Abstract
In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.
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Weng, D., Hong, J., Bell, D.A. (2014). Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_9
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DOI: https://doi.org/10.1007/978-3-319-11749-2_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11748-5
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