DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models
Hossein Rouhizadeh, Douglas Teodoro · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (Multi-CoNER).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.