基于深度神经网络的人工智能聊天机器人开发

Dammavalam Srinivasa Rao, K. Lakshman Srikanth, J. Noshitha Padma Pratyusha, M. Sucharitha, M. Tejaswini, T. Ashwini
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引用次数: 1

摘要

无论多知名的大学,在申请过程中,甚至在被录取后,人们总会有一些担忧。学院举办各种各样的活动,从部门活动到俱乐部活动。并不是每个人都知道所有的事件。聊天机器人在人和信息之间架起了桥梁。世界变得越来越自动化,人们期望服务也变得越来越自动化。聊天机器人是一种软件,它可以回答用户的问题并提供知识库中的信息。这个项目的目的是为VNRVJIET创建一个聊天机器人,它将回答有关测试、部门活动、事件、俱乐部、基础设施、安置数据、入学程序等方面的问题。所提出的方法包括使用深度神经网络和语音识别功能构建的聊天机器人。使用所提出的方法,以语音和文本两种方式传递信息。数据收集和初始格式化为JSON格式。对准备好的数据进行预处理,然后应用词包算法对其进行处理。词包算法是目前最具影响力的对象分类方法。使用该算法的关键方面是将单词向量转换为数值数据集,以便机器进行更深入的分析。利用张量流API构建深度神经网络,定义语音识别函数,实现输入查询和输出响应。最后,定义了chatbot函数,并利用它为任何给定查询生成响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Artificial Intelligence based Chatbot Using Deep Neural Network
No matter how well-known colleges are, there will always be concerns that people have during the application process and even after they have been accepted. The college hosts a variety of events, ranging from departmental activities to club activities. Not everyone is likely aware of all events. Chatbot bridges gap between people and information. The world is becoming more automated, and people expect services to become more automated as well. A chatbot is software that responds to user questions and provides information from a knowledge base. The purpose of this project is to create a chatbot for VNRVJIET that will answer queries raised about fests, departmental activities, events, clubs, infrastructure, placement data, admission procedure, and others. The proposed methodology consists of a chatbot built using Deep Neural Networks and speech recognition capabilities. The information is delivered in both speech and text modes using the proposed methodology. Data is collected and formatted in JSON format initially. The prepared data is preprocessed and then the bag of words algorithm is applied to it. The bag of words algorithm is most influential method for object categorization. The key aspect of using this algorithm is for converting the word vector to a numerical data set for machine to do a deeper analysis. A deep neural network is created using tensor flow API, and the speech recognition function is defined for the input query and output response. Finally, chatbot function is defined and utilized for generating responses for any given query.
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