基于Fasttext网络的煤矿事故案例文本分类方法对比

doi:10.11799/ce202111035

Abstract

Abstract:

With the rapid development of the big data, coal mining enterprises to accumulate a large amount of coal mine data resources. Among them, the coal mine accident case information is rich, including the analysis and summary of the time of the accident, the course of the accident, the cause of the accident, and the accident report. However, it is difficult to extract unstructured data information, and the implicit semantic features cannot be effectively obtained. Therefore, this paper focuses on coal mine accident cases and compares the text representation and classification methods based on Fasttext network, to better mine the semantic information in the text, and then accurately and effectively predict the category of the case accident. Provide strong technical support for the subsequent establishment of expert knowledge base and construction of emergency rescue platform. All the code for the experiment in this paper has been placed on GitHub.

CLC Number:

TD-05
TP39

References

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Comparison of text classification methods of coal mine accident cases based on Fasttext network

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