@inproceedings{he-sun-2017-f,
title = "{F}-Score Driven Max Margin Neural Network for Named Entity Recognition in {C}hinese Social Media",
author = "He, Hangfeng and
Sun, Xu",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2113",
pages = "713--718",
abstract = "We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44{\%} improvement over previous state-of-the-art result.",
}
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%0 Conference Proceedings
%T F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media
%A He, Hangfeng
%A Sun, Xu
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F he-sun-2017-f
%X We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44% improvement over previous state-of-the-art result.
%U https://aclanthology.org/E17-2113
%P 713-718
Markdown (Informal)
[F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media](https://aclanthology.org/E17-2113) (He & Sun, EACL 2017)
ACL