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.
S. L., Y., M., H., A. C., T., Scarselli, F., Gori, M. (2006). Document Mining using Graph Neural Network. In Comparative Evaluation of XML Information Retrieval Systems (pp.458-472). Springer Berlin Heidelberg [10.1007/978-3-540-73888-6_43].
Document Mining using Graph Neural Network
SCARSELLI, FRANCO;GORI, MARCO
2006-01-01
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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
inex2006GNN.pdf
non disponibili
Tipologia:
Post-print
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
195.73 kB
Formato
Adobe PDF
|
195.73 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/18289
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo