Semantics and Pragmatics of Natural Language Processing (Level 11) (P00882)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-SPNLP

This module takes up where Introduction to Computational Linguistics (ICL) leaves off, addressing computational approaches to Natural Language (NL) semantics, including formal semantics, lexical semantics and discourse semantics.

The objective is for students to understand some central problems in NL semantics associated with syntax, the lexicon and discourse, as well as methods for handling them and the machine-accessible resources that some of these methods use. The module also aims to show students ways in which aspects of semantic analysis can support Language Technology (LT) applications such as Information Retrieval, Information Extraction, Question Answering, and Generation.

Entry Requirements

? Pre-requisites : Introduction to Computational Linguistics or Linguistic & Computational Theory of Grammar (not running in 2006/07). For PG students only, or by special permission of the School. This course essentially assumes some elementary knowledge of sentential syntax, and familiarity with first order logic.

? Prohibited combinations : Semantics & Pragmatics of Natural Language Processing (Level 10)

Subject Areas

Home subject area

Language Processing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
07/01/2008	12:10	13:00	Room G17, Adam Ferguson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	12:10	13:00	Central
Lecture	Thursday	12:10	13:00	Central

Summary of Intended Learning Outcomes

It is anticipated that students who successfully complete the course will be able to:
-Describe how theorem proving and model building can be used in assigning interpretations to utterances in NL.
-Explain how theorem proving and model building can be used to support automated question answering.
-Evaluate algorithmic and statistical techniques for resolving anaphoric pronouns and noun phrases (NPs).
-Employ supervised machine learning techniques in anaphor resolution.
-Discuss sense-based properties of lexemes and sense-based relations between them, and the effects they have on LT applications.
-Use on-line lexical resources to support LT applications.