通过拓扑自然语言分析揭示客户问题
Uncovering Customer Issues through Topological Natural Language Analysis
AI总结：
针对电商客服中具体问题难以快速识别的难题，提出融合自然语言技术与拓扑数据分析的端到端深度学习框架，通过句子标注、嵌入白化与无向图拓扑特征检测新兴和趋势性客户问题，且结果与新闻来源高度一致。
摘要：
电子商务公司每天要处理大量客户服务请求。虽然通常使用简单的标注系统来概括客户联系的主题，但深入探究每一个具体问题可能颇具挑战。这带来了一个关键关切，尤其是在突发事件期间，公司必须迅速识别并解决具体问题。为应对这一挑战，我们提出了一种新颖的机器学习算法，利用自然语言技术和拓扑数据分析来监控新兴及趋势性客户问题。我们的方法包含一个端到端深度学习框架，可同时标注每位客户对话记录中的主要问题句并生成句子嵌入向量。随后，我们对嵌入向量进行白化处理，并用其构建无向图。在此基础上，我们依据每份对话记录的拓扑属性定义趋势性问题和新兴问题。我们通过多种方法验证了结果，发现其与新闻来源高度一致。
英文摘要：
E-commerce companies deal with a high volume of customer service requests daily. While a simple annotation system is often used to summarize the topics of customer contacts, thoroughly exploring each specific issue can be challenging. This presents a critical concern, especially during an emerging outbreak where companies must quickly identify and address specific issues. To tackle this challenge, we propose a novel machine learning algorithm that leverages natural language techniques and topological data analysis to monitor emerging and trending customer issues. Our approach involves an end-to-end deep learning framework that simultaneously tags the primary question sentence of each customer's transcript and generates sentence embedding vectors. We then whiten the embedding vectors and use them to construct an undirected graph. From there, we define trending and emerging issues based on the topological properties of each transcript. We have validated our results through various methods and found that they are highly consistent with news sources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
