Unsupervised Learning of Morphology

Unsupervised Learning of Morphology

some morphological pattern that recurs among the groups. Such emergent patterns provide enough clues for segmentation and can sometimes be formulated as rules or morphological paradigms. (c) Features and Classes: In this family of methods, a word is seen as made up of a set of features—n-grams in Mayfield and McNamee (2003) and McNamee and Mayfield (2007), and initial/terminal/mid-substring in ...

Unsupervised learning of derivational morphology

We present in this paper an unsupervised method to learn suuxes and suuxation operations from an innectional lexicon of a language. The elements acquired with our method are used to build stemming procedures and can assist lexicographers in the development of new lexical resources.

Unsupervised Learning of Morphology with Graph Sampling

We introduce a language-independent, graph-based probabilistic model of morphology, which uses transformation rules operating on whole words instead of the traditional morphological segmentation. The morphological analysis of a set of words is expressed through a graph having words as vertices and structural relationships between words as edges. We define a probability distribution over such gr...

Unsupervised Learning of Morphology Without Morphemes

The first morphological learner based upon the theory of Whole Word Morphology (Ford et al., 1997) is outlined, and preliminary evaluation results are presented. The program, Whole Word Morphologizer, takes a POS-tagged lexicon as input, induces morphological relationships without attempting to discover or identify morphemes, and is then able to generate new words beyond the learning sample. Th...

Unsupervised learning of Arabic non-concatenative morphology