@inproceedings{zhang-etal-2023-srcb-semeval,
title = "{SRCB} at {S}em{E}val-2023 Task 2: A System of Complex Named Entity Recognition with External Knowledge",
author = "Zhang, Yuming and
Li, Hongyu and
Zhang, Yongwei and
Jiang, Shanshan and
Dong, Bin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.92",
doi = "10.18653/v1/2023.semeval-1.92",
pages = "671--678",
abstract = "The MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team SRCB proposes an external knowledge based system, where we utilize 3 different types of external knowledge retrieved in different ways. Given an original text, our system retrieves the possible labels and the descriptions for each potential entity detected by a mention detection model. And we also retrieve a related document as extra context from Wikipedia for each original text. We concatenate the original text with the external knowledge as the input of NER models. The informative contextual representations with external knowledge significantly improve the NER performance in both Chinese and English tracks. Our system win the 3rd place in the Chinese track and the 6th place in the English track.",
}
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<abstract>The MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team SRCB proposes an external knowledge based system, where we utilize 3 different types of external knowledge retrieved in different ways. Given an original text, our system retrieves the possible labels and the descriptions for each potential entity detected by a mention detection model. And we also retrieve a related document as extra context from Wikipedia for each original text. We concatenate the original text with the external knowledge as the input of NER models. The informative contextual representations with external knowledge significantly improve the NER performance in both Chinese and English tracks. Our system win the 3rd place in the Chinese track and the 6th place in the English track.</abstract>
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%0 Conference Proceedings
%T SRCB at SemEval-2023 Task 2: A System of Complex Named Entity Recognition with External Knowledge
%A Zhang, Yuming
%A Li, Hongyu
%A Zhang, Yongwei
%A Jiang, Shanshan
%A Dong, Bin
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-srcb-semeval
%X The MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team SRCB proposes an external knowledge based system, where we utilize 3 different types of external knowledge retrieved in different ways. Given an original text, our system retrieves the possible labels and the descriptions for each potential entity detected by a mention detection model. And we also retrieve a related document as extra context from Wikipedia for each original text. We concatenate the original text with the external knowledge as the input of NER models. The informative contextual representations with external knowledge significantly improve the NER performance in both Chinese and English tracks. Our system win the 3rd place in the Chinese track and the 6th place in the English track.
%R 10.18653/v1/2023.semeval-1.92
%U https://aclanthology.org/2023.semeval-1.92
%U https://doi.org/10.18653/v1/2023.semeval-1.92
%P 671-678
Markdown (Informal)
[SRCB at SemEval-2023 Task 2: A System of Complex Named Entity Recognition with External Knowledge](https://aclanthology.org/2023.semeval-1.92) (Zhang et al., SemEval 2023)
ACL