Lecturer	Stephen Pulman
Degrees	Schedule C1 — Computer Science Schedule C1 — Mathematics and Computer Science
Term	Michaelmas Term 2011  (16 lectures)

Overview

The aim of this series of lectures is to provide an introduction to some of the major topics in computational linguistics. No previous knowledge of linguistics is required.

Learning outcomes

By the end of this lecture series you should understand what the concerns of computational linguists are and be familiar with some of the major topics in the area. You should also be in a position to find out more of the practical details for yourself.

Prerequisites

A prior understanding of fundamental linguistic concepts is helpful but not entirely necessary for the course. 

The practical component of the course consists of a relatively substantial programming assignment. Students are free to choose the programming language of their choice, but should be at least somewhat familiar with the following concepts:

• File I/O
• Associative Data Structures (e.g., Hash Maps)
• String Parsing (Some experience with regular expressions is helpful)

Students with little or no programming experience may find the practical component very challenging, and we would suggest they ensure that they develop a basic proficiency before attempting the course.

Syllabus

Week 1
Intro to linguistics (1) Parts of speech
Automatically assigning parts of speech (‘pos tagging’).

Week 2
Intro to linguistics (2) Syntax: constituent structure
Shallow parsing: NP chunking

Week 3
Context Free Grammars and parsers for natural language
Intro to Unification Grammar

Week 4
More efficient parsing: charts and packing
Probabilistic parsing and disambiguation

Week 5
Intro to linguistics (3) Semantics and inference
Logic for natural language semantics

Week 6
Compositional semantics
Automated inference for natural language

Week 7
Word sense disambiguation; vector space models of meaning
Some text processing applications:
Information Extraction; Question Answering

Week 8
Spoken language dialogue modelling:
information state approaches;
Markov Decision Processes, reinforcement learning

Reading list

The following textbooks cover much of the material. More detailed references will be given with the lectures. Lecture handouts will be supplied.

• James Allen 1995 Natural Language Understanding, Addison-Wesley Pub Co, 2nd edition.
• An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Daniel Jurafsky and James H. Martin, 2000, Prentice-Hall.

Taking our courses

This form is not to be used by students studying for a degree in the Department of Computer Science, or for Visiting Students who are registered for Computer Science courses

Other matriculated University of Oxford students who are interested in taking this, or other, courses in the Department of Computer Science, must complete this online form by 17.00 on Friday of 0th week of term in which the course is taught. Late requests, and requests sent by email, will not be considered. All requests must be approved by the relevant Computer Science departmental committee and can only be submitted using this form.