Multi-source Representation Enhancement for Wikipedia-style Entity Annotation

Abstract

Entity annotation in Wikipedia (officially named wikilinks) greatly benefits human end-users. Human editors are required to select all mentions that are most helpful to human end-users and link each mention to a Wikipedia page. We aim to design an automatic system to generate Wikipedia-style entity annotation for any plain text. However, existing research either rely heavily on mention-entity map or are restricted to named entities only. Besides, they neglect to select the appropriate mentions as Wikipedia requires. As a result, they leave out some necessary annotation and introduce excessive distracting annotation. Existing benchmarks also skirt around the coverage and selection issues. We propose a new task called Mention Detection and Se-lection for entity annotation, along with a new benchmark, WikiC, to better reflect annotation quality. The task is coined centering mentions specific to each position in high-quality human-annotated examples. We also proposed a new framework, DrWiki, to fulfill the task. We adopt a deep pre-trained span selection model inferring directly from plain text via tokens' context embedding. It can cover all possible spans and avoid limiting to mention-entity maps. In addition, information of both inarguable mention-entity pairs, and mention repeat has been introduced as token-wise representation enhancement by FLAT attention and repeat embedding respectively. Empirical results on WikiC show that, compared with often adopted and state-of-the-art Entity Linking and Entity Recognition methods, our method achieves improvement to previous methods in overall performance. Additional experiments show that DrWiki gains improvement even with a low-coverage mention-entity map.