Empirical Methods in Natural Language Processing (P02003)

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

The course aims to help the student understand some of the factors involved with modelling natural language phenomena (such as parsing, word sense disambiguation, part-of-speech tagging etc) from a mainly statistical perspective.

Entry Requirements

? Pre-requisites : Learning from Data (Level 10/11) and Introduction to Computational Linguistics are strongly recommended, although not required. For Informatics PG students only, or by special permission of the School. This course involves some programming (suitable languages include Perl, C, and/or lisp), a moderate understanding of probability theory and some exposure to natural language processing.

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Learning from Data, (School of Informatics, Schedule O)

Speech Processing, (School of Informatics, Schedule O)

Language Processing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

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

First Class Information

Date	Start	End	Room	Area	Additional Information
07/01/2008	17:10	18:00			To Be Confirmed

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	17:10	18:00	Central
Lecture	Thursday	17:10	18:00	Central

Summary of Intended Learning Outcomes

It is anticipated that students who successfully complete this course will be able to:
-Describe the main algorthms used in statistical natural language processing.
-Discuss the factors involved with processing noisy data sets.
-Demonstrate that they understand how to construct systems using small quantities of labelled data.
-Demonstrate that they understand the nature of existing language datasets.
-Demonstrate an understanding of how to apply techniques from statistical natural language processing to natural language problems.
-Demonstrate a Critical understanding of research literature in the field.