A Relation Extraction Method Based on Entity Type Embedding and Recurrent Piecewise Residual Networks

摘要

Relation extraction is an important while challenging task in information extraction. We find that existing solutions can hardly extract correct relation when the sentence is long and complex or the firsthand trigger word does not show. Inspired by the idea of fusing more and deeper information, we present a new relation extraction method that involves the types of entities in the joint embedding, namely, Entity Type Embedding (ETE). An architecture of Recurrent Piecewise Residual Networks (RPRN) is also proposed to cooperate with the joint embedding so that the relation extractor acquires the latent representation underlying the context of a sentence. We validate our method by experiments on public data set of New York Times. Experiment results show that our method outperforms the state-of-the-art models.