Natural Language Processing enables computers to understand and reason about human languages such as English and has resulted in many exciting technologies such as conversational assistants, machine translation and (intelligent) internet search. This module would provide the theoretical foundations of NLP as well as applied techniques for extracting and reasoning about information from text.
The module explores three major themes: Computational Models of human cognition such as memory, attention and psycholinguisticsSymbolic AI methods for processing language such as automated reasoning, planning, parsing of grammar, and conversational systems.
Statistical Models of Language including the use of machine learning to infer structure and meaning.
Learning Outcomes
By the end of the module students should be able to:
Demonstrate an understanding of the major topics in Natural Language Processing
Understand the role of machine learning techniques in widening the coverage of NLP systems
Demonstrate an ability to apply knowledge-based and statistical techniques to real-world NLP problems
Demonstrate the capacity to independently study, understand, and critically evaluate advanced materials or research articles in the subject areas covered by this module
