@inproceedings{tavan-najafi-2022-marsan,
title = "{M}ar{S}an at {S}em{E}val-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder",
author = "Tavan, Ehsan and
Najafi, Maryam",
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-1.226",
doi = "10.18653/v1/2022.semeval-1.226",
pages = "1639--1647",
abstract = "The multilingual complex named entity recognition task of SemEval2020 required participants to detect semantically ambiguous and complex entities in 11 languages. In order to participate in this competition, a deep learning model is being used with the T5 text-to-text language model and its multilingual version, MT5, along with the transformer{'}s encoder module. The subtoken check has also been introduced, resulting in a 4{\%} increase in the model F1-score in English. We also examined the use of the BPEmb model for converting input tokens to representation vectors in this research. A performance evaluation of the proposed entity detection model is presented at the end of this paper. Six different scenarios were defined, and the proposed model was evaluated in each scenario within the English development set. Our model is also evaluated in other languages.",
}
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<abstract>The multilingual complex named entity recognition task of SemEval2020 required participants to detect semantically ambiguous and complex entities in 11 languages. In order to participate in this competition, a deep learning model is being used with the T5 text-to-text language model and its multilingual version, MT5, along with the transformer’s encoder module. The subtoken check has also been introduced, resulting in a 4% increase in the model F1-score in English. We also examined the use of the BPEmb model for converting input tokens to representation vectors in this research. A performance evaluation of the proposed entity detection model is presented at the end of this paper. Six different scenarios were defined, and the proposed model was evaluated in each scenario within the English development set. Our model is also evaluated in other languages.</abstract>
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%0 Conference Proceedings
%T MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder
%A Tavan, Ehsan
%A Najafi, Maryam
%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 tavan-najafi-2022-marsan
%X The multilingual complex named entity recognition task of SemEval2020 required participants to detect semantically ambiguous and complex entities in 11 languages. In order to participate in this competition, a deep learning model is being used with the T5 text-to-text language model and its multilingual version, MT5, along with the transformer’s encoder module. The subtoken check has also been introduced, resulting in a 4% increase in the model F1-score in English. We also examined the use of the BPEmb model for converting input tokens to representation vectors in this research. A performance evaluation of the proposed entity detection model is presented at the end of this paper. Six different scenarios were defined, and the proposed model was evaluated in each scenario within the English development set. Our model is also evaluated in other languages.
%R 10.18653/v1/2022.semeval-1.226
%U https://aclanthology.org/2022.semeval-1.226
%U https://doi.org/10.18653/v1/2022.semeval-1.226
%P 1639-1647
Markdown (Informal)
[MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder](https://aclanthology.org/2022.semeval-1.226) (Tavan & Najafi, SemEval 2022)
ACL