Abstract
In the first half of the talk, I will discuss data mining technologies that can result in better browsing and searching. Consider the problem of merging documents from different categorizations (taxonomies) into a single master categorization. Current classifiers ignore the implicit similarity information present in the source categorizations. I will show that by incorporating this information into the classification model, classification accuracy can be substantially improved [1]. Next, I will demonstrate novel search technology that treats numbers as first-class objects, and thus yields dramatically better results than current Web search engines when searching over product descriptions or other number-rich documents [2].
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References
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Srikant, R. (2002). Data Mining Technologies for Digital Libraries and Web Information Systems. In: Lim, E.P., et al. Digital Libraries: People, Knowledge, and Technology. ICADL 2002. Lecture Notes in Computer Science, vol 2555. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36227-4_7
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DOI: https://doi.org/10.1007/3-540-36227-4_7
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