Name
Abstract | ||
|---|---|---|
It is widely believed that some queries submitted to search engines are by nature ambiguous (e.g., java, apple). However, few studies have investigated the questions of "how many queries are ambiguous?" and "how can we automatically identify an ambiguous query?" This paper deals with these issues. First, we construct the taxonomy of query ambiguity, and ask human annotators to manually classify queries based upon it. From manually labeled results, we find that query ambiguity is to some extent predictable. We then use a supervised learning approach to automatically classify queries as being ambiguous or not. Experimental results show that we can correctly identify 87% of labeled queries. Finally, we estimate that about 16% of queries in a real search log are ambiguous. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1242572.1242749 | WWW |
Keywords | Field | DocType |
paper deal,ambiguous query,query ambiguity,web search,supervised learning approach,human annotators,real search log,query classification,supervised learning,search engine | Web search query,Data mining,World Wide Web,Ask price,Search engine,Information retrieval,Computer science,Web query classification,Supervised learning,Spatial query,Java,Ambiguity | Conference |
Citations | PageRank | References |
33 | 1.28 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruihua Song | 1 | 1138 | 59.33 |
| Zhenxiao Luo | 2 | 65 | 2.66 |
| Ji-Rong Wen | 3 | 4431 | 265.98 |
| Yong Yu | 4 | 7637 | 380.66 |
| Hsiao-Wuen Hon | 5 | 1719 | 354.37 |