Abstract
Relation extraction studies the issue of predicting
semantic relations between pairs of entities in sentences. Attention mechanisms are often used in
this task to alleviate the inner-sentence noise by
performing soft selections of words independently.
Based on the observation that information pertinent to relations is usually contained within segments (continuous words in a sentence), it is possible to make use of this phenomenon for better
extraction. In this paper, we aim to incorporate
such segment information into neural relation extractor. Our approach views the attention mechanism as linear-chain conditional random fields over
a set of latent variables whose edges encode the desired structure, and regards attention weight as the
marginal distribution of each word being selected
as a part of the relational expression. Experimental
results show that our method can attend to continuous relational expressions without explicit annotations, and achieve the state-of-the-art performance
on the large-scale TACRED dataset