Many areas, such as the biological and healthcare domain, artistic works, and
organization names, have nested, overlapping, discontinuous entity mentions
that may even be syntactically or semantically ambiguous in practice.
Traditional sequence tagging algorithms are unable to recognize these complex
mentions because they may violate the assumptions upon which sequence tagging
schemes are founded. In this paper, we describe our contribution to SemEval
2022 Task 11 on identifying such complex Named Entities. We have leveraged the
ensemble of multiple ELECTRA-based models that were exclusively pretrained on
the Bangla language with the performance of ELECTRA-based models pretrained on
English to achieve competitive performance on the Track-11. Besides providing a
system description, we will also present the outcomes of our experiments on
architectural decisions, dataset augmentations, and post-competition findings.

本文介绍了我们在 SemEval 2022 Task 11 中研究的复杂命名实体识别问题的研究，使用了多种基于 ELECTRA 的模型以实现优秀的性能，同时讨论了一些技术方面的决策和实验结果。