PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Unsupervised Recursive Word Segmentation Using Minimum Description Length
Shlomo Argamon, Navot Akiva, Amihood Amir and Oren Kapah
In: COLING-04, 22-29 August 2004, Geneva, Switzerland.

Abstract

Automatic word segmentation is a basic requirement for unsupervised learning in morphological analysis. In this paper, we formulate a novel recursive method for minimum description length (MDL) word segmentation, whose basic operation is resegmenting the corpus on a prefix (equivalently, a suffix). We derive a local expression for the change in description length under resegmentation, i.e., one which depends only on properties of the specific prefix (not on the rest of the corpus). Such a formulation permits use of a new and efficient algorithm for greedy morphological segmentation of the corpus in a recursive manner. In particular, our method does not restrict words to be segmented only once, into a stem+affix form, as do many extant techniques. Early results for English and Turkish corpora are promising.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
ID Code:204
Deposited By:Navot Akiva
Deposited On:07 June 2004