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Autor:
Reimers, Nils; Eckle-Kohler, Judith; Schnober, Carsten; Kim, Jungi; Gurevych, Iryna:

Titel:
GermEval-2014
Nested named entity recognition with neural networks

Quelle:
In: Faaß, Gertrud;Ruppenhofer, Josef (Hrsg.): Workshop Proceedings of the 12th edition of the KONVENS Conference Hildesheim, Deutschland : Universitätsverlag Hildesheim (2014) , 117-120

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:
Automatisierung, Computerlinguistik, Daten, Evaluation, Information, Modell, Netzwerk, Sprachanalyse, Textanalyse, Wissen


Abstract(original):
Collobert et al. (2011) showed that deep neural network architectures achieve state-of-the-art performance in many fundamental NLP tasks, including Named Entity Recognition (NER). However, results were only reported for English. This paper reports on experiments for German Named Entity Recognition, using the data from the GermEval 2014 shared task on NER. Our system achieves an F1-measure of 75.09% according to the official metric. (DIPF/Orig.)


DIPF-Abteilung:
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last modified Nov 11, 2016