Bootstrapping morphological analyzers by combining human elicitation and machine learning
Oflazer, Kemal and Nirenburg, Sergei and McShane, Marjorie (2001) Bootstrapping morphological analyzers by combining human elicitation and machine learning. Computational Linguistics, 27 (1). pp. 59-85. ISSN 0891-2017
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Official URL: http://dx.doi.org/10.1162/089120101300346804
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 inflectional 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.
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