Corpus-level Fine-grained Entity Typing Using Contextual Information

TL;DR

This paper introduces FIGMENT, an embedding-based method for corpus-level entity typing that leverages global and contextual information to improve knowledge base completion tasks.

Contribution

It proposes a novel combined global and context-based embedding approach for fine-grained entity typing at the corpus level.

Findings

FIGMENT outperforms open information extraction-based approaches.

The model effectively aggregates contextual information for entity classification.

Results demonstrate significant improvements in entity typing accuracy.

Abstract

This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.