Open AccessText Classification of Potential Dangers in Coal Mine Safety Based on Convolutional Neural Network
Author(s) -
Na Liu,
Yanzhu Hu,
Xinbo Ai,
Yanchao Shao
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/300/2/022130
Subject(s) - word2vec , convolutional neural network , computer science , coal mining , artificial intelligence , deep learning , artificial neural network , machine learning , traffic classification , data mining , coal , engineering , computer security , embedding , network packet , waste management
In recent years, with the improvement of people’s safety awareness and the steady progress of safety production supervision, text classification algorithm based on data mining has been widely applied. At present, for the classification of hidden danger text in coal mine, it mainly relies on manual or machine learning. The efficiency of manual classification is too inefficient to meet the requirements of massive text classification. And the accuracy of machine learning-based classification method is low. In view of the above problems, this paper combines Word2vec and convolutional neural network to achieve accurate classification of hidden danger text in coal mine safety, and achieves great results. The results show that Word2vec can retain the semantic information between contexts. Convolutional neural network can effectively extract the high-level features of local contexts, and the classification effect is more accurate. This method can be implemented in the classification of hidden danger text in coal mine, which has very important practical significance.