Towards a Malay Derivational Lexicon: Learning Affixes Using Expectation Maximization
Suriani Sulaiman, Michael Gasser, Sandra Kuebler · 2011
We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of morphological segmentations produced by a naïve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given segmentation. We train the model with EM to find the segmentation that maximizes the probability of each morpheme. We extract the set of affix patterns produced by our algorithm and evaluate them against two references: a list of affix patterns extracted from our hand-segmented derivational wordlist and a derivational history produced by a stemmer. 1