Corpus-level Fine-grained Entity Typing

TL;DR

This paper introduces FIGMENT, an embedding-based approach for corpus-level entity typing that combines global and context models with multi-level representations and noise mitigation techniques, improving knowledge base completion.

Contribution

It proposes a novel embedding-based framework with multi-level representations and noise reduction algorithms for more accurate entity typing from large corpora.

Findings

Effective in large entity typing dataset from Freebase

Multi-level representations outperform single-level models

Noise mitigation improves model performance

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. Each of the two proposed models has some specific properties. For the global model, learning high quality entity representations is crucial because it is the only source used for the predictions. Therefore, we introduce representations using name and contexts of entities on the three levels of entity, word, and character. We show each has complementary information and a multi-level representation is the best. For the context model, we need to use distant supervision since the context-level labels are not available for entities. Distant supervised labels are noisy and this harms the performance of models. Therefore, we introduce and apply new algorithms for noise mitigation using multi-instance learning. We show the effectiveness of our models in a large entity typing dataset, built from Freebase.