@inproceedings{pu-etal-2022-cmb,
title = "{CMB} {AI} Lab at {S}em{E}val-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification",
author = "Pu, Keyu and
Liu, Hongyi and
Yang, Yixiao and
Ji, Jiangzhou and
Lv, Wenyi and
He, Yaohan",
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-1.221/",
doi = "10.18653/v1/2022.semeval-1.221",
pages = "1603--1607",
abstract = "This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board."
}
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<abstract>This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.</abstract>
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%0 Conference Proceedings
%T CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification
%A Pu, Keyu
%A Liu, Hongyi
%A Yang, Yixiao
%A Ji, Jiangzhou
%A Lv, Wenyi
%A He, Yaohan
%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 pu-etal-2022-cmb
%X This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.
%R 10.18653/v1/2022.semeval-1.221
%U https://aclanthology.org/2022.semeval-1.221/
%U https://doi.org/10.18653/v1/2022.semeval-1.221
%P 1603-1607
Markdown (Informal)
[CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification](https://aclanthology.org/2022.semeval-1.221/) (Pu et al., SemEval 2022)
ACL