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Computational Linguistics

Paola Merlo, Editor
March 2001, Vol. 27, No. 1, Pages 59-85
(doi: 10.1162/089120101300346804)
© 2001 Association for Computational Linguistics
Bootstrapping Morphological Analyzers by Combining Human Elicitation and Machine Learning
Article PDF (908.44 KB)
Abstract

This paper presents a semiautomatic technique for developing broad-coverage finite-state morphological analyzers for use in natural language processing applications. It consists of three components—elicitation of linguistic information from humans, a machine learning bootstrapping scheme, and a testing environment. The three components are applied iteratively until a threshold of output quality is attained. The initial application of this technique is for the morphology of low-density languages in the context of the Expedition project at NMSU Computing Research Laboratory. This elicit-build-test technique compiles lexical and inØectional information elicited from a human into a finite-state transducer lexicon and combines this with a sequence of morphographemic rewrite rules that is induced using transformation-based learning from the elicited examples. The resulting morphological analyzer is then tested against a test set, and any corrections are fed back into the learning procedure, which then builds an improved analyzer.