Abstract
In this paper, we present a fast and scalable Bayesian model for improving weakly annotated data – which is typically generated by a (semi) automated information extraction (IE) system from Web documents. Weakly annotated data suffers from incorrect ontological role assignments. Our experimental evaluations with the TAP and a collection of 20,000 home pages from university, shopping and sports Web sites, indicate that the model described here can improve the accuracy of role assignments from 40% to 85% for template driven sites, from 68% to 87% for non-template driven sites.
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Gelgi, F., Vadrevu, S., Davulcu, H. (2007). Relational Model Based Annotation of the Web Data. In: Wegrzyn-Wolska, K.M., Szczepaniak, P.S. (eds) Advances in Intelligent Web Mastering. Advances in Soft Computing, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72575-6_20
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DOI: https://doi.org/10.1007/978-3-540-72575-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72574-9
Online ISBN: 978-3-540-72575-6
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