Name
Abstract | ||
|---|---|---|
We propose a salient-context based semantic matching method to improve relevance ranking in information retrieval. We first propose a new notion of salient context and then define how to measure it. Then we show how the most salient context can be located with a sliding window technique. Finally, we use the semantic similarity between a query term and the most salient context terms in a corpus of documents to rank those documents. Experiments on various TREC collections show the effectiveness of our model compared to the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VCIP47243.2019.8965741 | VCIP |
Field | DocType | Citations |
Semantic similarity,Sliding window protocol,Information retrieval,Ranking,Context based,Computer science,Theoretical computer science,Salient,Semantic matching | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanyuan Qi | 1 | 0 | 0.34 |
| Jiayue Zhang | 2 | 0 | 0.34 |
| Weiran Xu | 3 | 210 | 43.79 |
| Jun Guo | 4 | 1579 | 137.24 |
| Jun Guo | 5 | 7 | 3.24 |