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Description
Module Content
In this seminar series we will take a tour in the field of NLP, and link it to the study of linguistics. We will start with an overview: we will compare NLP with linguistics and computational linguistics, and familiarize ourselves with the key concepts of machine learning and deep learning. The bulk of the series will be devoted to a few core topics. We will look at language modelling, syntactic and semantic parsing, Automatic speech recognition and text to speech, dialogue systems and other NLP applications in the real world.
Teaching Delivery
Students attend a weekly lecture and additionally work with tutors to develop skills and work on projects.
Indicative Topics
1. NLP and Computational Linguistics: an overview
2. Key concepts in machine learning and deep learning
3. Core topics in NLP: language modelling
4. Core topics in NLP: syntactic and semantic parsing
5. Core topics in NLP: ASR (automatic speech recognition) and TTS (text to speech)
6. Core topics in NLP: dialogue systems and other NLP applications in the real world
7. Design your own NLP project
Module Aims and/or Objectives
Module aims: To develop an understanding of how key functions are realised in NLP, and how concepts in linguistics figure in the development of final applications.
Module outcomes:
To deepen understanding of how NLP applications can be developed
To widen skill set, including practical coding skills, to complete an NLP project
To bring together knowledge formed in linguistics modules with knowledge about NLP applications.
To read and write coding languages
To work with abstract and formal systems
To break down large projects into manageable pieces
To approach problems critically and logically
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Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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