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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Natural Language Generation (Level 11) (INFR11060)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe area of study called natural language generation (NLG) investigates how computer programs can be made to produce high-quality natural language text or speech from computer-internal representations of information (or other texts). Motivations for this study range from foundational attempts to understand how people produce text and speech (linguistic, psycholinguistic) to entirely practical efforts to produce natural language output for a wide range of applications, including automatic explanation from advisory systems, automatic summarisation from single or multiple documents, machine translation, dialogue systems, human-robot interaction, tutorial systems, and many more.

An introduction to the theory and practice of computational approaches to natural language generation. The course will cover common approaches to content selection and organization, sentence planning, and realisation. The course will cover both symbolic approaches to generation, as well as more recent statistical and trainable techniques. It also aims to provide: An understanding of key aspects of human language production An understanding of evaluation methods used in this field Exposure to techniques and tools used to develop practical systems that can communicate with users; Insight into open research problems in applications of natural language generation, e.g., summarization, paraphrase, dialogue, multimodal discourse.
Course description *Content selection and organization
*Sentence planning and realization
*Human language production (monologue, dialogue)
*Generating discourse
*Generating dialogue
*Probabilistic and trainable systems
*Multimodal generation
*Text to text generation (summarisation, paraphrase)

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Natural Language Computing
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed ( Foundations of Natural Language Processing (INFR09028) OR Advanced Natural Language Processing (INFR11059)) AND Introductory Applied Machine Learning (INFR09029)
Prohibited Combinations Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Information for Visiting Students
Course Delivery Information
Not being delivered
Learning Outcomes
1 - Given an NLG system students should be able to: o Provide a written analysis of how the main theories and algorithms behind NLG systems have been incorporated into the system, including an exposition of the theories and algorithms. o Provide the basis for the evaluation of the system by diagnosing its relations to other NLG systems, and to human performance data.
2 - Given a simple NLG problem, students should be able to use computational tools and methodologies to solve it.
3 - Given a current area of NLG research, students should be able to locate and summarise recent progress in the area.
4 - Given and open-ended NLG problem, students should be able to provide a well-justified solution to the problem using the range of tools and techniques covered in the course.
Reading List
* Reiter and Dale 2000 Building Natural Language Generation Systems
* Mani 2001 Automatic Summarization
* Textbook on human production (Pickering and Garrod chapter, or new textbook)
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Iain Murray
Tel: (0131 6)51 9078
Course secretaryMs Katey Lee
Tel: (0131 6)50 2701
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