DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models

Hossein Rouhizadeh, Douglas Teodoro


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.
Anthology ID:
2022.semeval-1.212
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1543–1548
Language:
URL:
https://aclanthology.org/2022.semeval-1.212
DOI:
10.18653/v1/2022.semeval-1.212
Bibkey:
Cite (ACL):
Hossein Rouhizadeh and Douglas Teodoro. 2022. DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1543–1548, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models (Rouhizadeh & Teodoro, SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.212.pdf
Video:
 https://aclanthology.org/2022.semeval-1.212.mp4
Data
MultiCoNER