基于深度学习的阿姆哈拉语问答聊天机器人
Deep Learning Based Amharic Chatbot for FAQs in Universities
AI总结：
本文提出一个基于深度学习的阿姆哈拉语聊天机器人，用于回答大学常见问题，采用自然语言处理技术，通过SVM、朴素贝叶斯和深度神经网络实现高准确率的问答。
摘要：
大学学生常花费大量时间向管理员或教师寻求常见问题的答案，这给双方都带来了麻烦。本文提出一个利用自然语言处理和深度学习技术的聊天机器人，用于回答阿姆哈拉语中的常见问题。聊天机器人通过分词、标准化、停用词去除和词干提取分析和分类阿姆哈拉语输入句子。三种机器学习模型算法用于分类令牌并检索合适响应：支持向量机（SVM）、多项式朴素贝叶斯和通过TensorFlow、Keras和NLTK实现的深度神经网络。深度学习模型在使用Adam优化器和SoftMax激活函数时，达到91.55%的准确率和0.3548的验证损失。聊天机器人与Facebook Messenger集成，并部署在Heroku服务器上，实现24小时可用性。实验结果表明，聊天机器人框架实现了其目标，有效解决了阿姆哈拉语的词形变体、形态变体和词汇缺口问题。未来研究可以探索整合阿姆哈拉语WordNet以缩小词汇缺口并支持更复杂的问题。
英文摘要：
University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer programs that simulate human conversation through the use of artificial intelligence (AI), acting as a virtual assistant to handle questions and other tasks. The proposed chatbot program employs tokenization, normalization, stop word removal, and stemming to analyze and categorize Amharic input sentences. Three machine learning model algorithms were used to classify tokens and retrieve appropriate responses: Support Vector Machine (SVM), Multinomial Naïve Bayes, and deep neural networks implemented through TensorFlow, Keras, and NLTK. The deep learning model achieved the best results with 91.55% accuracy and a validation loss of 0.3548 using an Adam optimizer and SoftMax activation function. The chatbot model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility. The experimental results demonstrate that the chatbot framework achieved its objectives and effectively addressed challenges such as Amharic Fidel variation, morphological variation, and lexical gaps. Future research could explore the integration of Amharic WordNet to narrow the lexical gap and support more complex questions.
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。
