Text Technologies (Level 11) (P00955)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-TTS

Text technologies are concerned with retrieving and synthesising information from large volumes of structured textual material (such as documents or web pages). An appreciation of computer science, statistics and natural language processing is essential for those working in this area.

The course will cover:
-Concepts from information retrieval and question answering (such as relevance, text representation, query/question representation, indexing, evaluation, retrieval models)
-Applications (such as cross-language information retrieval, web searching, collaborative filtering, question answering, information extraction, trust detection).

Entry Requirements

? Pre-requisites : For Informatics PG students only, or by special permission of the School. Students must be able to program. An appreciation of topics in computer science, probability and natural language processing will enhance one's understanding of the material to be covered in the course.

? Prohibited combinations : Text Technologies (Level 10)

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Language Processing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
20/09/2007	12:10	13:00	Room 7.01, David Hume Tower	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	12:10	13:00	Central
Lecture	Thursday	12:10	13:00	Central

Summary of Intended Learning Outcomes

It is anticipated that students who successfully complete the course will be able to:
-Describe the main computational algorithms used to organise, search and recover information present within large volumes of textual material.
-Evaluate the output of such algorithms.
-Discuss applications of such techniques in a wide variety of practical situations.
-Demonstrate an understanding of how to successfully recover information from large volumes of texts.
-Critically evaluate research literature in the field.