Natural Language Processing

Catalog description

This course explores the computational techniques that enable machines to interpret, generate, and interact with human language. Students will examine both the foundational linguistics and the advanced algorithms that power modern Natural Language Processing (NLP) systems, from rule-based parsing to large-scale transformer models. Topics include text preprocessing, tokenization, word embeddings, part-of-speech tagging, named entity recognition, syntactic parsing, sentiment analysis, and machine translation. The course covers classic models such as Hidden Markov Models (HMMs) and n-gram language models, as well as deep learning-based approaches including Recurrent Neural Networks (RNNs), sequence-to-sequence models, and transformer architectures. Students will apply techniques in real-world scenarios such as chatbots, summarization, text classification, information retrieval, and question answering. Projects emphasize both implementation and evaluation, with attention to ethical concerns such as bias, misinformation, and the responsible use of generative language models.