Effective Piecewise CNN with attention mechanism for distant supervision on relation extraction task

Yuming Li, Pin Ni, Gangmin Li, Victor Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
7 Downloads (Pure)

Abstract

Relation Extraction is an important sub-task in the field of information extraction. Its goal is to identify entities from text and extract semantic relationships between entities. However, the current Relationship Extraction task based on deep learning methods generally have practical problems such as insufficient amount of manually labeled data, so training under weak supervision has become a big challenge. Distant Supervision is a novel idea that can automatically annotate a large number of unlabeled data based on a small amount of labeled data. Based on this idea, this paper proposes a method combining the Piecewise Convolutional Neural Networks and Attention mechanism for automatically annotating the data of Relation Extraction task. The experiments proved that the proposed method achieved the highest precision is 76.24% on NYT-FB (New York Times-Freebase) dataset (top 100 relation categories). The results show that the proposed method performed better than CNN-based models in most cases.

Original languageEnglish
Title of host publicationCOMPLEXIS 2020 - Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk
EditorsReinhold Behringer, Victor Chang
PublisherSciTePress
Pages53-62
Number of pages10
ISBN (Electronic)9789897584275
Publication statusPublished - 8 May 2020
Event5th International Conference on Complexity, Future Information Systems and Risk - Virtual, Online
Duration: 8 May 20209 May 2020

Publication series

NameCOMPLEXIS 2020 - Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk

Conference

Conference5th International Conference on Complexity, Future Information Systems and Risk
Abbreviated titleCOMPLEXIS 2020
CityVirtual, Online
Period8/05/209/05/20

Bibliographical note

Funding Information:
We are grateful to VC Research (Funding No. VCR 0000040) to support this work.

Publisher Copyright:
© 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

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