FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature
  Context and Term-level Knowledge
Zunran Wang Zhonghua Li Wei Shen Qi Ye Liqiang Nie
Lexicon-based retrieval has gained siginificant popularity in text retrieval
due to its efficient and robust performance. To further enhance performance of
lexicon-based retrieval, researchers have been diligently incorporating
state-of-the-art methodologies like Neural retrieval and text-level contrastive
learning approaches. Nonetheless, despite the promising outcomes, current
lexicon-based retrieval methods have received limited attention in exploring
the potential benefits of feature context representations and term-level
knowledge guidance. In this paper, we introduce an innovative method by
introducing FEature Context and TErm-level Knowledge modules(FecTek). To
effectively enrich the feature context representations of term weight, the
Feature Context Module (FCM) is introduced, which leverages the power of BERT's
representation to determine dynamic weights for each element in the embedding.
Additionally, we develop a term-level knowledge guidance module (TKGM) for
effectively utilizing term-level knowledge to intelligently guide the modeling
process of term weight. Evaluation of the proposed method on MS Marco benchmark
demonstrates its superiority over the previous state-of-the-art approaches.