Abstract
The Graph Neural Network is a relatively new machine learning method capable of encoding data as well as relationships between data elements. This paper applies the Graph Neural Network for the first time to a given learning task at an international competition on the classification of semi-structured documents. Within this setting, the Graph Neural Network is trained to encode and process a relatively large set of XML formatted documents. It will be shown that the performance using the Graph Neural Network approach significantly outperforms the results submitted by the best competitor.
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Yong, S.L., Hagenbuchner, M., Tsoi, A.C., Scarselli, F., Gori, M. (2007). Document Mining Using Graph Neural Network. In: Fuhr, N., Lalmas, M., Trotman, A. (eds) Comparative Evaluation of XML Information Retrieval Systems. INEX 2006. Lecture Notes in Computer Science, vol 4518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73888-6_43
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DOI: https://doi.org/10.1007/978-3-540-73888-6_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73887-9
Online ISBN: 978-3-540-73888-6
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