CharNER: Character-Level Named Entity Recognition

Onur Kuru, Ozan Arkan Can, Deniz Yuret


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://www.aclweb.org/anthology/C16-1087
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1087.pdf