Abstract
We developed a new indexing formalism that considers not only the terms in a document, but also the concepts to represent the semantic content of a document. In this approach, concept clusters are defined and a concept vector space model is proposed to represent the semantic importance of words and concepts within a document. Through experiments on the TREC-2 collection, we show that the proposed method outperforms an indexing method based on term frequency.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kang, BY., Kim, HJ., Lee, SJ. (2004). Performance Analysis of Semantic Indexing in Text Retrieval. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_52
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DOI: https://doi.org/10.1007/978-3-540-24630-5_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21006-1
Online ISBN: 978-3-540-24630-5
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