Abstract
The behavior of verbs in sublanguages is highly specific and does not follow general principles of lexical decomposition. NLP applications require specific lexicons for tasks like surface parsing and shallow semantic interpretation. The reduced set of verbal senses specific to a given domain is more appropriate for efficient processing in real world tasks (e.g. information extraction and retrieval). In this paper a method for learning verb subcategorization patterns from corpora is proposed. Conceptual clustering techniques are applied to the results of surface parsing in order to extract relevant domain typical senses and automatically build a lexicon of subcategorization frames. The aim is to learn a core of lexico-grammatical knowledge suitable to support more sophisticated parsing strategies to be applied in a target NLP application. Results derived for the Italian language from several corpora are presented.
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© 1997 Springer-Verlag Berlin Heidelberg
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Basili, R., Pazienza, M.T., Vindigni, M. (1997). Corpus-driven unsupervised learning of verb subcategorization frames. In: Lenzerini, M. (eds) AI*IA 97: Advances in Artificial Intelligence. AI*IA 1997. Lecture Notes in Computer Science, vol 1321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63576-9_105
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DOI: https://doi.org/10.1007/3-540-63576-9_105
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