Clustering mechanisms are important for many NLP tasks such as knowledge acquisition, term extraction and disambiguation, machine translation and ontology building. Our approach focuses on the clustering of terminological contexts as a bootstrapping method for such tasks. While most techniques involve statistical methods, we combine syntactic and semantic information about terms and their contexts in order to group contexts according to their similarity. We develop a prototype system which aims to demonstrate the feasibility of this approach.
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