(Natural Language Processing (Nlp)) - Three Linguistic Uses Of Statistical Nlp(1), EBOOKI PDF, NLP

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Three Linguistic Uses of
Statistical NLP
Chris Brew
Linguistics
The Ohio State University
Plan of the talk

Introduction

Three applications

Verb-classes

Fluency for NLG

Aphasia and swearing

Conclusions
November 2000
Linguistic SNLP
Why language?

Machine learning is flavour of the decade
in computational linguistics.

But machine learning also does games,
robotics, medicine, scene interpretation,
motion tracking, credit ratings …

So why do language?
November 2000
Linguistic SNLP
Which technique?
Area
Topic
Techniques
Intonation
Classify contours
Continuous HMM
Tokenization
Guess tokenisation
Error-driven learning
POS Tagging
Stochastic CUF
Discrete HMM, Logic
Programming
Syntax
Stochastic HPSG
EM algorithm
Verb classes
Classify verb
occurrences
Graphical models
Style
Evaluate translation
quality
Multidimensional scaling,
clustering.
Translation
Find and classify
translation pairs
Contingency table
measures (G score, mutual
information)
Very diverse tasks, many challenges.
November 2000
Linguistic SNLP
What’s in it for human sciences?

If you have a clear hypothesis, you can
run machine learning experiments to test
it. Cheaper than psycholinguistics.

It is possible to systematically explore
large classes of theories, even ones too
costly to code up by hand.

Not necessarily explanatory
November 2000
Linguistic SNLP
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