Relation Extraction with Contextualized Relation Embedding (CRE)

TL;DR

This paper introduces CRE, a novel relation extraction architecture that integrates knowledge base modeling with contextualized relation embeddings, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper presents a new model architecture that internalizes knowledge base modeling within relation extraction using contextualized relation embeddings.

Findings

Achieves state-of-the-art performance on NYT and FreeBase datasets.

Introduces a novel encoding of sentences into contextualized relation embeddings.

Demonstrates effective integration of KB modeling with relation extraction.

Abstract

Relation extraction is the task of identifying relation instance between two entities given a corpus whereas Knowledge base modeling is the task of representing a knowledge base, in terms of relations between entities. This paper proposes an architecture for the relation extraction task that integrates semantic information with knowledge base modeling in a novel manner. Existing approaches for relation extraction either do not utilize knowledge base modelling or use separately trained KB models for the RE task. We present a model architecture that internalizes KB modeling in relation extraction. This model applies a novel approach to encode sentences into contextualized relation embeddings, which can then be used together with parameterized entity embeddings to score relation instances. The proposed CRE model achieves state of the art performance on datasets derived from The New York Times Annotated Corpus and FreeBase. The source code has been made available.