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Found 6 papers, 1 papers with code
Modeling Morphological Typology for Unsupervised Learning of Language Morphology
This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology.
Morphological Segmentation for Low Resource Languages
This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program.
Unsupervised Morphology Learning with Statistical Paradigms
This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e. g., suffixes) that can be applied to a homogeneous set of words (e. g., nouns or verbs).
Finding Optimal 1-Endpoint-Crossing Trees
Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees.
