@inproceedings{c0224e9707764fa4b8e73186434c3541,
title = "Finding Salient Context based on Semantic Matching for Relevance Ranking",
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.",
keywords = "contextual salience, matching, semantic matching",
author = "Yuanyuan Qi and Jiayue Zhang and Weiran Xu and Jun Guo and Yan Li",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019 ; Conference date: 01-12-2019 Through 04-12-2019",
year = "2019",
month = dec,
doi = "10.1109/VCIP47243.2019.8965741",
language = "English",
series = "2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019",
address = "United States",
}