Context-Aware Term Weighting For First Stage Passage Retrieval
2020pp. 1533–1536
Citations Over TimeTop 10% of 2020 papers
Abstract
Term frequency is a common method for identifying the importance of a term in a document. But term frequency ignores how a term interacts with its text context, which is key to estimating document-specific term weights. This paper proposes a Deep Contextualized Term Weighting framework (DeepCT) that maps the contextualized term representations from BERT to into context-aware term weights for passage retrieval. The new, deep term weights can be stored in an ordinary inverted index for efficient retrieval. Experiments on two datasets demonstrate that DeepCT greatly improves the accuracy of first-stage passage retrieval algorithms.
Related Papers
- → Extended implied weighting(2013)233 cited
- → Weighting in LCA – approaches and applications(2000)92 cited
- → The application of combination weighting approach in multiple attribute decision making(2009)9 cited
- → New Internet search volume-based weighting method for integrating various environmental impacts(2015)24 cited
- → A study of weighting factors of the quadratic performance index(1969)5 cited