Advanced Natural Language Processing (P02747)

? Credit Points : 20 ? SCQF Level : 11 ? Acronym : INF-P-ANLP

The course will synthesize recent research in linguistics, computer science, and natural language processing with the aim of introducing students to theoretical and computational models of language. The course will familiarize students with a wide range of linguistic
phenomena with the aim of appreciating the complexity, but also the systematic behaviour of natural languages like English, the pervasiveness of ambiguity, and how this presents challenges in natural language processing. In addition, the course introduce the most important algorithms and data structures that are commonly used
to solve many NLP problems.

Entry Requirements

? Co-requisites : Computer Programming for Speech & Language Processing, or equivalent background. For Informatics PG and final year MInf students only, or by special permission of the School.

? Prohibited combinations : Foundations of Natural Language Processing

Variants

? This course has variants for part year visiting students, as follows

Advanced Natural Language Processing (VS1) (Semester 1 (Blocks 1-2))
Advanced Natural Language Processing (VS1) (Semester 1 (Blocks 1-2))
Advanced Natural Language Processing (VS1) (Semester 1 (Blocks 1-2))

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Other subject areas

Cognitive Science, (School of Informatics, Schedule O)

Computational Linguistics, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

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

? Contact Teaching Time : 3 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
26/09/2008	11:10	12:00	Seminar Room 6, Chrystal Macmillan Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	11:10	12:00	Central
Lecture	Friday	11:10	12:00	Central

Summary of Intended Learning Outcomes

? Students should be able to construct examples of ambiguous Natural Language sentences and provide a written explanation of how ambiguity arises in natural language and why this is a problem for computational analysis.
? Given a grammar, semantics and sentence, students should be able to construct a syntatic and semantic analysis of the sentence.
? Given an appropriate NLP problem, students should be able to apply sequence models, parsing and search algorithms and provide a summary of their operation in this context.
? Given an appropriate NLP problem, students should be able to analyse the problem and decide which data structures and algorithms to apply.
? Review and classify search algorithms and ways of manipulating dynamic data structures.
? Given two NLP algorithms, students should be able to describe how they are related and illustrate differences and limitations by providing illustrative examples.