# Word-Entity Duet Representations for Document Ranking

arXiv: https://arxiv.org/abs/1706.06636 · HTML: https://mlsanity.com/paper/1706.06636/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 16 of 16)

- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [Neural ranking models for document retrieval](https://mlsanity.com/paper/2102.11903/) (2021)
- [ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget](https://mlsanity.com/paper/2408.00103/) (2024)
- [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://mlsanity.com/paper/2308.05502/) (2023)
- [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021)
- [Entity-aware Transformers for Entity Search](https://mlsanity.com/paper/2205.00820/) (2022)
- [Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement](https://mlsanity.com/paper/2112.13510/) (2021)
- [DREQ: Document Re-Ranking Using Entity-based Query Understanding](https://mlsanity.com/paper/2401.05939/) (2024)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases](https://mlsanity.com/paper/2209.00351/) (2022)
- [Query-Specific Knowledge Graphs for Complex Finance Topics](https://mlsanity.com/paper/2211.04142/) (2022)
- [QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking](https://mlsanity.com/paper/2510.11589/) (2025)
- [Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking](https://mlsanity.com/paper/2606.15998/) (2026)
- [Knowledge-enhanced Multi-perspective Video Representation Learning for Scene Recognition](https://mlsanity.com/paper/2401.04354/) (2024)
- [Dynamic Injection of Entity Knowledge into Dense Retrievers](https://mlsanity.com/paper/2507.03922/) (2025)
- [REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking](https://mlsanity.com/paper/2510.11592/) (2025)

## Abstract (arXiv)

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.