Learning term weights by overfitting pairwise ranking loss
Turkish Journal of Electrical Engineering and Computer Sciences, vol.30, no.5, pp.1914-1930, 2022 (SCI-Expanded, Scopus, TRDizin)
- Publication Type: Article / Article
- Volume: 30 Issue: 5
- Publication Date: 2022
- Doi Number: 10.55730/1300-0632.3913
- Journal Name: Turkish Journal of Electrical Engineering and Computer Sciences
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
- Page Numbers: pp.1914-1930
- Keywords: Information retrieval, passage ranking, term weighting, pairwise ranking optimization
- Open Archive Collection: AVESIS Open Access Collection
- Hacettepe University Affiliated: Yes
Abstract
© 2022 Turkiye Klinikleri. All rights reserved.A search engine strikes a balance between effectiveness and efficiency to retrieve the best documents in a scalable way. Recent deep learning-based ranker methods are proving to be effective and improving the state-of-the-art in relevancy metrics. However, as opposed to index-based retrieval methods, neural rankers like bidirectional encoder representations from transformers (BERT) do not scale to large datasets. In this article, we propose a query term weighting method that can be used with a standard inverted index without modifying it. Query term weights are learned using relevant and irrelevant document pairs for each query, using a pairwise ranking loss. The learned weights prove to be more effective than term recall which is a probabilistic relevance feedback, previously used for the task. We further show that these weights can be predicted with a BERT regression model and improve the performance of both a BM25 based index and an index already optimized with a term weighting function.