@inproceedings{yang-etal-2017-neural-reranking,
title = "Neural Reranking for Named Entity Recognition",
author = "Yang, Jie and
Zhang, Yue and
Dong, Fei",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_101",
doi = "10.26615/978-954-452-049-6_101",
pages = "784--792",
abstract = "We propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, {``}PER was born in LOC{''} can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.",
}
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%0 Conference Proceedings
%T Neural Reranking for Named Entity Recognition
%A Yang, Jie
%A Zhang, Yue
%A Dong, Fei
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F yang-etal-2017-neural-reranking
%X We propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as Barack Obama, into their entity types, such as PER. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, “PER was born in LOC” can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.
%R 10.26615/978-954-452-049-6_101
%U https://doi.org/10.26615/978-954-452-049-6_101
%P 784-792
Markdown (Informal)
[Neural Reranking for Named Entity Recognition](https://doi.org/10.26615/978-954-452-049-6_101) (Yang et al., RANLP 2017)
ACL
- Jie Yang, Yue Zhang, and Fei Dong. 2017. Neural Reranking for Named Entity Recognition. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 784–792, Varna, Bulgaria. INCOMA Ltd..