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COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Research on Language and Computation
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This paper presents a semiautomatic technique for developing broad-coverage finite-state mor-phological 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.