Models for predicting the inflectional paradigm of Croatian words

• Main Author: Jan Šnajder
• Format: Article
• Language: English
• Published: Znanstvena založba Filozofske fakultete Univerze v Ljubljani (Ljubljana University Press, Faculty of Arts) 2013-12-01
• Series: Slovenščina 2.0: Empirične, aplikativne in interdisciplinarne raziskave
• Subjects: computational morphology; paradigm prediction; machine learning; feature selection
• Online Access: http://www.trojina.org/slovenscina2.0/arhiv/2013/2/Slo2.0_2013_2_02.pdf

Morphological analysis is a prerequisite for many natural language processing tasks. For inflectionally rich languages such as Croatian, morphological analysis typically relies on a morphological lexicon, which lists the lemmas and their paradigms. However, a real-life morphological analyzer must also be able to handle properly the out-of-vocabulary words. We address the task of predicting the correct inflectional paradigm of unknown Croatian words. We frame this as a supervised machine learning problem: we train a classifier to predict whether a candidate lemma-paradigm pair is correct based on a number of string- and corpus-based features. The candidate lemma-paradigm pairs are generated using a handcrafted morphology grammar. Our aim is to examine the machine learning aspect of the problem: we test a comprehensive set of features and evaluate the classification accuracy using different feature subsets. We show that satisfactory classification accuracy (92%) can be achieved with SVM using a combination of string- and corpus-based features. On a per word basis, the F1-score is 53% and accuracy is 70%, which outperforms a frequency-based baseline by a wide margin. We discuss a number of possible directions for future research.