TY - GEN
T1 - Nested named entity recognition revisited
AU - Katiyar, Arzoo
AU - Cardie, Claire
N1 - Funding Information:
We thank Wei Lu for help with the datasets. We also thank Jack Hessel, Vlad Niculae and the reviewers for their helpful comments and feedback. This work was supported in part by NSF grant SES-1741441 and DARPA DEFT Grant FA8750-13-2-0015. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of NSF, DARPA or the U.S. Government.
Publisher Copyright:
© 2018 The Association for Computational Linguistics.
PY - 2018
Y1 - 2018
N2 - We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-The-Art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.
AB - We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-The-Art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.
UR - http://www.scopus.com/inward/record.url?scp=85062538154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062538154&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85062538154
T3 - NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
SP - 861
EP - 871
BT - Long Papers
PB - Association for Computational Linguistics (ACL)
T2 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Y2 - 1 June 2018 through 6 June 2018
ER -