Abstract
We propose the Critical Sentence Vector Model (CSVM), a novel model to measure text similarity. The CSVM accounts for the structural and semantic information of the document. Compared to existing methods based on keyword vector, e.g. Vector Space Model (VSM), CSVM measures documents similarity by measuring similarity between critical sentence vectors extracted from documents. Experiments show that CSVM outperforms VSM in calculation of text similarity.
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Li, W., Wong, KF., Yuan, C., Li, W., Xia, Y. (2005). Improving Text Similarity Measurement by Critical Sentence Vector Model. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_44
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DOI: https://doi.org/10.1007/11562382_44
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