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Ticketing Chatbot Service using Serverless NLP Technology

机译:使用无服务器NLP技术的票务聊天机器人服务

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摘要

Personal assistant using a human operator need some time to process single request such as ticket booking, ordering something, and get services. One request can contain many queries for some information provided on the internet. Business performance values time efficiency so must be considered an alternative way to take request. Chatbot can give 24 hours service which can become an advantage besides using a human personal assistant. Chatbot acts like routing agent that can classify user context in conversation. Chatbot helped with natural language processing (NLP) to analyze the request and extract some keyword information. One important process in NLP is morphological analysis and part of speech (POS) tagging. POS help to parse the meaning of chat text based on a set of rules. The rule base is specific to some language and designed to capture all the keyword relies on chat text. Keyword in booking conversation term is like departure and destination city and also the date of flight. There is a variation from a user determining city and date. NLP in booking confirmation has a task to analyze various pattern describing ordering requests like city and date. Messenger bot would be an example of assistance that can help user connected to many services some like ticketing service through conversation interaction. The contribution of this research is to conduct some scenario that happening in ordering tickets. This research conduct that chatbot can help acts as customer service, based on the conducted scenario and show an F-measure score of 89.65%.
机译:使用人工操作员的私人助理需要一些时间来处理单个请求,例如机票预订,订购某些物品并获得服务。一个请求可以包含许多对Internet上提供的某些信息的查询。业务绩效重视时间效率,因此必须考虑采用另一种方式提出请求。 Chatbot可以提供24小时服务,这除了使用人工助手之外,还可以成为一项优势。 Chatbot就像路由代理一样,可以在对话中对用户上下文进行分类。 Chatbot帮助自然语言处理(NLP)分析请求并提取一些关键字信息。 NLP中的一个重要过程是形态分析和语音部分(POS)标记。 POS帮助根据一组规则来解析聊天文本的含义。规则库特定于某些语言,旨在捕获所有依赖于聊天文本的关键字。预订对话条款中的关键字类似于出发地和目的地城市,以及航班日期。用户确定城市和日期与实际情况有所不同。预订确认中的NLP的任务是分析描述订购请求的各种模式,例如城市和日期。 Messenger bot将是帮助的一个示例,它可以帮助用户通过对话交互连接到许多服务,例如票务服务。这项研究的贡献在于完成了一些在订购机票时发生的情况。根据所进行的场景,该聊天机器人可以帮助充当客户服务的研究行为,并显示F-measure分数为89.65%。

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