LaQuE: Enabling Entity Search at Scale

Negar Arabzadeh, Amin Bigdeli, Ebrahim Bagheri

Abstract: Entity search plays a crucial role in various information access domains, where users seek information about specific entities. Despite significant research efforts to improve entity search methods, the availability of large-scale resources and extensible frameworks has been limiting progress. In this work, we present LaQuE (Large-scale Queries for Entity search), a curated framework for entity search, which includes a reproducible and extensible code base as well as a large relevance judgment collection consisting of real-user queries based on the ORCAS collection. LaQuE is industry-scale and suitable for training complex neural models for entity search. We develop methods for curating and judging entity collections, as well as training entity search methods based on LaQuE. We additionally establish strong baselines within LaQuE based on various retrievers, including traditional bag-of-words-based methods and neural-based models. We show that training neural entity search models on LaQuE enhances retrieval effectiveness compared to the state-of-the-art. Additionally, we categorize the released queries in LaQuE based on their popularity and difficulty, encouraging research on more challenging queries for the entity search task. We publicly release LaQuE at https://github.com/Narabzad/LaQuE.