- Kemal Oflazer, S. Nirenburg. 1999. Practical Bootstrapping of Morphological Analyzers. Abstract: This paper presents a semi-automatic technique for developing broad-coverage finite-state morphological analyzers for any language. 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 morphology of low-density languages in the context of the Expedition project at NMSU CRL. This elicitbuild-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 suite, and any corrections are fed back into the learning procedure that builds an improved analyzer.
