NSURL-2019 Task 7: Named Entity Recognition (NER) in Farsi

Beheshti-NER: Persian Named Entity Recognition Using BERT

Named entity recognition is a natural language processing task to recognize and extract spans of text associated with named entities and classify them in semantic Categories. Google BERT is a deep bidirectional language model, pre-trained on large corpora that can be fine-tuned to solve many NLP tasks such as question answering, named entity recognition, part of speech tagging and etc. In this paper, we use the pre-trained deep bidirectional network, BERT, to make a model for named entity recognition in Persian. We also compare the results of our model with the previous state of the art result…

LLM-RM 在 SemEval-2023 任务 2 中的表现：基于 XLM-RoBERTa 的多语言复杂命名实体识别

LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa

本文针对多语言复杂命名实体识别任务，团队LLM-RM参与SemEval 2023任务2，通过在12种语言数据集上微调XLM-RoBERTa基础模型、利用其跨语言表示来解决该问题。

SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models

Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play to the differentiation advantages of different models, especially in the open domain. This paper describes our NER system in the SemEval 2022 task11: MultiCoNER. We proposed an effective system to adaptively ensemble pre-trained language models by a Transformer layer. By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effec…

SemEval-2023 任务 2：细粒度多语言命名实体识别（MultiCoNER 2）

SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)

本文介绍 SemEval-2023 任务 2（MultiCoNER 2），该任务在 12 种语言、220 万实例上评测细粒度命名实体识别，发现融合外部知识的 transformer 方法表现最佳，但复杂实体类别和噪声数据仍是主要挑战。