Identification of ambiguous queries in web search

  • Authors:

  • Ruihua Song;Zhenxiao Luo;Jian-Yun Nie;Yong Yu;Hsiao-Wuen Hon

  • Affiliations:

  • Shanghai Jiao Tong University, Shanghai 200240, China and Microsoft Research Asia, 4F, Sigma Center, No. 49 Zhichun Road, Beijing 100190, China;Fudan University, Shanghai 200433, China;University of Montréal, H3C 3J7, Canada;Shanghai Jiao Tong University, Shanghai 200240, China;Microsoft Research Asia, 4F, Sigma Center, No. 49 Zhichun Road, Beijing 100190, China

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2009

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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.