UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition

Elisa Schneider, Renzo M. Rivera-Zavala, Paloma Martinez, Claudia Moro, Emerson Paraiso


Abstract
This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data.
Anthology ID:
2022.semeval-1.199
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:
1448–1456
Language:
URL:
https://aclanthology.org/2022.semeval-1.199
DOI:
10.18653/v1/2022.semeval-1.199
Bibkey:
Cite (ACL):
Elisa Schneider, Renzo M. Rivera-Zavala, Paloma Martinez, Claudia Moro, and Emerson Paraiso. 2022. UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1448–1456, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition (Schneider et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.199.pdf
Video:
 https://aclanthology.org/2022.semeval-1.199.mp4
Data
CoNLL 2002CoNLL 2003MultiCoNER