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CM52042: Natural language processing

[Page last updated: 22 April 2025]

Academic Year: 2025/26
Owning Department/School: Department of Computer Science
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Masters UG & PG (FHEQ level 7)
Period:
Academic Year
Assessment Summary: CWPI 70%, CWSI 30%
Assessment Detail:
  • Set exercises Individual (CWSI 30%)
  • Project output Individual (CWPI 70%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: In taking this module you cannot take CM32030 OR take CM32030
Before taking this module you must take CM22009 OR take CM20315
Learning Outcomes: 1. Demonstrate knowledge of the fundamental principles of natural language processing. 2. Demonstrate understanding of key algorithms for natural language processing. 3. Write programs that process language. 4. Evaluate the performance of programs that process language. 5. Critically evaluate the strengths and weaknesses of various algorithms for natural language processing.


Synopsis: Introduces the theory and practice of natural language processing, which is the basis for information retrieval (search engines), speech recognition, chat bots, virtual assistants (such as Siri), summarisation, translation, and the analysis of large bodies of text (such as social media).

Content: Topics covered by this unit will typically include language models, word embeddings, topic models, part-of-speech tagging, named entity recognition, parsing, information extraction, information retrieval, text classification, speech recognition, sentiment analysis, machine translation.

Course availability:

CM52042 is Optional on the following courses:

Department of Computer Science
  • USCM-AFM27 : MComp(Hons) Computer Science and Artificial Intelligence (Year 4)
  • USCM-AAM27 : MComp(Hons) Computer Science and Artificial Intelligence with Study year abroad (Year 5)
  • USCM-AKM27 : MComp(Hons) Computer Science and Artificial Intelligence with Year long work placement (Year 5)

Notes:

  • This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
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