Abstract
Computational morphologies often consist of a lexicon and some rule component, the creation of which requires various competences and considerable effort. Such a description, on the other hand, makes an easy extension of the morphology with new lexical items possible. Most freely available morphological resources, however, contain no rule component. They are usually based on just a morphological lexicon, containing base forms and some information (often just a paradigm ID) identifying the inflectional paradigm of the word, possibly augmented with some other morphosyntactic features. The aim of the research presented in this paper was to create an algorithm that makes the integration of new words into such resources similarly easy to the way a rule-based morphology can be extended. This is achieved by predicting the correct paradigm for words not present in the lexicon. The supervised machine learning algorithm described in this paper is based on longest matching suffixes and lexical frequency data, and is demonstrated and evaluated for Russian.
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Novák, A. (2015). Making Morphologies the “Easy” Way. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9041. Springer, Cham. https://doi.org/10.1007/978-3-319-18111-0_10
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DOI: https://doi.org/10.1007/978-3-319-18111-0_10
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