Natural language processing

Module title:

Natural language processing

Module overview:

This module is designed to equip students with the knowledge and skills necessary to understand, implement, and evaluate algorithms and techniques for processing and analyzing human language text data. Main goals of the course are:
Understand the fundamental concepts and techniques of Natural Language Processing (NLP).
Learn about the different tasks and applications of NLP, including text classification, sentiment analysis, and machine translation.
Develop practical skills in implementing NLP algorithms and models.
Explore advanced topics in NLP, such as deep learning for NLP, semantic analysis, and dialogue systems.

In this module students will:
Learn about the fundamental concepts and techniques of Natural Language Processing (NLP), including tokenization, stemming, and lemmatization.
Explore various NLP tasks and applications, such as text classification, named entity recognition, and sentiment analysis.
Gain hands-on experience in implementing NLP algorithms and models.
Study advanced topics in NLP, such as deep learning for NLP, sequence-to-sequence models, and neural language models.
Investigate real-world NLP applications and case studies across different domains.

Literature:

Required readings:
1. Jurafsky, D., Martin, J. H. (2019.), Speech and Language Processing (3nd edition), Prentice Hall

Supplementary readings:
1. Zhang, Y., and Teng, Z. (2021). Natural language processing: A machine learning perspective. Cambridge University Press.
2. Kulkarni, A., and Shivananda, A. (2021). Natural language processing projects: Build next-generation NLP applications using AI techniques (1st ed.). APress.
3. Bird, S., Klein, E., and Loper, E. (2014). Natural language processing with python. O’Reilly Media.