@inproceedings{shavarani-sarkar-2025-entity,
title = "Entity Retrieval for Answering Entity-Centric Questions",
author = "Shavarani, Hassan and
Sarkar, Anoop",
editor = "Shi, Weijia and
Yu, Wenhao and
Asai, Akari and
Jiang, Meng and
Durrett, Greg and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.knowledgenlp-1.1/",
doi = "10.18653/v1/2025.knowledgenlp-1.1",
pages = "1--17",
ISBN = "979-8-89176-229-9",
abstract = "The similarity between the question and indexed documents is a key factor in document retrieval for retrieval-augmented question answering. Although this is typically the only method for obtaining the relevant documents, it is not the sole approach when dealing with entity-centric questions. We study Entity Retrieval, an alternative retrieval method, which rather than relying on question-document similarity, depends on the salient entities within the question to identify the retrieval documents. We conduct an in-depth analysis of the performance of both dense and sparse retrieval methods in comparison to Entity Retrieval. Our findings reveal the great potential of entity-driven methods for improving augmentation document retrieval in both accuracy and efficiency."
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<abstract>The similarity between the question and indexed documents is a key factor in document retrieval for retrieval-augmented question answering. Although this is typically the only method for obtaining the relevant documents, it is not the sole approach when dealing with entity-centric questions. We study Entity Retrieval, an alternative retrieval method, which rather than relying on question-document similarity, depends on the salient entities within the question to identify the retrieval documents. We conduct an in-depth analysis of the performance of both dense and sparse retrieval methods in comparison to Entity Retrieval. Our findings reveal the great potential of entity-driven methods for improving augmentation document retrieval in both accuracy and efficiency.</abstract>
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%0 Conference Proceedings
%T Entity Retrieval for Answering Entity-Centric Questions
%A Shavarani, Hassan
%A Sarkar, Anoop
%Y Shi, Weijia
%Y Yu, Wenhao
%Y Asai, Akari
%Y Jiang, Meng
%Y Durrett, Greg
%Y Hajishirzi, Hannaneh
%Y Zettlemoyer, Luke
%S Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-229-9
%F shavarani-sarkar-2025-entity
%X The similarity between the question and indexed documents is a key factor in document retrieval for retrieval-augmented question answering. Although this is typically the only method for obtaining the relevant documents, it is not the sole approach when dealing with entity-centric questions. We study Entity Retrieval, an alternative retrieval method, which rather than relying on question-document similarity, depends on the salient entities within the question to identify the retrieval documents. We conduct an in-depth analysis of the performance of both dense and sparse retrieval methods in comparison to Entity Retrieval. Our findings reveal the great potential of entity-driven methods for improving augmentation document retrieval in both accuracy and efficiency.
%R 10.18653/v1/2025.knowledgenlp-1.1
%U https://aclanthology.org/2025.knowledgenlp-1.1/
%U https://doi.org/10.18653/v1/2025.knowledgenlp-1.1
%P 1-17
Markdown (Informal)
[Entity Retrieval for Answering Entity-Centric Questions](https://aclanthology.org/2025.knowledgenlp-1.1/) (Shavarani & Sarkar, KnowledgeNLP 2025)
ACL
- Hassan Shavarani and Anoop Sarkar. 2025. Entity Retrieval for Answering Entity-Centric Questions. In Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing, pages 1–17, Albuquerque, New Mexico, USA. Association for Computational Linguistics.