Understanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increase in text conversation between customers and agents. In this paper, we propose an approach to data mining the conversation intents behind the textual data. Using the customer service data set, we train unsupervised text representation models, and then develop an intent mapping model which would rank the predefined intents base on cosine similarity between sentences and intents. Topic-modeling techniques are used to define intents and domain experts are also involved to interpret topic modelling results. With this approach, we can get a good understanding of the user intentions behind the unlabelled customer service textual data.

翻译：由于不同文化和教育背景的人对互联网的使用急剧增加,了解客户与客户服务代理商之间聊天的意图已成为当今一个关键问题。更重要的是,电子商务的爆炸导致客户与代理商之间文本对话大量增加。在本文中,我们提议了一种方法来对文本数据背后的谈话意图进行数据挖掘。利用客户服务数据集,我们培训不受监督的文本表述模型,然后开发一种意图映射模型,根据判决与意图的相似性对预先界定的意图基础进行排序。使用专题模型技术来界定意图,并让域专家也参与解释专题建模结果。通过这种方法,我们可以很好地了解未贴标签的客户服务文本数据背后的用户意图。