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	Interrogative-guided re-ranking for question-oriented software text retrieval
	Ye, Ting ; Xie, Bing ; Zou, Yanzhen ; Chen, Xiuzhao
	2014
英文摘要	In many software engineering tasks, question-oriented text retrieval is often used to help developers search for software artifacts. In this paper, we propose an interrogativeguided re-ranking approach for question-oriented software text retrieval. Since different interrogatives usually indicate users' different search focuses, we firstly label 9 kinds of question-answer pairs according to the common interrogatives. Then, we train document classifiers by using 1,826 questions along with 2,460 answers from StackOverflow, apply the classifiers to our document repository and present a re-ranking approach to improve the retrieval precision. In software document classification, our classifiers achieve the average precision, recall and F-measure of 56.2%, 90.9% and 69.4% respectively. Our re-ranking approach presents 9.6% improvement in nDCG@1 upon the baseline, and we also obtain 8.1% improvement in nDCG@10 when more candidates are included. ? 2014 ACM.; EI; 0
语种	英语
DOI标识	10.1145/2642937.2642953
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294839]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Ye, Ting,Xie, Bing,Zou, Yanzhen,et al. Interrogative-guided re-ranking for question-oriented software text retrieval. 2014-01-01.

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