个人理财问题中的主题检测
Towards Theme Detection in Personal Finance Questions
摘要：
银行业呼叫中心每年接到数百万个电话，这些电话中的大部分信息无法被有兴趣跟踪新的和新兴的呼叫中心趋势的分析师所获得。在这项研究中，我们提出了一种呼叫中心主题检测的方法，捕捉在一个问题中的多个主题的发生，使用公开的语料库的StackExchange个人财务问题，由用户与主题标签标记，作为一个测试床。为了捕捉单个问题中多个主题的出现，该方法在句子而不是问题级别进行编码和聚类。我们还提出了一个比较国家的最先进的句子编码模型，包括SBERT家庭的句子编码器。我们框架我们的评估作为一个多类分类任务，并表明，一个简单的组合的原始句子文本，通用句子编码器，和KMeans优于更复杂的技术，涉及语义解析，SBERT家庭模型，和HDBSCAN。我们的最高性能的方法实现了0.46的微F1的这项任务，我们表明，所产生的集群，即使在轻微的噪音，包含句子的主题与标签相关联的集群。
英文摘要：
Banking call centers receive millions of calls annually, with much of the information in these calls unavailable to analysts interested in tracking new and emerging call center trends. In this study we present an approach to call center theme detection that captures the occurrence of multiple themes in a question, using a publicly available corpus of StackExchange personal finance questions, labeled by users with topic tags, as a testbed. To capture the occurrence of multiple themes in a single question, the approach encodes and clusters at the sentence- rather than question-level. We also present a comparison of state-of-the-art sentence encoding models, including the SBERT family of sentence encoders. We frame our evaluation as a multiclass classification task and show that a simple combination of the original sentence text, Universal Sentence Encoder, and KMeans outperforms more sophisticated techniques that involve semantic parsing, SBERT-family models, and HDBSCAN. Our highest performing approach achieves a Micro-F1 of 0.46 for this task and we show that the resulting clusters, even when slightly noisy, contain sentences that are topically consistent with the label associated with the cluster.
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。
