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.

电子商务公司关注顾客服务请求，提出了一种新型的机器学习算法，利用自然语言技术和拓扑数据分析监控顾客问题的新兴和趋势，验证结果与新闻来源高度一致。

通过拓扑自然语言分析揭示客户问题

Uncovering Customer Issues through Topological Natural Language Analysis

Advanced machine learning and natural language techniques enable attackers to
launch sophisticated and targeted social engineering-based attacks. To counter
the active attacker issue, researchers have since resorted to proactive methods
of detection. Email masquerading using targeted emails to fool the victim is an
advanced attack method. However automatic text generation requires controlling
the context and coherency of the generated content, which has been identified
as an increasingly difficult problem. The method used leverages a hierarchical
deep neural model which uses a learned representation of the sentences in the
input document to generate structured written emails. We demonstrate the
generation of short and targeted text messages using the deep model. The global
coherency of the synthesized text is evaluated using a qualitative study as
well as multiple quantitative measures.

使用层次化深度神经模型生成有结构的邮件以应对基于高级机器学习和自然语言技术的社交攻击的检测问题。