Unsupervised learning of the morphology of a natural language
Computational Linguistics
Minimally supervised morphological analysis by multimodal alignment
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Formal languages and their relation to automata
Formal languages and their relation to automata
Knowledge-free induction of morphology using latent semantic analysis
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Unsupervised models for morpheme segmentation and morphology learning
ACM Transactions on Speech and Language Processing (TSLP)
A naive theory of affixation and an algorithm for extraction
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Evaluating an agglutinative segmentation model for ParaMor
SigMorPhon '08 Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology
ParaMor: minimally supervised induction of paradigm structure and morphological analysis
SigMorPhon '07 Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Fully unsupervised word segmentation with BVE and MDL
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Word segmentation as general chunking
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Optimal stem identification in presence of suffix list
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton. For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. We create a word-trie, transform it into a minimal DFA, then identify hubs. Those hubs mark the boundary between root and suffix, achieving similar performance to more complex mixtures of techniques.