Relation Extraction with Contextualized Relation Embedding (CRE)
This work addresses the problem of improving relation extraction accuracy by better integrating knowledge base modeling, which is significant for researchers and practitioners working on information extraction and knowledge graph construction.
This paper proposes a novel architecture for relation extraction that integrates semantic information with knowledge base modeling. The model encodes sentences into contextualized relation embeddings, which are then used with parameterized entity embeddings to score relation instances, achieving state-of-the-art performance on datasets derived from The New York Times Annotated Corpus and FreeBase.
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.