@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://preview.aclanthology.org/ingest-emnlp/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."
}