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Natural Language Processing

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... rt human communication. Enabling human-machine communication ... Machine (assisted) translation, tools. Language modeling. Lexicography. Speech. 8. NLP systems ... – PowerPoint PPT presentation

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Title: Natural Language Processing


1
Natural Language Processing
  • (Ling 581)

2
What is NLP?
  • Natural language processing
  • Computer methods and their usefulness (or
    uselessness) for human language processing
    (textual, spoken, gestural, etc.)
  • Implementation of techniques, procedures,
    algorithms for language computation
  • AI and cognitive science w/rt human communication
  • Enabling human-machine communication
  • Enhancing human-human communication

3
NOT this NLP!
  • Neuro-Linguistic Programming
  • New-age, pop-psychology trance-inducing hypnosis
    technique for behavior modifi-cation
  • Improving sales techniques
  • Enhancing sports performance
  • Losing weight
  • Dealing with problem people

4
Situating NLP
philosophy
linguistics
computer science
NLP
communication
math/statistics
psychology/cognitive science
5
Theoretical foundations
  • math statistics, calculus, algebra, modeling
  • computational paradigms connectionist,
    rule-based, cognitively plausible
  • linguistics LFG, HPSG, GB, OT, CG, etc.
  • architectures stacks, automata, networks,
    compilers

6
Multidisciplinarity
  • humanities, natural and behavioral sciences, and
    engineering
  • linguistics, computer science, psychology, and
    mathematics
  • theory and practice, science and art
  • models, foundations vs. corpora, data (top-down
    vs. bottom-up)

7
Some areas of research
  • Corpora, tools, resources, standards
  • Language/grammar engineering
  • Machine (assisted) translation, tools
  • Language modeling
  • Lexicography
  • Speech

8
NLP systems
  • Computer systems designed to perform 1 language
    functions (perhaps with other tasks)
  • LISP, Prolog, C, C, Perl, Java, specialized
    high-level metalanguages
  • Downloaded, web-enabled, proprietary
  • Various UIs, resource requirements, complexity
    of use
  • Mono/multilingual, various i/o modalities

9
Sample NLP class tasks
  • Computational morphology finite-state techniques
    for morphophonological tasks
  • Lexical ontologies using WordNet
  • Language modeling neural net, memory-based
    learning, or AML
  • Language generation generate full sentences from
    semantic frames
  • Parsing using finite-state parsers and
    transducers for syntax and semantics
  • Speech recognition spoken language
    identification
  • Speech synthesis develop a TTS system for a
    language
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