Publication at Faculty of Mathematics and Physics |
2013
Abstract
We present a novel method of statistical morphological generation, i.e. the prediction of inflected word forms given lemma, part-of-peech and morphological features, aimed at robustness to unseen inputs. Our system uses a trainable classifier to predict “edit scripts” that are then used to transform lemmas into inflected word forms.
Suffixes of lemmas are included as features to achieve robustness. We evaluate our system on 6 languages with a varying degree of morphological richness.
The results show that the system is able to learn most morphological phenomena and generalize to unseen inputs, producing significantly better results than a dictionary-based baseline.