GermEval 2014 Named Entity Recognition Shared Task
A GermEval 2014 Named Entity Recognition Shared Task is a Shared Task for making available CC-BY Licensed German Named Entity Recognition Systems.
- AKA: GermEval 2014 NER Shared Task.
- See: GermEval 2014 Dataset, Annotation Task, Word Embedding, NoSta-D, Bidirectional LSTM-CNN-CRF Training System.
References
2018
- (GermEval 2014 NER, 2018) ⇒ https://sites.google.com/site/germeval2014ner/ Retrieved:2018-08-12
- QUOTE: Named Entity Recognition (NER) has been shown useful for a wide range of NLP tasks from Information Extraction to Speech Processing. For Semantic Web applications like entity linking, NER is a crucial preprocessing step.
Even though German is a relatively well-resourced language, NER for German has been challenging, both because capitalization is a less useful feature than in other languages, and because existing training data sets are encumbered by license problems. Therefore, no publicly available NER taggers for German exist that are free of usage restrictions and perform at high levels of accuracy.
The GermEval 2014 NER Shared Task is an event that makes available CC-licensed German data with NER annotation with the goal of significantly advancing the state of the art in German NER and to push the field of NER towards nested representations of named entities.
We invite all researchers and industry professionals to participate in the challenge and to demonstrate their capabilities of creating a Named Entity Recognition system for German. The systems will be evaluated on a manually created test set. Training data and development data will be provided. There are no restrictions regarding the type of NER system submissions, and no restrictions on the use of external data, background corpora, lexical resources etc.
GermanEval 2014 NER is associated with the KONVENS 2014 conference and will take place as a KONVENS workshop at Hildesheim on October 7th 2014.
- QUOTE: Named Entity Recognition (NER) has been shown useful for a wide range of NLP tasks from Information Extraction to Speech Processing. For Semantic Web applications like entity linking, NER is a crucial preprocessing step.