Abstract
Our goal is to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need. While related to the problem of support passage retrieval, in this paper, we explore two underutilized indicators of relevance: contextual entities and entity salience. The effectiveness of the indicators is studied within a supervised learning-to-rank framework on a dataset from TREC Complex Answer Retrieval. We find that salience is a useful indicator, but it is often not applicable. In contrast, although performance improvements are obtained by using contextual entities, using contextual words still outperforms contextual entities.
Recommended Citation
S. Chatterjee and L. Dietz, "Why Does This Entity Matter? Support Passage Retrieval for Entity Retrieval," ICTIR 2019 - Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 221 - 224, Association for Computing Machinery, Sep 2019.
The definitive version is available at https://doi.org/10.1145/3341981.3344243
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-145036881-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
23 Sep 2019
