Modeling and learning multilingual inflectional morphology in a minimally supervised framework
Modeling and learning multilingual inflectional morphology in a minimally supervised framework
Automatic rule induction for unknown-word guessing
Computational Linguistics
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Computational Linguistics
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CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
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Language software applications encounter new words, e.g., acronyms, technical terminology, names or compounds of such words. In order to add new words to a lexicon, we need to indicate their inflectional paradigm. We present a new generally applicable method for creating an entry generator, i.e. a paradigm guesser, for finite-state transducer lexicons. As a guesser tends to produce numerous suggestions, it is important that the correct suggestions be among the first few candidates. We prove some formal properties of the method and evaluate it on Finnish, English and Swedish full-scale transducer lexicons. We use the open-source Helsinki Finite-State Technology [1] to create finite-state transducer lexicons from existing lexical resources and automatically derive guessers for unknown words. The method has a recall of 82-87 % and a precision of 71-76 % for the three test languages. The model needs no external corpus and can therefore serve as a baseline.