Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning class-to-class selectional preferences
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Using co-composition for acquiring syntactic and semantic subcategorisation
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Disambiguating noun and verb senses using automatically acquired selectional preferences
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Clustering Syntactic Positions with Similar Semantic Requirements
Computational Linguistics
Inducing classes of terms from text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Clonal selection algorithm for learning concept hierarchy from Malay text
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
A hybrid approach for learning concept hierarchy from Malay text using artificial immune network
Natural Computing: an international journal
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The context of this paper is the application of unsupervised Machine Learning techniques to building ontology extraction tools for Natural Language Processing. Our method relies on exploiting large amounts of linguistically annotated text, and on linguistic concepts such as selectional restrictions and co-composition. We work with a corpus of medical texts in English. First we apply a shallow parser to the corpus to get subject-verb-object structures. We then extract verb-noun relations, and apply a clustering algorithm to them to build semantic classes of nouns. We have evaluated the adequacy of the clustering method when applied to a syntactically tagged corpus, and the relevance of the semantic content of the resulting clusters.