捕捉客户来电意图信息,开展客户来电意图识别研究具有重要意义。现有的客户来电意图识别大都是采用人工分析方法,尚没有采用机器学习、深度学习模型识别客户来电意图的研究。为降低人工分析代价,提高客户来电意图识别结果,该文分别从基于传统机器学习模型、基于单/多深度学习模型、基于BERT和深度学习模型组合三个方面,进行客户来电意图识别研究。在移动客服领域客户来电数据上的实验结果显示,F1值最高达到86.30%,说明该文提出的客户来电意图识别方法是有效的,能够有效帮助移动客服人员进行客户来电意图识别分析。
Abstract
It is of great significance to capture the intention of customer's call. Besides the existing manual intention detection, so far, there is no public report on the intention detection of customer's call via machine learning or deep learning models. This paper proposes three intention detection methods based on classical machine learning model, on single/multiple deep learning model, and on the combination of BERT and deep learning model, respectively. The experiments on mobile customer service corpus show that the top F1-value reaches 86.30%.
关键词
意图识别 /
机器学习 /
深度学习 /
BERT
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Key words
intention detection /
machine learning /
deep learning /
BERT
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参考文献
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脚注
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基金
国家863计划(2015AA015404)
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