Self-Organizing Maps
Unsupervised learning of the morphology of a natural language
Computational Linguistics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Morphological cues for lexical semantics
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Unsupervised discovery of morphemes
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
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Automatic creation of syntactic and semantic word categorizations is a challenging problem for highly inflecting languages due to excessive data sparsity. Moreover, the study of colloquial language resources requires the utilization of fully corpus-based tools. We present a completely automated approach for producing word categorizations for morphologically rich languages. Self-Organizing Map (SOM) is utilized for clustering words based on the morphological properties of the context words. These properties are extracted using an automated morphological segmentation algorithm called Morfessor. Our experiments on a colloquial Finnish corpus of stories told by young children show that utilizing unsupervised morphs as features leads to clearly improved clusterings when compared to the use of whole context words as features.