A STEPWISE DETECTION OF CONJUNCTIVE STRUCTURES IN QUESTIONS USING MAXIMUM ENTROPY MODEL

摘要：

This paper presents a maximum entropy model approach to identifying conjuncts of conjunctive structures in questions of financial domain from on-line discussion groups.To avoid phrasal ambiguity, only features in lexical and shallow syntactic level are used.The conjunct detection problem is converted into a stepwise boundary identification task, reducing the search space of a n-word sentence from O(n2) to O(n), The best performance on the test set achieves 85.88％ recall and 96％ rejection.This approach itself is domain-independent and can be used for conjunct identification in questions universally.

关键词： Conjunctive structure detection Maximum entropy Question and answering Financial domain

作者: YAO-YUN ZHANG XUAN WANG XIAO-LONG WANG SHI-XI FAN

作者单位: Intelligence Computing Research Center, Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3916-3921

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）