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.
ŞAHİN, ÖMER; ÇİÇEKLİ, İLYAS; and ERCAN, GÖNENÇ
"Learning term weights by overfitting pairwise ranking loss,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
5, Article 16.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss5/16