PublishInCovid19在WNUT 2020共享任务-1：使用结构化学习环境和情境化嵌入的湿实验室协议中的实体识别
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings
摘要：
在本文中，我们描述了我们用于解决湿实验室协议上的实体识别任务的方法-EMNLP WNUT-2020研讨会中的共享任务。我们的方法由两个阶段组成。在第一阶段，我们使用各种上下文化的词嵌入（如Flair，基于BERT的）和BiLSTM-CRF模型进行实验，以获得性能最佳的架构。在第二阶段，我们创建了一个由11个BiLSTM-CRF模型组成的系综。单个模型在完整数据集的随机训练验证分割上进行训练。在这里，我们还尝试了不同的输出合并方案，包括多数投票和结构化学习集成（SLE）。我们最终提交的作品在实体跨度的部分和精确匹配方面分别获得了0.8175和0.7757的微观F1分数。我们分别在部分匹配和完全匹配方面排名第一和第二。
英文摘要：
In this paper, we describe the approach that we employed to address the task of Entity Recognition over Wet Lab Protocols -- a shared task in EMNLP WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase, we experiment with various contextualised word embeddings (like Flair, BERT-based) and a BiLSTM-CRF model to arrive at the best-performing architecture. In the second phase, we create an ensemble composed of eleven BiLSTM-CRF models. The individual models are trained on random train-validation splits of the complete dataset. Here, we also experiment with different output merging schemes, including Majority Voting and Structured Learning Ensembling (SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for the partial and exact match of the entity spans, respectively. We were ranked first and second, in terms of partial and exact match, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
