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| Content Provider | ACM Digital Library |
|---|---|
| Author | Nallapati, Ramesh Croft, Bruce Allan, James |
| Abstract | In traditional relevance feedback, researchers have explored relevant document feedback, wherein, the query representation is updated based on a set of relevant documents returned by the user. In this work, we investigate relevant query feedback, in which we update a document's representation based on a set of relevant queries. We propose four statistical models to incorporate relevant query feedback.To validate our models, we considered anchor text of incoming links to a given document as feedback queries and performed experiments on the home-page retrieval task of TREC 2001. Our results show that three of our four models outperform the query-likelihood baseline by at least 35% in MRR score on a test set. |
| Starting Page | 560 |
| Ending Page | 563 |
| Page Count | 4 |
| File Format | |
| ISBN | 1581137230 |
| DOI | 10.1145/956863.956973 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2003-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Relevance feedback Relevant query Relevant document |
| Content Type | Text |
| Resource Type | Article |
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