsing , 2006 .