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Computational Linguistics:  2010-2011

Information

Lecturer

Stephen Pulman

Degrees

Schedule C1 — Honour School of Computer Science

Schedule C1 — Honour School of Mathematics and Computer Science

Schedule C — MSc in Computer Science

Term

Michaelmas Term 2010  (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.

Synopsis

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

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.