@inproceedings{rouhizadeh-teodoro-2022-ds4dh,
title = "{DS}4{DH} at {S}em{E}val-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models",
author = "Rouhizadeh, Hossein and
Teodoro, Douglas",
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-1.212/",
doi = "10.18653/v1/2022.semeval-1.212",
pages = "1543--1548",
abstract = "In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages. After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model. We evaluated our proposed approach on the multilingual NER dataset at SemEval-2022. The ensemble model provided consistent improvements against the individual models on the multilingual track, achieving a macro F1 performance of 65.2{\%}. However, our results were significantly outperformed by the top ranking systems, achieving thus a baseline performance."
}
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<abstract>In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages. After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model. We evaluated our proposed approach on the multilingual NER dataset at SemEval-2022. The ensemble model provided consistent improvements against the individual models on the multilingual track, achieving a macro F1 performance of 65.2%. However, our results were significantly outperformed by the top ranking systems, achieving thus a baseline performance.</abstract>
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%0 Conference Proceedings
%T DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models
%A Rouhizadeh, Hossein
%A Teodoro, Douglas
%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 rouhizadeh-teodoro-2022-ds4dh
%X In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages. After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model. We evaluated our proposed approach on the multilingual NER dataset at SemEval-2022. The ensemble model provided consistent improvements against the individual models on the multilingual track, achieving a macro F1 performance of 65.2%. However, our results were significantly outperformed by the top ranking systems, achieving thus a baseline performance.
%R 10.18653/v1/2022.semeval-1.212
%U https://aclanthology.org/2022.semeval-1.212/
%U https://doi.org/10.18653/v1/2022.semeval-1.212
%P 1543-1548
Markdown (Informal)
[DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models](https://aclanthology.org/2022.semeval-1.212/) (Rouhizadeh & Teodoro, SemEval 2022)
ACL