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    <abstract>Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation. In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account. In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively. Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets.</abstract>
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%0 Conference Proceedings
%T Named Entity Recognition for Social Media Texts with Semantic Augmentation
%A Nie, Yuyang
%A Tian, Yuanhe
%A Wan, Xiang
%A Song, Yan
%A Dai, Bo
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F nie-etal-2020-named
%X Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation. In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account. In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively. Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets.
%R 10.18653/v1/2020.emnlp-main.107
%U https://aclanthology.org/2020.emnlp-main.107
%U https://doi.org/10.18653/v1/2020.emnlp-main.107
%P 1383-1391

Markdown (Informal)

[Named Entity Recognition for Social Media Texts with Semantic Augmentation](https://aclanthology.org/2020.emnlp-main.107) (Nie et al., EMNLP 2020)

• Named Entity Recognition for Social Media Texts with Semantic Augmentation (Nie et al., EMNLP 2020)

ACL

• Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, and Bo Dai. 2020. Named Entity Recognition for Social Media Texts with Semantic Augmentation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1383–1391, Online. Association for Computational Linguistics.