论文标题
客户服务中的自然语言处理：系统评价
Natural Language Processing in Customer Service: A Systematic Review
论文作者
论文摘要
人工智能和自然语言处理（NLP）越来越多地用于客户服务与用户互动并回答他们的问题。该系统评价的目的是研究有关在客户服务中使用NLP技术的现有研究，包括研究领域，应用程序，所使用的数据集和评估方法。该评论还着眼于该领域的未来方向和任何重大局限性。该评论涵盖了2015年至2022年的时间段，其中包括来自五个主要科学数据库的论文。聊天机器人和提问系统被发现在10个主要领域中使用，其中最常见的是社交网络和电子商务领域。 Twitter是第二常用的数据集，大多数研究还使用其自己的原始数据集。准确性，精度，召回和F1是最常见的评估方法。未来的工作旨在提高对用户行为和情感的性能和理解，并解决诸如数据集的数量，多样性和质量之类的限制。这篇评论包括对不同语言，模型和技术的研究。
Artificial intelligence and natural language processing (NLP) are increasingly being used in customer service to interact with users and answer their questions. The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods. The review also looks at the future direction of the field and any significant limitations. The review covers the time period from 2015 to 2022 and includes papers from five major scientific databases. Chatbots and question-answering systems were found to be used in 10 main fields, with the most common use in general, social networking, and e-commerce areas. Twitter was the second most commonly used dataset, with most research also using their own original datasets. Accuracy, precision, recall, and F1 were the most common evaluation methods. Future work aims to improve the performance and understanding of user behavior and emotions, and address limitations such as the volume, diversity, and quality of datasets. This review includes research on different spoken languages and models and techniques.
