Undergraduate Course: Natural Language Generation (Level 10) (INFR10034)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/nlg |
Taught in Gaelic? | No |
| Course description | The 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. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
| Not being delivered |
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
Assessment
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
| None |
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)
*Evaluation
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)
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| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 35
Private Study/Other 45
Total 100 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Mary Cryan
Tel: (0131 6)50 5153
Email: mcryan@inf.ed.ac.uk |
Course secretary | Miss Kate Farrow
Tel: (0131 6)50 2706
Email: Kate.Farrow@ed.ac.uk |
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