摘要
文章针对列表类自动问题回答的任务要求,提出了一种基于短语检索和答案距离排序模型的列表类问题回答的方法。该短语检索模型在传统的TF/IDF检索模型上进行改进,提出了利用不同长度短语作为查询词的检索方法,能够返回更多包含正确答案的相关文档;答案的距离排序模型则利用答案与上下文词之间的距离作为排序的依据对答案列表进行排序,可以提高正确答案的排名。这两种模型地提出在一定程度上解决了如何在返回尽可能多的答案的同时保证答案质量的问题。实验结果表明利用这两种模型的列表类问题回答方法对系统的性能有显著提高。
This paper presents a List Question Answering method based on a phrase-retrieval model and an answerranking model. The retrieval model utilizes phrases as query words and the answer ranking model scores the candidate answer mainly through the distance between the candidate answer and other contextual words. The two models jointly offer an effective way to find more answers and better answers in the list question answering task. The experiment shows that our phrase retrieval model outperforms other retrieval models and our answer ranking model improves the F score significantly.
出处
《中文信息学报》
CSCD
北大核心
2008年第5期74-79,共6页
Journal of Chinese Information Processing
基金
国家自然科学基金重点资助项目(60435020
60673038)
关键词
计算机应用
中文信息处理
问题回答
文档检索
信息抽取
computer application
Chinese information processing
question answering
document retrieval
information extraction
