Finding Salient Context based on Semantic Matching for Relevance Ranking
This work addresses relevance ranking for information retrieval systems, presenting an incremental improvement with a novel method for a known bottleneck.
The paper tackles relevance ranking in information retrieval by introducing a salient-context based semantic matching method, which improves performance over state-of-the-art methods as demonstrated in experiments on TREC collections.
In this paper, 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 collections from TREC show the effectiveness of our model compared to the state-of-the-art methods.