Corpus ID: 49421402

Character-Level Feature Extraction with Densely Connected Networks

@article{Lee2018CharacterLevelFE,
  title={Character-Level Feature Extraction with Densely Connected Networks},
  author={Chanhee Lee and Young-Bum Kim and Dongyub Lee and Heuiseok Lim},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.09089}
}
Generating character-level features is an important step for achieving good results in various natural language processing tasks. [...] Key Method The proposed method does not require any language or task specific assumptions, and shows robustness and effectiveness while being faster than CNN- or RNN-based methods. Evaluating this method on three sequence labeling tasks - slot tagging, Part-of-Speech (POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art performance with a 96.62 F1-score…Expand
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