Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction (Student Abstract)

Long Bai, Xiaolong Jin, Chuanzhi Zhuang, Xueqi Cheng

AAAI 2020 pp. 13751-13752

doi:10.1609/AAAI.V34I10.7147 /aaai/2020/bai2020aaai-entity/

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.

PDF AAAI Semantic Scholar

Cite

Text

Bai et al. "Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7147

Markdown

[Bai et al. "Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/bai2020aaai-entity/) doi:10.1609/AAAI.V34I10.7147

BibTeX

@inproceedings{bai2020aaai-entity,
  title     = {{Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction (Student Abstract)}},
  author    = {Bai, Long and Jin, Xiaolong and Zhuang, Chuanzhi and Cheng, Xueqi},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13751-13752},
  doi       = {10.1609/AAAI.V34I10.7147},
  url       = {https://mlanthology.org/aaai/2020/bai2020aaai-entity/}
}