@inproceedings{wang-etal-2022-damo,
title = "{DAMO}-{NLP} at {S}em{E}val-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition",
author = "Wang, Xinyu and
Shen, Yongliang and
Cai, Jiong and
Wang, Tao and
Wang, Xiaobin and
Xie, Pengjun and
Huang, Fei and
Lu, Weiming and
Zhuang, Yueting and
Tu, Kewei and
Lu, Wei and
Jiang, Yong",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.200",
doi = "10.18653/v1/2022.semeval-1.200",
pages = "1457--1468",
abstract = "The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.",
}
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<abstract>The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.</abstract>
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%0 Conference Proceedings
%T DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
%A Wang, Xinyu
%A Shen, Yongliang
%A Cai, Jiong
%A Wang, Tao
%A Wang, Xiaobin
%A Xie, Pengjun
%A Huang, Fei
%A Lu, Weiming
%A Zhuang, Yueting
%A Tu, Kewei
%A Lu, Wei
%A Jiang, Yong
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F wang-etal-2022-damo
%X The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
%R 10.18653/v1/2022.semeval-1.200
%U https://aclanthology.org/2022.semeval-1.200
%U https://doi.org/10.18653/v1/2022.semeval-1.200
%P 1457-1468
Markdown (Informal)
[DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition](https://aclanthology.org/2022.semeval-1.200) (Wang et al., SemEval 2022)
ACL
- Xinyu Wang, Yongliang Shen, Jiong Cai, Tao Wang, Xiaobin Wang, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang, Kewei Tu, Wei Lu, and Yong Jiang. 2022. DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1457–1468, Seattle, United States. Association for Computational Linguistics.
