Multilingual noise-robust supervised morphological analysis using the WordFrame model

Abstract

This paper presents the WordFrame model, a noise-robust supervised algorithm capable of inducing morphological analyses for languages which exhibit prefixation, suffixation, and internal vowel shifts. In combination with a näive approach to suffix-based morphology, this algorithm is shown to be remarkably effective across a broad range of languages, including those exhibiting infixation and partial reduplication. Results are presented for over 30 languages with a median accuracy of 97.5% on test sets including both regular and irregular verbal inflections. Because the proposed method trains extremely well under conditions of high noise, it is an ideal candidate for use in co-training with unsupervised algorithms.