Learning Dense Representations for Entity Retrieval
Learning Dense Representations for Entity Retrieval Daniel Gillick∗ Sayali Kulkarni∗ Larry Lansing∗ Alessandro Presta∗ Google Research Google Research Google Research Google Research dgillick@google.com sayali@google.com llansing@google.com apresta@google.com Jason Baldridge Eugene Ie Diego Garcia-Olanoy Google Research Google Research University of Texas at Austin jasonbaldridge@google.com eugeneie@google.com diegoolano@gmail.com Abstract arbitrary, hard cutoffs, such as only including the thirty most popular entities associated with a particular men- We show that it is feasible to perform entity tion. We show that this configuration can be replaced linking by training a dual encoder (two-tower) with a more robust model that represents both entities model that encodes mentions and entities in and mentions in the same vector space. Such a model the same dense vector space, where candidate allows candidate entities to be directly and efficiently entities are retrieved by approximate nearest retrieved for a mention, using nearest neighbor search. neighbor search. Unlike prior work, this setup To see why a retrieval approach is desirable, we need does not rely on an alias table followed by a to consider how alias tables are employed in entity reso- re-ranker, and is thus the first fully learned en- lution systems. In the following example, Costa refers tity retrieval model. We show that our dual to footballer Jorge Costa, but the entities associated with encoder, trained using only anchor-text links that alias in existing Wikipedia text are Costa Coffee, in Wikipedia, outperforms discrete alias ta- Paul Costa Jr, Costa Cruises, and many others—while ble and BM25 baselines, and is competitive excluding the true entity.
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