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Autor*innen: Nam, Jinseok
Titel: Semi-supervised neural networks for nested named entity recognition
Aus: Faaß, Getrud;Ruppenhofer, Josef (Hrsg.): Workshop proceedings of the 12th edition of the KONVENS Conference, Hildesheim: Universitätsverlag Hildesheim, 2014 , S. 144-148
URL: http://www.uni-hildesheim.de/konvens2014/data/konvens2014-workshop-proceedings.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Algorithmus; Automatisierung; Computerlinguistik; Daten; Indexierung; Lernen; Netzwerk; Text
Abstract (english): 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: Informationszentrum Bildung