Last modified: 22 May 2019 17:07
Artificial intelligence has helped solve complex practical problems such as driving a car, translating text from/to different languages, understanding and answering questions, and playing games such as chess and Go. This course will provide students of our MSc in AI with knowledge of core natural language generation concepts, approaches, tools, techniques and technologies.
|Second Sub Session
|15 credits (7.5 ECTS credits)
The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which 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. The course will focus mainly on algorithms and methods, some of which pioneered by ARRIA Data2Text. Some programming experience is expected.
Information on contact teaching time is available from the course guide.
Software and report (75%); essay (25%).
Resit: where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).
There are no assessments for this course.
Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.