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：大学学生通常需要花费大量时间从行政人员或教师那里寻找常见问题的答案。这对双方而言都可能变得繁琐，因此需要一种解决方案。为此，本文提出一种利用自然语言处理与深度学习技术的问答机器人模型，用于回答阿姆哈拉语的常见问题。问答机器人是一种通过人工智能模拟人类对话的计算机程序，可作为虚拟助手处理问题及其他任务。所提出的问答机器人程序采用分词、标准化、停用词移除和词干提取等技术分析和分类阿姆哈拉语输入语句。本研究使用了三种机器学习模型算法对词元进行分类并检索相应回答：支持向量机、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，使用Adam优化器和SoftMax激活函数达到91.55%的准确率，验证损失为0.3548。该问答机器人模型与Facebook Messenger集成，并部署于Heroku服务器以实现全天候访问。实验结果表明，该问答机器人框架实现了既定目标，有效解决了阿姆哈拉语Fidel字符变体、形态变化及词汇空缺等挑战。未来研究可探索整合阿姆哈拉语WordNet以缩小词汇空缺并支持更复杂的问题。