Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 1 of 1
Back to Result List

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.

Download full text files

Export metadata

Additional Services

Share in Twitter    Search Google Scholar    frontdoor_oas
Author:Nils Reimers, Judith Eckle-Kohler, Carsten Schnober, Jungi Kim, Iryna Gurevych
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
Source:Workshop Proceedings of the 12th KONVENS 2014
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