We describe a supervised approach to predicting the set of all inflected forms of a lexical item. Our system automatically acquires the orthographic transformation rules of morphological paradigms from labeled examples, and then learns the contexts in which those transformations apply using a discriminative sequence model. Because our approach is completely 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 predict complete paradigms with 86.1% accuracy and individual inflected forms with 94.9% accuracy, averaged across three languages and two parts of speech.
Durret in REF presents a completely data-driven and language independent approach; the paper describes a supervised technique to predicting the set of all inflected forms of a lexical item.
Supervised Learning of Complete Morphological Paradigms
NAACL
Computer Science