Learning IS-A relations from specialized-domain texts with co-occurrence measures

[EN] Ontology enrichment is a classification problem in which an algorithm categorizes an input conceptual unit in the corresponding node in a target ontology. Conceptual enrichment is of great importance both to Knowledge Engineering and Natural Language Processing, because it helps maximize the efficacy of intelligent systems, making them more adaptable to scenarios where information is produced by means of language. Following previous research on distributional semantics, this paper presents a case study of ontology enrichment using a feature-extraction method which relies on collocational information from corpora. The major advantage of this method is that it can help locate an input unit within its corresponding superordinate node in a taxonomy using a relatively small number of lexical features. 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