THE UNIVERSITY of EDINBURGH
DEGREE REGULATIONS & PROGRAMMES OF STUDY 2009/2010
Advanced Natural Language Processing (VS1) (P02942)
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.
? This course is only available to visiting students.
? 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
? Normal year taken : Postgraduate
? Contact Teaching Time : 3 hour(s) per week for 10 weeks
First Class Information
All of the following classes
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.
Written Examination 70
Assessed Assignments 30
Oral Presentations 0
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.
Contact and Further Information
The Course Secretary should be the first point of contact for all enquiries.
Miss Tamise Totterdell
Dr Douglas Armstrong
Course Website : http://www.inf.ed.ac.uk/teaching/courses/
School Website : http://www.informatics.ed.ac.uk/
College Website : http://www.scieng.ed.ac.uk/