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Authors: Yuming Li 1 ; Pin Ni 1 ; Gangmin Li 2 and Victor Chang 3

Affiliations: 1 University of Liverpool, U.K. ; 2 Xi’an Jiaotong-Liverpool University, China ; 3 Teesside University, U.K.

Keyword(s): Relation Extraction, Distant Supervision, Piecewise Convolutional Neural Networks, Attention, Convolutional Neural Networks.

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. (More)

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Paper citation in several formats:
Li, Y.; Ni, P.; Li, G. and Chang, V. (2020). Effective Piecewise CNN with Attention Mechanism for Distant Supervision on Relation Extraction Task. In Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-427-5; ISSN 2184-5034, SciTePress, pages 53-60. DOI: 10.5220/0009582700530060

@conference{complexis20,
author={Yuming Li. and Pin Ni. and Gangmin Li. and Victor Chang.},
title={Effective Piecewise CNN with Attention Mechanism for Distant Supervision on Relation Extraction Task},
booktitle={Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2020},
pages={53-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009582700530060},
isbn={978-989-758-427-5},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - Effective Piecewise CNN with Attention Mechanism for Distant Supervision on Relation Extraction Task
SN - 978-989-758-427-5
IS - 2184-5034
AU - Li, Y.
AU - Ni, P.
AU - Li, G.
AU - Chang, V.
PY - 2020
SP - 53
EP - 60
DO - 10.5220/0009582700530060
PB - SciTePress

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