Paper: Supervised Learning of Complete Morphological Paradigms

ACL ID N13-1138
Title Supervised Learning of Complete Morphological Paradigms
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013

We describe a supervised approach to predict- ing the set of all inflected forms of a lexical item. Our system automatically acquires the orthographic transformation rules of morpho- logical paradigms from labeled examples, and then learns the contexts in which those trans- formations apply using a discriminative se- quence model. Because our approach is com- pletely data-driven and the model is trained on examples extracted from Wiktionary, our method can extend to new languages without change. Our end-to-end system is able to pre- dict complete paradigms with 86.1% accuracy and individual inflected forms with 94.9% ac- curacy, averaged across three languages and two parts of speech.