Papers › CharNER: Character-Level Named Entity Recognition

CharNER: Character-Level Named Entity Recognition

1 Dec 2016COLING 2016 12archive 2025-07-28

Onur Kuru, Ozan Arkan Can, Deniz Yuret

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 the same basic model using only labeled NER data and no hand-engineered features or other external resources like syntactic taggers or Gazetteers.

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Decoder Feature Engineering NER Named Entity Recognition Named Entity Recognition (NER)Sentence TAG Word Embeddings named-entity-recognition

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