SEMANTIC CLUSTERING OF VERBS Analysis of Morphosyntactic Contexts Using the SOM Algorithm

Obtaining semantic or functional word categories from data in an unsupervised manner is a problem motivated both from the linguistic point of view and from that of construing language models for various language processing tasks. In this work, we use the self-organizing map algorithm to visualize and cluster common Finnish verbs based on functional and semantic information coded by case marking and function words like postpositions and adverbs. Firstly, based on a data set of over 500,000 utterances of 25 verbs, we studied (a) the base forms and (b) the most common word forms of the same verbs (4764 forms). Secondly, the first experiment was repeated on a set of 600 verbs. The results show that even the simple feature selection used in this experiment was found to be suitable for rough automatic categorization of verbs on the basis of data extracted from unrestricted texts. In particular, the results demonstrate the importance of cultural, social and emotional dimensions in lexical

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