Answering Definitional Question by Dependency-Based Knowledge

摘要：

Most current systems apply flat pattern and flat centroid words, which are extracted only by relative position to question target, to identify definition sentences. In contrast to the flat knowledge, we propose dependency-based knowledge, including dependency pattern and dependency centroid word, which are extracted by dependency relation to question target. Specifically, we use the improved ultraconservative online algorithm, binary Margin Infused Relaxed Algorithm (MIRA), to estimate the weight of each dependency knowledge for the task of candidate sentences ranking. We demonstrate that the dependency-based knowledge is more effective than the flat knowledge. Meanwhile, we also show that our definitional question answering system outperforms the state-of-the-art systems on recent TREC data.

关键词： definitional question answering MIRA dependency relation

作者: Junkuo CAO Xuanjing HUANG

作者单位: Dept of Computer Science,Fudan University Shanghai,China

会议类型: 国际会议

会议名称: The 2008 IEEE International Conference on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-19（万方平台首次上网日期，不代表论文的发表时间）