Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics. Our approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules. Our results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems.

中文翻译：

---

C-Norm：少数镜头实体归一化的神经方法

实体规范化是一项重要的信息提取任务，在过去的十年中，尤其是在生物医学和生命科学领域，它得到了新的关注。在这些领域中，更广泛地说，在所有专业领域中，对于基于机器学习的最新方法而言，这项任务仍然具有挑战性，因为这些方法难以处理高度多类，很少学习的问题。为了解决这个问题，我们提出了一种新的神经方法C-Norm，它将标准和弱监督，本体论知识集成和分布语义协同地结合起来。我们的方法大大优于在BioNLP Open Shared Tasks 2019的细菌生物群落数据集上评估的所有方法，而无需集成任何手动设计的特定领域规则。