Unsupervised models for morpheme segmentation and morphology learning

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Unsupervised models for morpheme segmentation and morphology learning (2007)

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by Mathias Creutz , Krista Lagus

Venue:	ACM Trans. Speech Lang. Process
Citations:	108 - 8 self

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@ARTICLE{Creutz07unsupervisedmodels,
    author = {Mathias Creutz and Krista Lagus},
    title = {Unsupervised models for morpheme segmentation and morphology learning},
    journal = {ACM Trans. Speech Lang. Process},
    year = {2007}
}

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Abstract

We present a model family called Morfessor for the unsupervised induction of a simple morphology from raw text data. The model is formulated in a probabilistic maximum a posteriori framework. Morfessor can handle highly inflecting and compounding languages where words can consist of lengthy sequences of morphemes. A lexicon of word segments, called morphs, is induced from the data. The lexicon stores information about both the usage and form of the morphs. Several instances of the model are evaluated quantitatively in a morpheme segmentation task on different sized sets of Finnish as well as English data. Morfessor is shown to perform very well compared to a widely known benchmark algorithm, in particular on Finnish data.

Keyphrases

morphology learning morpheme segmentation lengthy sequence english data simple morphology several instance unsupervised induction posteriori framework benchmark algorithm word segment lexicon store information model family probabilistic maximum raw text data morpheme segmentation task finnish data

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