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Autor*innen: Reimers, Nils; Eckle-Kohler, Judith; Schnober, Carsten; Kim, Jungi; Gurevych, Iryna
Titel: GermEval-2014. Nested named entity recognition with neural networks
Aus: Faaß, Gertrud; Ruppenhofer, Josef (Hrsg.): Workshop Proceedings of the 12th edition of the KONVENS Conference, Hildesheim: Universitätsverlag Hildesheim, 2014 , S. 117-120
URL: http://www.uni-hildesheim.de/konvens2014/data/konvens2014-workshop-proceedings.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Automatisierung; Computerlinguistik; Daten; Evaluation; Information; Modell; Netzwerk; Sprachanalyse; Textanalyse; Wissen
Abstract: 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: Informationszentrum Bildung