Unsupervised Learning of the Morphology of a Natural Language | Zendy

John Goldsmith | Zendy

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Unsupervised Learning of the Morphology of a Natural Language

Author(s) -

John Goldsmith

Publication year - 2001

Publication title -

computational linguistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.314

H-Index - 98

eISSN - 1530-9312

pISSN - 0891-2017

DOI - 10.1162/089120101750300490

Subject(s) - computer science , generative grammar , heuristics , natural language processing , artificial intelligence , minimum description length , grammar , metric (unit) , segmentation , set (abstract data type) , natural language , generative model , probabilistic logic , linguistics , philosophy , operations management , economics , programming language , operating system

This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist.In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.

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