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Thesis

Probabilistic modelling of morphologically rich languages

Abstract:

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms.

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Institution:
University of Oxford
Research group:
Computational Linguistics Group
Oxford college:
Lincoln College
Department:
Mathematical,Physical & Life Sciences Division - Computer Science,Department of
Role:
Author

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Role:
Supervisor
Role:
Supervisor
Publication date:
2014
Type of award:
DPhil
Level of award:
Doctoral
URN:
uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c7
Local pid:
ora:11837

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