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Cross-Level Matching Model for Information Retrieval

  • Yifan Nie
  • Jian-Yun NieEmail author
Conference paper
  • 42 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12004)

Abstract

Recently, many neural retrieval models have been proposed and shown competitive results. In particular, interaction-based models have shown superior performance to traditional models in a number of studies. However, the interactions used as the basic matching signals are between single terms or their embeddings. In reality, a term can often match a phrase or even longer segment of text. This paper proposes a Cross-Level Matching Model which enhances the basic matching signals by allowing terms to match hidden representation states within a sentence. A gating mechanism aggregates the learned matching patterns of different matching channels and outputs a global matching score. Our model provides a simple and effective way for word-phrase matching.

Keywords

Information retrieval Neural network Ranking 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of MontrealMontrealCanada

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