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.

翻译：大学生常常需要花费大量时间向行政人员或教师寻求常见问题的答案。这对双方来说都可能变得繁琐，因此需要一种解决方案。为此，本文提出了一种利用自然语言处理和深度学习技术，以阿姆哈拉语回答常见问题（FAQ）的聊天机器人模型。聊天机器人是通过人工智能（AI）模拟人类对话的计算机程序，充当虚拟助手来处理问题和其他任务。所提出的聊天机器人程序采用分词、归一化、停用词去除和词干提取技术来分析和分类阿姆哈拉语的输入句子。实验采用了三种机器学习模型算法用于分类分词并检索适当的回复：支持向量机（SVM）、多项朴素贝叶斯以及通过TensorFlow、Keras和NLTK实现的深度神经网络。深度学习模型取得了最佳效果，在使用Adam优化器和SoftMax激活函数时，准确率达到91.55%，验证损失为0.3548。该聊天机器人模型与Facebook Messenger集成，并部署在Heroku服务器上以实现24小时可访问性。实验结果表明，该聊天机器人框架实现了其目标，并有效解决了阿姆哈拉语字符变化、形态变化和词汇缺口等挑战。未来的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。