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Autor:
Nam, Jinseok:

Titel:
Semi-supervised neural networks for nested named entity recognition

Quelle:
In: Faaß, Getrud;Ruppenhofer, Josef (Hrsg.): Workshop proceedings of the 12th edition of the KONVENS Conference Hildesheim : Universitätsverlag Hildesheim (2014) , 144-148

URL des Volltextes:
http://www.uni-hildesheim.de/konvens2014/data/konvens2014-workshop-proceedings.pdf

Sprache:
Englisch

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

Schlagwörter:
Algorithmus, Automatisierung, Computerlinguistik, Daten, Indexierung, Lernen, Netzwerk, Text


Abstract(englisch):
In this paper, we investigate a semi-supervised learning approach based on neural networks for nested named entity recognition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network architecture to deal with named entities in both levels simultaneously and to improve generalization performance on the classes that have a small number of labelled examples. (DIPF/Autor)


DIPF-Abteilung:
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last modified Nov 11, 2016