Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10791-013-9221-8 | Inf. Retr. |
Keywords | Field | DocType |
mining subtopics,readable subtopic,query subtopic mining,ntcir-9 intent chinese subtopic,extract text,query log,ntcir-9 intent task,query term,web search query,web query,group similar text fragment,search result | Web search query,Data mining,Ranking,Information retrieval,Query expansion,Computer science,Web query classification,Ranking (information retrieval),Novelty,Cluster analysis,Language model | Journal |
Volume | Issue | ISSN |
16 | 4 | 1573-7659 |
Citations | PageRank | References |
10 | 0.48 | 36 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinglei Wang | 1 | 10 | 0.48 |
| Yanan Qian | 2 | 111 | 6.75 |
| Ruihua Song | 3 | 1138 | 59.33 |
| Zhicheng Dou | 4 | 706 | 41.96 |
| Fan Zhang | 5 | 229 | 69.82 |
| Tetsuya Sakai | 6 | 1460 | 139.97 |
| Qinghua Zheng | 7 | 1261 | 160.88 |