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CSA3180Natural Language Processing

Introduction

and

Course Overview

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Acknowledgement

• Material for some of these slides taken from J Nivre, University of Gotheborg, Sweden

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Why Language and Computers

• Engineering– NLP is concerned with the design and

implementation of effective NL input and output components for computational systems (Robert Dale 2000)

• Scientific– The use of computers for linguistic research

and applications

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NLP is Interdisciplinary

• Linguistics– Theoretical– Applied

• Computer Science– Algorithms– Compiling Techniques

• Artificial Intelligence– Understanding, reasoning– Intelligent Action

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Uszkoreit’s (2000) Five Points

• Solving the human language puzzle– by implementing complex theories directly

• Teaching computers to communicate with people– by exploiting natural modes of communication

• Friendly software should listen and speak– through development of multimodal communication

• Machines can help people communicate with each other.– by developing multilingual applications

• Language is the fabric of the web– through language technology for knowledge management

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Application Areas• Document Processing

– Classification– Summarisation– Information Extraction

• Question Answering– Information Retrieval– Dialogue

• Multilinguality– Machine Translation– Translation tools

• Multimodality– speech– intonation– image

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Basic Problems

• Analysis– Conversion of NL input to internal representations

• Generation– Conversion of internal representations to NL output

• Issues– What kind of input/output/representations– Evaluation– Learning

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Levels of Linguistic Knowledge

• Phonetics/Phonology: sound structure

• Morphology: word structure

• Syntax: sentence structure

• Semantics: meanings

• Pragmatics: use of language in context

• Discourse: paragraphs, texts, dialogues

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Ambiguity

• Morpho-SyntacticWe saw her duck

• Lexical SemanticThey went to the bank

• Structural semanticYoung men and women

• ReferentialShe did it

• PragmaticCan you pass the salt

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Ways of Studying NLP

• By ApplicationMT, IE, IR etc.

• By Approachrational vs. empirical

• By Linguistic Levelmorphology, syntax etc.

• By Algorithm

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Algorithms

• State Machines– automata and transducers

• Rule Systems– regular and context free grammars

• Search– top-down/bottom-up parsing

• Probabilistic algorithms

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Approach in this CoursePart I - Algorithms

• Words [3]– Finite State Algorithms– Morphological Processing

• Sentences [3]– Parsing– (Generation)

• Texts [3]– Tagging– Chunking

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Approach in this CoursePart II – Topics and Tools

• Semantics [6]

• Statistics [6]

• Information Extraction [6]

• Machine Translation [4]

• Information Retrieval [3]

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Course Information

• Course Websitewww.cs.um.edu.mt/~mros/csa3180

• Reference TextJurafsky and Martin

• Tools– Prolog: SWI Prolog– NLTK: nltk.sourceforge.net