Supervised Learning of Complete Morphological Paradigms
Venue
Proceedings of the North American Chapter of the Association for Computational Linguistics (2013)
Publication Year
2013
Authors
Greg Durrett, John DeNero
BibTeX
Abstract
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.
