Introduction to Natural Language Processing

Catalog description

Recent developments in large language models, such as ChatGPT, have revolutionized how our society accommodates machine-based reasoning and analysis of text data, which is a prevalent form of unstructured data. This course will examine the ideas behind the implementation of these models. Topics include: basic techniques in text representation such as tokenization, tagging, chunking, co-occurrence and tf-idf; software for text analytics; sentiment analysis, categorization, and visualization of text data; basics of neural networks; word vectors and algorithms of word2vec and GloVe; the family of recurrent neural networks; attention and transformers; natural language generation.