Fine-Grained Entity Typing for Domain Independent Entity Linking

By Yasumasa Onoe, Greg Durrett. 2019

94 citations recorded by the source corpus.

Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in. For this problem, a “domain” is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset. We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. Rather than predicting entities directly, our approach models fine-grained entity properties, which can help disambiguate between even closely related entities. We derive a large inventory of types (tens of thousands) from Wikipedia categories, and use hyperlinked mentions in Wikipedia to distantly label data and train an entity typing model. At test time, we classify a mention with this typing model and use soft type predictions to link the mention to the most similar candidate entity. We evaluate our entity linking system on the CoNLL-YAGO dataset (Hoffart et al. 2011) and show that our approach outperforms prior domain-independent entity linking systems. We also test our approach in a harder setting derived from the WikilinksNED dataset (Eshel et al. 2017) where all the mention-entity pairs are unseen during test time. Results indicate that our approach generalizes better than a state-of-the-art neural model on the dataset.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Autoregressive Entity Retrieval (2020, methodology): “B.1 N AMED E NTITY D ISAMBIGUATION (Onoe & Durrett, 2020).”
Zero-shot Entity Linking with Dense Entity Retrieval (2019, background): “There is also recent work demonstrating that fine-grained entity typing information helps entity linking (Raiman and Raiman, 2018; Onoe and Durrett, 2019; Khalife and Vazirgiannis, 2018).”
Scalable Zero-shot Entity Linking with Dense Entity Retrieval (2019, methodology): “Following prior work (Sun et al., 2015; Cao et al., 2018; Gillick et al., 2019; Onoe and Durrett, 2019), we pre-train our models on Wikipedia6 data.”