Automatic extraction of concepts from texts
A statistical approach
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The extraction of relevant terms from texts is an extensively researched task in Text-Mining. Relevant terms have been applied in diverse areas such as Information Retrieval or document clustering and classification. Relevance has, however, a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. Concepts, on the other hand, have a less fuzzy nature. Instead of inferring the importance of a term during the extraction process, as is usually done, it is proposed to focus on the extraction of generic concepts from texts and leave the decision abo...
The extraction of relevant terms from texts is an extensively researched task in Text-Mining. Relevant terms have been applied in diverse areas such as Information Retrieval or document clustering and classification. Relevance has, however, a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. Concepts, on the other hand, have a less fuzzy nature. Instead of inferring the importance of a term during the extraction process, as is usually done, it is proposed to focus on the extraction of generic concepts from texts and leave the decision about relevance to downstream applications. This book, therefore, presents ConceptExtractor, a statistical and language-independent methodology. Concepts extracted by this method can be used to identify keywords in documents or to extract semantic relations from texts, among other possible applications.
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