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Relation Extraction for Coal Mine Safety Information Using Recurrent Neural Networks with Bidirectional Minimal Gated Unit
Jian Zhang
Beijing Information Science and Technology University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
The data of coal mine safety field are massive, multi-source and heterogeneous. It is of practical importance to extract information from big data to achieve disaster precaution and emergency response. Existing approaches need to build more features and rely heavily on the linguistic knowledge of researchers, leading to inefficiency, poor portability and slow update speed. This paper proposes a new relation extraction approach using recurrent neural networks with bidirectional minimal gated unit (MGU) model. This is achieved by adding a back-to-front MGU layer on the basis of original MGU model. It does not require to construct complex text features and can capture the global context information by combining the forward and backward features. Evident from extensive experiments, the proposed approach outperforms the existing initiatives in terms of training time, accuracy, recall rate and F value.
Keywords
Relation Extraction, Coal Mine Safety, Recurrent Neural Networks, Bidirectional Minimal Gated Unit
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CURRENT STATUS: Under Review
Version 1
Posted 23 Sep, 2020
No community comments so far
Editorial decision: Major revision
On 12 Nov, 2020
Review #3 received
Received 08 Nov, 2020
Reviewer #3 agreed
On 31 Oct, 2020
Review #2 received
Received 23 Oct, 2020
Review #1 received
Received 20 Oct, 2020
Reviewer #2 agreed
On 25 Sep, 2020
Reviewers invited
Invitations sent on 24 Sep, 2020
Reviewer #1 agreed
On 24 Sep, 2020
Editor assigned
On 23 Sep, 2020
Editor invited
On 22 Sep, 2020
Submission checks complete
On 22 Sep, 2020
First submitted
On 18 Sep, 2020
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Subject Areas
Technical Communication
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This preprint is under consideration at EURASIP Journal on Wireless Communications and Networking. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Relation Extraction for Coal Mine Safety Information Using Recurrent Neural Networks with Bidirectional Minimal Gated Unit
Jian Zhang
Beijing Information Science and Technology University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 23 Sep, 2020
No community comments so far
Editorial decision: Major revision
On 12 Nov, 2020
Review #3 received
Received 08 Nov, 2020
Reviewer #3 agreed
On 31 Oct, 2020
Review #2 received
Received 23 Oct, 2020
Review #1 received
Received 20 Oct, 2020
Reviewer #2 agreed
On 25 Sep, 2020
Reviewers invited
Invitations sent on 24 Sep, 2020
Reviewer #1 agreed
On 24 Sep, 2020
Editor assigned
On 23 Sep, 2020
Editor invited
On 22 Sep, 2020
Submission checks complete
On 22 Sep, 2020
First submitted
On 18 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Technical Communication
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The data of coal mine safety field are massive, multi-source and heterogeneous. It is of practical importance to extract information from big data to achieve disaster precaution and emergency response. Existing approaches need to build more features and rely heavily on the linguistic knowledge of researchers, leading to inefficiency, poor portability and slow update speed. This paper proposes a new relation extraction approach using recurrent neural networks with bidirectional minimal gated unit (MGU) model. This is achieved by adding a back-to-front MGU layer on the basis of original MGU model. It does not require to construct complex text features and can capture the global context information by combining the forward and backward features. Evident from extensive experiments, the proposed approach outperforms the existing initiatives in terms of training time, accuracy, recall rate and F value.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.