Abstract
The classical set theory provides a method for comparing objects using cardinality and intersection, in combination with well-known resemblance coefficients such as Dice, Jaccard, and cosine. However, set operations are intrinsically crisp: they do not take into account similarities between elements. We propose a new general-purpose method for comparison of objects using a soft cardinality function that show that the soft cardinality method is superior via an auxiliary affinity (similarity) measure. Our experiments with 12 text matching datasets suggest that the soft cardinality method is superior to known approximate string comparison methods in text comparison task.
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Jimenez, S., Gonzalez, F., Gelbukh, A. (2010). Text Comparison Using Soft Cardinality. In: Chavez, E., Lonardi, S. (eds) String Processing and Information Retrieval. SPIRE 2010. Lecture Notes in Computer Science, vol 6393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16321-0_31
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DOI: https://doi.org/10.1007/978-3-642-16321-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16320-3
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