Mining subtopics from text fragments for a web query

  • Authors:

  • Qinglei Wang;Yanan Qian;Ruihua Song;Zhicheng Dou;Fan Zhang;Tetsuya Sakai;Qinghua Zheng

  • Affiliations:

  • SPKLSTN Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049;SPKLSTN Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049;Microsoft Research Asia, Beijing, People's Republic of China 100080;Microsoft Research Asia, Beijing, People's Republic of China 100080;Nankai-Baidu Joint Lab, Nankai University, Tianjin, People's Republic of China 300071;Microsoft Research Asia, Beijing, People's Republic of China 100080;SPKLSTN Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049

  • Venue:
  • Information Retrieval
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Web search queries are often ambiguous or faceted, and the task of identifying the major underlying senses and facets of queries has received much attention in recent years. We refer to this task as query subtopic mining. In this paper, we propose to use surrounding text of query terms in top retrieved documents to mine subtopics and rank them. We first extract text fragments containing query terms from different parts of documents. Then we group similar text fragments into clusters and generate a readable subtopic for each cluster. Based on the cluster and the language model trained from a query log, we calculate three features and combine them into a relevance score for each subtopic. Subtopics are finally ranked by balancing relevance and novelty. Our evaluation experiments with the NTCIR-9 INTENT Chinese Subtopic Mining test collection show that our method significantly outperforms a query log based method proposed by Radlinski et al. (2010) and a search result clustering based method proposed by Zeng et al. (2004) in terms of precision, I-rec, D-nDCG and D#-nDCG, the official evaluation metrics used at the NTCIR-9 INTENT task. Moreover, our generated subtopics are significantly more readable than those generated by the search result clustering method.