Last modified: 31 Jul 2023 11:19
The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which Natural Language Generation (NLG) is pursued, (2) the main rule based and statistical methods that are used in NLG, and (3) some of the main NLG algorithms and systems. The course will cover NLG both as a theoretical enterprise (e.g., for constructing models of language production) and as practical language engineering, paying particular attention to the link between NLG and data science. Some programming experience is expected.
| Study Type | Postgraduate | Level | 5 |
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| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
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The course will cover topics including:
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 25 | |
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| Feedback |
Technology Assessment Report |
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| Assessment Type | Summative | Weighting | 25 | |
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| Knowledge Level | Thinking Skill | Outcome |
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| Assessment Type | Summative | Weighting | 50 | |
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| Assessment Weeks | Feedback Weeks | |||
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| Knowledge Level | Thinking Skill | Outcome |
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There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
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| Assessment Weeks | Feedback Weeks | |||
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| Knowledge Level | Thinking Skill | Outcome |
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| Knowledge Level | Thinking Skill | Outcome |
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| Factual | Remember | ILO’s for this course are available in the course guide. |
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