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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Natural Language Generation (Level 10) (INFR10034)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionThe 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Informatics 2A - Processing Formal and Natural Languages (INFR08008) AND Foundations of Natural Language Processing (INFR09028) AND Introductory Applied Machine Learning (INFR09029)
Prohibited Combinations Students MUST NOT also be taking Natural Language Generation (Level 11) (INFR11060)
Other requirements None
Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2011/12 Semester 2, Available to all students (SV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 16:10 - 17:00
CentralLecture1-11 16:10 - 17:00
First Class Week 1, Tuesday, 16:10 - 17:00, Zone: Central. Chrystal Mac Building Sem Rm 1
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Summary of Intended 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.
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0

Practical exercises involving application of existing algorithms and evalution techniques.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
Additional Information
Academic description Not entered
Syllabus *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
Transferable skills Not entered
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)
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 35
Private Study/Other 45
Total 100
KeywordsNot entered
Course organiserDr Amos Storkey
Tel: (0131 6)51 1208
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
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