Paradigm classification in supervised learning of morphology

Supervised morphological paradigm learning by identifying and aligning the longest com-mon subsequence found in inflection tables has recently been proposed as a simple yet competitive way to induce morphological pat-terns. We combine this non-probabilistic strat-egy of inflection table generalization with a discriminative classifier to permit the recon-struction of complete inflection tables of un-seen words. Our system learns morphological paradigms from labeled examples of inflection patterns (inflection tables) and then produces inflection tables from unseen lemmas or base forms. We evaluate the approach on datasets covering 11 different languages and show that this approach results in consistently higher ac-curacies vis-à-vis other methods on the same task, thus indicating that the general method is a viable approach to quickly creating high-accuracy morphological resources.