Distant Learning for Entity Linking with Automatic Noise Detection

Phong Le, Ivan Titov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate entity linkers have been produced for domains and languages where annotated data (i.e., texts linked to a knowledge base) is available. However, little progress has been made for the settings where no or very limited amounts of labeled data are present (e.g., legal or most scientific domains). In this work, we show how we can learn to link mentions without having any labeled examples, only a knowledge base and a collection of unannotated texts from the corresponding domain.In order to achieve this, we frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. As the learning signal is weak and our surrogate labels are noisy, we introduce a noise detection component in our model: it lets the model detect and disregard examples which are likely to be noisy. Our method, jointly learning to detect noise and link entities, greatly outperforms the surface matching baseline. For a subset of entity categories, it even approaches the performance of supervised learning.

Original languageEnglish
Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics (long papers)
EditorsAnna Korhonen, David Traum, Lluís Màrquez
Place of PublicationFlorence, Italy
PublisherACL Anthology
Pages4081–4090
Number of pages10
Volume1
ISBN (Print)978-1-950737-48-2
Publication statusE-pub ahead of print - 2 Aug 2019
Event57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy
Duration: 28 Jul 20192 Aug 2019
Conference number: 57
http://www.acl2019.org/EN/index.xhtml

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2019
CountryItaly
CityFlorence
Period28/07/192/08/19
Internet address

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