Name
Abstract | ||
|---|---|---|
We address the problem of finding multiple groups of words or phrases that explain the underlying query facets, which we refer to as query dimensions. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query dimensions can be mined out by aggregating these significant lists. Experimental results show that a large number of lists do exist in the top results, and query dimensions generated by grouping these lists are useful for users to learn interesting knowledge about the queries. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2063576.2063767 | CIKM |
Keywords | Field | DocType |
significant list,large number,interesting knowledge,top result,important aspect,query dimension,multiple group,underlying query facet,entity | Query optimization,Range query (database),Web search query,Data mining,Query language,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Spatial query | Conference |
Citations | PageRank | References |
18 | 0.62 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhicheng Dou | 1 | 706 | 41.96 |
| Sha Hu | 2 | 106 | 6.99 |
| Yulong Luo | 3 | 18 | 0.62 |
| Ruihua Song | 4 | 1138 | 59.33 |
| Ji-Rong Wen | 5 | 4431 | 265.98 |