Boosting Entity Linking Performance by Leveraging Unlabeled Documents
Phong Ba Le, Ivan S. Titov · 2019
Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL).In contrast, we propose an approach which exploits only naturally occurring information: unlabeled documents and Wikipedia.Our approach consists of two stages.First, we construct a high recall list of candidate entities for each mention in an unlabeled document.Second, we use the candidate lists as weak supervision to constrain our document-level entity linking model.The model treats entities as latent variables and, when estimated on a collection of unlabelled texts, learns to choose entities relying both on local context of each mention and on coherence with other entities in the document.The resulting approach rivals fully-supervised state-of-the-art systems on standard test sets.It also approaches their performance in the very challenging setting: when tested on a test set sampled from the data used to estimate the supervised systems.By comparing to Wikipedia-only training of our model, we demonstrate that modeling unlabeled documents is beneficial.