A Generative Model for Relation Extraction and Classification
This provides a more efficient and flexible method for relation extraction, benefiting NLP applications like knowledge base population and question answering, though it is incremental in nature.
The authors tackled relation extraction by modeling it as a sequence-to-sequence generation task with their GREC model, achieving state-of-the-art performance on three benchmark datasets.
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering. In this paper, we present a novel generative model for relation extraction and classification (which we call GREC), where RE is modeled as a sequence-to-sequence generation task. We explore various encoding representations for the source and target sequences, and design effective schemes that enable GREC to achieve state-of-the-art performance on three benchmark RE datasets. In addition, we introduce negative sampling and decoding scaling techniques which provide a flexible tool to tune the precision and recall performance of the model. Our approach can be extended to extract all relation triples from a sentence in one pass. Although the one-pass approach incurs certain performance loss, it is much more computationally efficient.