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Description
Module Content
The module introduces students to core concepts in computational linguistics and provides a comprehensive introduction to the Python programming language. It discusses foundational issues such as the representation of linguistic structure and probability theory. Students gain hands-on experience implementing formal theories and working with probabilistic models such as n-grams and Hidden Markov Models.
Teaching Delivery
120-minute lectures are presented every week with weekly 60-minute tutorials devoted to data analysis and discussion.
Indicative Topics
1. Basics of programming and the Python programming language
2. First-order logic and probability theory
3. Formal linguistic models such as the Rational Speech Act model or probabilistic grammars
4. n-gram models and Hidden Markov Models
5. Machine learning classifiers
Module Aims and/or Objectives
Module aims: To develop an understanding of core computational linguistics concepts and models.
Module outcomes:
To develop understanding of how linguistic theories can be implemented using computational models
To learn about core concepts in formal logic and probability theory
To read and write programming languages
To bring together knowledge formed in linguistics modules with knowledge about core computer science concepts.
To work with abstract and formal systems
To approach problems critically and logically
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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