Li, Yuming;
Ni, Pin;
Li, Gangmin;
Chang, Victor;
(2020)
Effective Piecewise CNN with Attention Mechanism for Distant Supervision on Relation Extraction Task.
In: Behringer, R and Chang, V, (eds.)
Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS.
(pp. pp. 53-60).
SciTePress
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Effective Piecewise CNN with Attention Mechanism for Distant Supervision on Relation Extraction Task |
| Event: | 5th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS) |
| Location: | Prague, CZECH REPUBLIC |
| Dates: | 8 May 2020 - 9 May 2020 |
| ISBN-13: | 9789897584275 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5220/0009582700530060 |
| Publisher version: | http://dx.doi.org/10.5220/0009582700530060 |
| Language: | English |
| Additional information: | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
| Keywords: | Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Interdisciplinary Applications, Computer Science, Relation Extraction, Distant Supervision, Piecewise Convolutional Neural Networks, Attention, Convolutional Neural Networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159891 |
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