# Word-Entity Duet Representations for Document Ranking

## TL;DR

This paper introduces a word-entity duet framework that leverages knowledge bases for improved document ranking, using an attention-based model to effectively combine word and entity representations and handle noise.

## Contribution

The paper proposes a novel word-entity duet framework with an attention-based ranking model that effectively integrates word and entity information for ad-hoc retrieval.

## Key findings

- The duet interactions significantly improve ranking performance.
- The attention mechanism effectively filters out noisy entities.
- The model outperforms existing word-only and entity-only ranking systems.

## Abstract

This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations and entity-based representations. Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph. To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed. With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet. Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems.