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Autor:
Nam, Jinseok; Kim, Jungi; Loza Mencía, Eneldo; Gurevych, Iryna; Fürnkranz, Johannes:

Titel:
Large-scale multi-label text classification
Revisiting neural networks

Quelle:
In: Calders,Toon;Esposito,Floriana;Hüllermeier,Eyke;Meo, Rosa (Hrsg.): Machine learning and knowledge discovery in databases Berlin : Springer Verlag (2014) , 437-452

Serie:
Lecture notes in Computer Science, 8725

URL des Volltextes:
http://link.springer.com/chapter/10.1007/978-3-662-44851-9_28

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Elektronische Bibliothek, Information Retrieval, Klassifikation, Lernen, Netzwerk, Ranking, Text


Abstract(original):
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting. Our experimental results show that simple NN models equipped with advanced techniques such as rectified linear units, dropout, and AdaGrad perform as well as or even outperform state-of-the-art approaches on six large-scale textual datasets with diverse characteristics. (DIPF/Org.)


DIPF-Abteilung:
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zuletzt verändert: 11.11.2016