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GermEval-2014: Nested Named Entity Recognition with Neural Networks

  • 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.

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Author:Nils Reimers, Judith Eckle-Kohler, Carsten Schnober, Jungi Kim, Iryna Gurevych
Parent Title (English):Workshop proceedings of the 12th edition of the KONVENS conference
Document Type:Conference Proceeding
Date of Publication (online):2014/11/25
Release Date:2014/11/25
Tag:NER; Named entity recognition; neural networks
GND Keyword:Computerlinguistik
First Page:117
Last Page:120
PPN:Link zum Katalog
Institutes:Fachbereich III / Informationswissenschaft und Sprachtechnologie
DDC classes:400 Sprache / 400 Sprache, Linguistik
Collections:KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014
Licence (German):License LogoCreative Commons - Namensnennung 3.0