Advanced Natural Language Processing (VS1) (P02942)

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

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.

The course will cover formal models for representing and analyzing syntax and semantics of words, sentences, and discourse. Students will learn how to analyse sentences algorithmically, using hand-crafted and automatically induced treebank grammars, how to make monotonic syntactic derivations, and build interpretable semantic representations. The course will also cover a number of standard algorithms that are used throughout language processing. Examples include Hidden Markov Models, the EM algorithm, and state space algorithms such as dynamic programming.

Entry Requirements

? This course is only available to visiting students.

? This course is a variant of the following course : P02747

? Pre-requisites : This course is only available to part-year visiting students who are only in Edinburgh for Semester 1. For Informatics PG and final year MInf students only, or by special permission of the School.

? Co-requisites : CPSLP or equivalent background.

? Prohibited combinations : Foundations of Natural Language Processing

Subject Areas

Home subject area

Informatics, (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
25/09/2009	09:00	09:50	Room G.09, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	09:00	09:50	Central
Lecture	Friday	09:00	09:50	Central

Summary of Intended Learning Outcomes

1 - 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.
2 - 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.
3 - Given an appropriate NLP problem, students should be able to analyse the problem and decide which data structures and algorithms to apply.
4 - Review and classify search algorithms and ways of manipulating dynamic data structures.
5 - Given two NLP algorithms, students should be able to describe how they are related and illustrate differences and limitations by providing illustrative examples.

Assessment Information

Written Examination 70
Assessed Assignments 30
Oral Presentations 0

Assessment
The coursework will include 3 homeworks, one on sequence models, one on parsing, and one on applying the methods introduced in the course to an unseen problem.