CM32030: 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: | Honours (FHEQ level 6) |
Period: |
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Assessment Summary: | CWPI 50%, EXCB 50% |
Assessment Detail: |
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Supplementary Assessment: |
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Requisites: |
In taking this module you cannot take CM52042
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.
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Synopsis: | You will learn about the theory and practice of natural language processing as a tool 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.
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Course availability: |
CM32030 is Optional on the following courses:Department of Computer Science
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Notes:
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