Abstract
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.- Anthology ID:
- C16-1087
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 911–921
- Language:
- URL:
- https://aclanthology.org/C16-1087
- DOI:
- Cite (ACL):
- Onur Kuru, Ozan Arkan Can, and Deniz Yuret. 2016. CharNER: Character-Level Named Entity Recognition. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 911–921, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- CharNER: Character-Level Named Entity Recognition (Kuru et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C16-1087.pdf
- Code
- ozanarkancan/char-ner