Unsupervised and semi-supervised learning of morphology provide practical solutions for
processing morphologically rich languages with less human labor than the traditional rule …

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 …

We use the Base and Transforms Model proposed by Chan [1] as the core of a
morphological analyzer, extending its concept of base-derived relationships to allow multi …

Morpho Challenge is an annual evaluation campaign for unsupervised morpheme analysis.
In morpheme analysis, words are segmented into smaller meaningful units. This is an …

This paper describes a language-independent model for fully unsupervised morphological
analysis that exploits a universal framework leveraging morphological typology. By …

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 …

Paradigms provide an inherent organizational structure to natural language morphology.
ParaMor, our minimally supervised morphology induction algorithm, retrusses the word …

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation,
and other language engineering tasks. The traditional approach to performing …

The morphological lexicon is an important part of NLP systems which is typically hand-
written with the help of linguist experts. Even a partial automation of this process could …

We summarize the strong performance of ParaMor, an unsupervised morphology induction
system, at Morpho Challenge 2008. When ParaMor's morphological analyses, which …