CAJ | 학술논문

依存句法是句法分析的一种,相比于短语结构句法分析,依存句法具有更简洁的表达方式.该文采用英文语义角色标注的研究方法,实现了一个基于中文依存句法分析的语义角色标注系统.该系统针对中文依存关系树,采用有效的剪枝算法和特征,使用最大熵分类器进行语义角色的识别和分类.系统使用了两种不同的语料,一种是由标准短语结构句法分析(CTB5.0)转换而来,另一种是CoNLL2009公布的中文语料.系统分别在两种语料的标准谓词和自动谓词的基础上进行实验,在标准谓词上取得的F1值分别为84.30%和81.68%,在自动谓词上的F1值为81.02%和81.33%.
의존구법시구법분석적일충,상비우단어결구구법분석,의존구법구유경간길적표체방식.해문채용영문어의각색표주적연구방법,실현료일개기우중문의존구법분석적어의각색표주계통.해계통침대중문의존관계수,채용유효적전지산법화특정,사용최대적분류기진행어의각색적식별화분류.계통사용료량충불동적어료,일충시유표준단어결구구법분석(CTB5.0)전환이래,령일충시CoNLL2009공포적중문어료.계통분별재량충어료적표준위사화자동위사적기출상진행실험,재표준위사상취득적F1치분별위84.30%화81.68%,재자동위사상적F1치위81.02%화81.33%.
Dependency representations are more simple and intuitive than constituent representations for Chinese parse. This paper implements a Chinese dependency parse based semantic role labeling (SRL) by using the similar methods in English SRL. In the system, effective pruning algorithm and useful features are adopted for Chinese dependency tree, and the semantic role identification and classification are accomplished by a maximum entropy classifier. Two different corpora are adopted to test our system, one is transferred from constituent-based corpus (CTB5.0), and the other is Chinese dataset provided by CoNLL 2009 shared task. Based on the two datasets, the system achieves, respectively, 84.30% and 81.68% in labeled F1 for gold predicates, and 81.02% and 81.33% for automatic predicates.