CharNER: Character-Level Named Entity Recognition

@inproceedings{Kuru2016CharNERCN,
  title={CharNER: Character-Level Named Entity Recognition},
  author={Onur Kuru and Ozan Arkan Can and Deniz Yuret},
  booktitle={COLING},
  year={2016}
}
We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag probabilities for each character. These probabilities are then converted to consistent word level named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER performance in seven languages with… CONTINUE READING
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