聊天机器人用户界面的客户关系管理使用NLP模型

Jash Doshi
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引用次数: 6

摘要

NLP是研究最多的领域。语音-文本转换、假新闻检测和文本摘要是自然语言处理的热点。聊天机器人用户界面(UI)使用NLP,使机器更好地了解客户。其目的是使用不同的NLP和机器学习技术,并添加ChatBot UI来指导客户或客户通过CRM软件,并在他们遇到困难时提供帮助。不同的方法、库和算法,如“RASA”、python的“Chatterbot”、“余弦相似性”和谷歌的“嵌入器”,被用来训练模型,然后进行比较,看看哪个能给出最好的结果。之后,在部署过程中尝试了另外两种方法,一种是从数据库中获取问题然后训练模型,另一种是维护一个本地文本文档并从中训练模型。还讨论了每种方法的优缺点,以及面临的挑战和更好的部署方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chatbot User Interface for Customer Relationship Management using NLP models
NLP is the most researched field. Speech-totext conversions, fake-news detection, and text summarization are the hot topics of NLP. ChatBot User Interface(UI) using NLP, allows machines to understand customers better. The aim was to use different NLP and machine learning techniques and to add ChatBot UI to guide customers or clients through the CRM software and help them whenever they get stuck. Different approaches, libraries, and algorithms like 'RASA', python's 'Chatterbot', 'Cosine similarity', and Google's embedder were used to train the model and then later compared to see which gave the best results. After that, during the deployment other 2 approaches were tried, one was fetching questions from the database and then training the model, the other was to maintain a local text document and train the model from that. The advantages and disadvantages of each approach, plus challenges and better methods for deployment is also discussed.
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