Name
Abstract | ||
|---|---|---|
It is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to classify queries based on ambiguity and to answer ''what the proportion of ambiguous queries is''. This paper deals with these issues. First, we clarify the definition of ambiguous queries by constructing the taxonomy of queries from being ambiguous to specific. Second, we ask human annotators to manually classify queries. From manually labeled results, we observe that query ambiguity is to some extent predictable. Third, we propose a supervised learning approach to automatically identify ambiguous queries. Experimental results show that we can correctly identify 87% of labeled queries with the approach. Finally, by using our approach, we estimate that about 16% of queries in a real search log are ambiguous. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.ipm.2008.09.005 | Inf. Process. Manage. |
Keywords | Field | DocType |
query taxonomy,paper deal,broad topics,ambiguous query,query classification,query ambiguity,web search,human annotators,supervised learning approach,real search log,supervised learning,search engine | Data mining,Search engine,Ask price,Information retrieval,Computer science,Web query classification,Supervised learning,Ambiguity,Java,Query formulation | Journal |
Volume | Issue | ISSN |
45 | 2 | Information Processing and Management |
Citations | PageRank | References |
32 | 1.38 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruihua Song | 1 | 1138 | 59.33 |
| Zhenxiao Luo | 2 | 65 | 2.66 |
| Jian-yun Nie | 3 | 3681 | 238.61 |
| Yong Yu | 4 | 7637 | 380.66 |
| Hsiao-Wuen Hon | 5 | 1719 | 354.37 |