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Title: Nested Named Entity Recognition Revisited
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  more » « less
Award ID(s):
1741441
NSF-PAR ID:
10075233
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Volume:
1 (Long papers)
Page Range / eLocation ID:
861 - 871
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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