Semi-supervised extensions to Morfessor Baseline

Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of Morphology

Morfessor is a family of methods for learning morphological segmentations of words based on unannotated data. We introduce a new variant of Morfessor, FlatCat, that applies a hidden Markov model structure. It builds on previous work on Morfessor, sharing model components with the popular Morfessor Baseline and Categories-MAP variants. Our experiments show that while unsupervised FlatCat does no...

Morfessor 2.0: Toolkit for statistical morphological segmentation

Morfessor is a family of probabilistic machine learning methods for finding the morphological segmentation from raw text data. Recent developments include the development of semi-supervised methods for utilizing annotated data. Morfessor 2.0 is a rewrite of the original, widely-used Morfessor 1.0 software, with well documented command-line tools and library interface. It includes new features s...

Advances in Weakly Supervised Learning of Morphology

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Oskar Kohonen Name of the doctoral dissertation Advances in Weakly Supervised Learning of Morphology Publisher School of Science Unit Department of Computer Science Series Aalto University publication series DOCTORAL DISSERTATIONS 91/2015 Field of research Language Technology Manuscript submitted 19 January 2014 Date of the de...

Semi-Supervised Learning of Concatenative Morphology

We consider morphology learning in a semi-supervised setting, where a small set of linguistic gold standard analyses is available. We extend Morfessor Baseline, which is a method for unsupervised morphological segmentation, to this task. We show that known linguistic segmentations can be exploited by adding them into the data likelihood function and optimizing separate weights for unlabeled and...

Morfessor 2 . 0 : Python Implementation and Extensions for Morfessor Baseline