案源线索管理是工商行政执法办案的初始环节。随着网络举报途径的简化,案源线索的数量激增,现有的人工对案源线索进行分派处理的方式存在压力大、错误率高、人工成本高等种种弊端。为了降低人工成本、提高案源线索分类的准确率,该文以某一线城市的案源线索数据为例,探索基于深度学习模型的分类算法,来实现违法种类的自动识别。经过模型选择和实证研究,发现所提算法的总体分类准确率较高,能够满足实际的业务需求。本研究的成果表明了基于深度学习模型的分类器,可以有效地实现案源线索的自动分类,为推进社会治理能力的智能化和现代化提供借鉴。
Abstract
Case source clues management is the initial step for industrial and commercial administration and law-enforcement. To deal with the sharp increasing case source clues, this paper explore the deep learning model to realize illegal types automatic recognition. After model selection and empirical research, the overall classification accuracy rate meets actual business needs. The experiment on a first-tier city’s data show that the proposed model can effectively realize the case source clues automatic classification.
关键词
案源线索 /
文本分类 /
BERT模型
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Key words
case source clues /
text classification /
BERT model
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参考文献
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脚注
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基金
国家重点研发计划(2020YFC0833304)
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