tailieunhanh - Báo cáo khoa học: "Dependency Parsing and Projection Based on Word-Pair Classification"

In this paper we describe an intuitionistic method for dependency parsing, where a classifier is used to determine whether a pair of words forms a dependency edge. And we also propose an effective strategy for dependency projection, where the dependency relationships of the word pairs in the source language are projected to the word pairs of the target language, leading to a set of classification instances rather than a complete tree. | Dependency Parsing and Projection Based on Word-Pair Classification Wenbin Jiang and Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences . Box 2704 Beijing 100190 China jiangwenbin liuqun @ Abstract In this paper we describe an intuitionistic method for dependency parsing where a classifier is used to determine whether a pair of words forms a dependency edge. And we also propose an effective strategy for dependency projection where the dependency relationships of the word pairs in the source language are projected to the word pairs of the target language leading to a set of classification instances rather than a complete tree. Experiments show that the classifier trained on the projected classification instances significantly outperforms previous projected dependency parsers. More importantly when this classifier is integrated into a maximum spanning tree MST dependency parser obvious improvement is obtained over the MST baseline. 1 Introduction Supervised dependency parsing achieves the state-of-the-art in recent years McDonald et al. 2005a McDonald and Pereira 2006 Nivre et al. 2006 . Since it is costly and difficult to build human-annotated treebanks a lot of works have also been devoted to the utilization of unannotated text. For example the unsupervised dependency parsing Klein and Manning 2004 which is totally based on unannotated data and the semisupervised dependency parsing Koo et al. 2008 which is based on both annotated and unannotated data. Considering the higher complexity and lower performance in unsupervised parsing and the need of reliable priori knowledge in semisupervised parsing it is a promising strategy to project the dependency structures from a resource-rich language to a resource-scarce one across a bilingual corpus Hwa et al. 2002 Hwa et al. 2005 Ganchev et al. 2009 Smith and Eisner 2009 Jiang et al. 2009 . For dependency projection the relationship between .