Automatic classification method of coal mine safety hidden danger informatio
Manual classification method is difficult to meet classification requirements of massive coal mine safety hidden danger information, and automatic text classification method based on probability statistics has low classification accuracy rate. In view of the above problems, an automatic classificati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2018-10-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018050019 |
| _version_ | 1797869009405739008 |
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| author | XIE Binhong MA Fei PAN Lihu ZHANG Yingjun |
| author_facet | XIE Binhong MA Fei PAN Lihu ZHANG Yingjun |
| author_sort | XIE Binhong |
| collection | DOAJ |
| description | Manual classification method is difficult to meet classification requirements of massive coal mine safety hidden danger information, and automatic text classification method based on probability statistics has low classification accuracy rate. In view of the above problems, an automatic classification method of coal mine safety hidden danger information was proposed which was based on Word2vec and convolutional neural network. Firstly, hidden danger information is pre-processed through word segmentation and stop word deletion. Then semantic similarity between words is represented by employing Word2vec. Finally, local context high-level features of hidden danger information are extracted by use of convolutional neural network, and Softmax classifier is used to realize automatic classification of hidden danger information. The experimental results show that the method realizes end-to-end automatic classification and can effectively improve accuracy and comprehensiveness of classification. |
| first_indexed | 2024-04-10T00:04:49Z |
| format | Article |
| id | doaj.art-b5b2bc00d92f4969bb378796e23bf204 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:04:49Z |
| publishDate | 2018-10-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-b5b2bc00d92f4969bb378796e23bf2042023-03-17T01:19:03ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2018-10-014410101410.13272/j.issn.1671-251x.2018050019Automatic classification method of coal mine safety hidden danger informatioXIE Binhong0MA Fei1PAN LihuZHANG Yingjun2Department of Computer Science and Technology, Taiyuan University of Science and Technology,Taiyuan 030024, ChinaDepartment of Computer Science and Technology, Taiyuan University of Science and Technology,Taiyuan 030024, ChinaDepartment of Computer Science and Technology, Taiyuan University of Science and Technology,Taiyuan 030024, ChinaManual classification method is difficult to meet classification requirements of massive coal mine safety hidden danger information, and automatic text classification method based on probability statistics has low classification accuracy rate. In view of the above problems, an automatic classification method of coal mine safety hidden danger information was proposed which was based on Word2vec and convolutional neural network. Firstly, hidden danger information is pre-processed through word segmentation and stop word deletion. Then semantic similarity between words is represented by employing Word2vec. Finally, local context high-level features of hidden danger information are extracted by use of convolutional neural network, and Softmax classifier is used to realize automatic classification of hidden danger information. The experimental results show that the method realizes end-to-end automatic classification and can effectively improve accuracy and comprehensiveness of classification.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018050019coal mine safetyautomatic classification of hidden danger informationtext classificationconvolutional neural networkword2vec |
| spellingShingle | XIE Binhong MA Fei PAN Lihu ZHANG Yingjun Automatic classification method of coal mine safety hidden danger informatio Gong-kuang zidonghua coal mine safety automatic classification of hidden danger information text classification convolutional neural network word2vec |
| title | Automatic classification method of coal mine safety hidden danger informatio |
| title_full | Automatic classification method of coal mine safety hidden danger informatio |
| title_fullStr | Automatic classification method of coal mine safety hidden danger informatio |
| title_full_unstemmed | Automatic classification method of coal mine safety hidden danger informatio |
| title_short | Automatic classification method of coal mine safety hidden danger informatio |
| title_sort | automatic classification method of coal mine safety hidden danger informatio |
| topic | coal mine safety automatic classification of hidden danger information text classification convolutional neural network word2vec |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018050019 |
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