Abstract
Measuring semantic nearness of documents is important for accurate information retrieval, automated text categorization and classification. Inspired by the observation that text documents contain semantically coherent set of ideas/topics, this paper presents the design and experimental evaluation of a method to represent a text document as a set of concepts. Based on this, we propose a method to measure semantic nearness of texts. Our method makes use of WordNet which is a lexico-semantic network of words. We bypass word sense disambiguation. In order to show the effectiveness of our representation of texts, we compare experimental results of text classification and clustering with the results of classification and clustering with standard techniques. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Pandya, A., & Bhattacharyya, P. (2005). Text similarity measurement using concept representation of texts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3776 LNCS, pp. 678–683). https://doi.org/10.1007/11590316_109
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