Name
Abstract | ||
|---|---|---|
Entity-based ranking systems often employ entity linking systems to align entities to query and documents. Previously, entity linking systems were not designed specifically for search engines and were mostly used as a preprocessing step. This work presents JointSem, a joint semantic ranking system that combines query entity linking and entity-based document ranking. In JointSem, the spotting and linking signals are used to describe the importance of candidate entities in the query, and the linked entities are utilized to provide additional ranking features for the documents. The linking signals and the ranking signals are combined by a joint learning-to-rank model, and the whole system is fully optimized towards end-to-end ranking performance. Experiments on TREC Web Track datasets demonstrate the effectiveness of joint learning of entity linking and entity-based ranking.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3132847.3133048 | CIKM |
Keywords | Field | DocType |
Entity Linking, Document Ranking, Entity-based Search | Entity linking,Data mining,Search engine,Information retrieval,Ranking,Computer science,Weak entity,Preprocessor,Spotting | Conference |
ISBN | Citations | PageRank |
978-1-4503-4918-5 | 2 | 0.36 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-Yan Xiong | 1 | 405 | 30.82 |
| Zhengzhong Liu | 2 | 37 | 7.69 |
| James P. Callan | 3 | 6237 | 833.28 |
| Eduard H. Hovy | 4 | 7450 | 663.27 |