2011 | OriginalPaper | Buchkapitel
SC Spectra: A Linear-Time Soft Cardinality Approximation for Text Comparison
verfasst von : Sergio Jiménez Vargas, Alexander Gelbukh
Erschienen in: Advances in Soft Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Soft cardinality (SC) is a softened version of the classical cardinality of set theory. However, given its prohibitive cost of computing (exponential order), an approximation that is quadratic in the number of terms in the text has been proposed in the past. SC Spectra is a new method of approximation in linear time for text strings, which divides text strings into consecutive substrings (i.e., q-grams) of different sizes. Thus, SC in combination with resemblance coefficients allowed the construction of a family of similarity functions for text comparison. These similarity measures have been used in the past to address a problem of entity resolution (name matching) outperforming SoftTFIDF measure. SC spectra method improves the previous results using less time and obtaining better performance. This allows the new method to be used with relatively large documents such as those included in classic information retrieval collections. SC spectra method exceeded SoftTFIDF and cosine tf-idf baselines with an approach that requires no term weighing.