20 March 2012 introduce Natural Language Processing as one of the components of Artificial Intelligence, both from engineering and cognitive viewpoints show how Natural Language Processing techniques can be programmed using the Prolog programming language describe major trends and systems in Natural Language ProcessingExamination define: morphology; syntax; semantics; pragmatics; and give appropriate examples to illustrate their definitionsEssay and examination describe several standard methods of applying morphological and syntactic knowledge in Natural Language Processing systems, for instance: finite-state methods; probabilistic methods; context-free grammars and parsers, including the Active Chart Parsers; unification grammars and parsingEssay and examination implement context-free grammars implemented by Prolog's Definite Clause GrammarExamination describe simple feature-based semantic systems typically based on logic showing the difference between building semantic representations and interpreting semantic representationsEssay and examination demonstrate a knowledge of two or more methods for resolving pronoun referents as an example of semantic interpretationExamination show an understanding of the role of pragmatics in understanding natural languageEssay and examination describe an application of natural language processing (for instance machine translation) and show the place of syntactic, semantic and pragmatic processingExamination

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02630

2 hrs/week lectures and exercise classes.

 24 1.5 hr examination (50%), continuous assessment (50%). By examination only.

The nature and timing of the continuous assessment will be specified on the module web page -- see under "Relevant Links".

 The module presents an overview of Natural Language Processing and its applications, followed by introductions to morphology, syntax and semantics. These topics are used to introduce some linguistic theory and appropriate algorithms for their computational implementation. Examples are mostly given using Prolog. Introduction and Overview (1 week) Finite State Networks & Transducers (1 week) Simple grammars (1 week) Basic parsing algorithms (1 week) Active chart parsing (1 week) Features (1 week) Semantics (1 week) Discourse Representation Theory (1 week) Discourse (1 week) Cohesion in Text Pronoun Reference Resolution Pragmatics (1 week) Speech Acts Pragmatic Inference Applications (1 week) Speech and language processing: an introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (2nd ed) Jurafsky D & Martin J H Prentice Hall 2008

Module web page

Prolog teaching pages