@inproceedings{jia-etal-2019-cross,
title = "Cross-Domain {NER} using Cross-Domain Language Modeling",
author = "Jia, Chen and
Liang, Xiaobo and
Zhang, Yue",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1236/",
doi = "10.18653/v1/P19-1236",
pages = "2464--2474",
abstract = "Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods."
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<abstract>Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.</abstract>
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%0 Conference Proceedings
%T Cross-Domain NER using Cross-Domain Language Modeling
%A Jia, Chen
%A Liang, Xiaobo
%A Zhang, Yue
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F jia-etal-2019-cross
%X Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.
%R 10.18653/v1/P19-1236
%U https://aclanthology.org/P19-1236/
%U https://doi.org/10.18653/v1/P19-1236
%P 2464-2474
Markdown (Informal)
[Cross-Domain NER using Cross-Domain Language Modeling](https://aclanthology.org/P19-1236/) (Jia et al., ACL 2019)
ACL
- Chen Jia, Xiaobo Liang, and Yue Zhang. 2019. Cross-Domain NER using Cross-Domain Language Modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2464–2474, Florence, Italy. Association for Computational Linguistics.