Corpus ID: 6761343

GermaNER: Free Open German Named Entity Recognition Tool

@inproceedings{Benikova2015GermaNERFO,
  title={GermaNER: Free Open German Named Entity Recognition Tool},
  author={Darina Benikova and Seid Muhie Yimam and Prabhakaran Santhanam and Chris Biemann},
  booktitle={GSCL},
  year={2015}
}
With this paper, we release a freely available statistical German Named Entity Tagger based on conditional random fields (CRF). The tagger is trained and evaluated on the GermEval 2014 dataset for named entity recognition and comes close to the performance of the best (proprietary) system in the competition with 76% F-measure test set performance on the four standard NER classes. We describe a range of features and their influence on German NER classification and provide a comparative… Expand
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References

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The approach to creating annotation guidelines based on linguistic and semantic considerations is described, and how they were iteratively refined and tested in the early stages of annotation to arrive at the largest publicly available dataset for German NER, consisting of over 31,000 manually annotated sentences from German Wikipedia and German online news. Expand
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This work alleviates the small size of available NER training corpora for German with distributional generalization features trained on large unlabelled corpora with a freely available optimized Named Entity Recognizer for German. Expand
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This paper presents the best performing Named Entity Recognition system in the GermEval 2014 Shared Task. Our approach combines semi-automatically created lexical resources with an ensemble of binaryExpand
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MoSTNER is a German NER system based on machine learning with log-linear models and morphology-aware features. We use morphological analysis with Morphisto for generating features, moreover we useExpand
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This paper reports on experiments for German Named Entity Recognition, using the data from the GermEval 2014 shared task on NER, which achieves an F1-measure of 75.09% according to the official metric. Expand
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In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such asExpand
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Some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system are analyzed, and several solutions to these challenges are developed. Expand
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