Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction

6Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

Abstract

Distantly Supervised Relation Extraction (DSRE) has been widely studied, since it can automatically extract relations from very large corpora. However, existing DSRE methods only use little semantic information about entities, such as the information of entity type. Thus, in this paper, we propose a method for integrating entity type information into a neural network based DSRE model. It also adopts two attention mechanisms, namely, sentence attention and type attention. The former selects the representative sentences for a sentence bag, while the latter selects appropriate type information for entities. Experimental comparison with existing methods on a benchmark dataset demonstrates its merits.

Cite

CITATION STYLE

APA

Bai, L., Jin, X., Zhuang, C., & Cheng, X. (2020). Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (pp. 13751–13752). AAAI press.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free