NanoNER：利用专家知识与远程监督的纳米生物学命名实体识别
NanoNER: Named Entity Recognition for nanobiology using experts' knowledge and distant supervision
AI总结：
针对纳米生物学领域，提出基于专家知识与远程监督的NER模型NanoNER，在728篇全文语料上对已知实体F1达0.98，并能以0.77-0.81精确度发现新实体，且所需人力少，可推广至其他专业领域。
摘要：
本文介绍了NanoNER的训练与评估，NanoNER是一个用于纳米生物学的命名实体识别（NER）模型。NER是指在非结构化文本片段中识别特定实体，通常是自然语言处理（NLP）和信息抽取中的首要任务。我们模型的目标是识别领域专家先前确定的、构成该领域基本知识的实体。基于本体（ontology），它为我们提供了领域词汇和分类体系，我们实现了一个迭代过程，使专家能够确定与当前领域相关的实体。随后，我们深入探讨了远程监督学习在NER中的潜力，支持该方法能以最少的人力增加标注数据的数量。在我们包含超过12万次实体出现的728篇纳米生物学全文文章完整语料库上，NanoNER在识别先前已知实体方面获得了0.98的F1分数。我们的模型还展示了其在文本中发现新实体的能力，精确度分数在0.77到0.81之间。消融实验进一步证实了这一点，并使我们能够评估我们的方法对外部资源的依赖性。它突显了该方法对资源的依赖性，同时也证实了其能够重新发现高达30%的被消融术语。本文详细介绍了所采用的方法论、实验设计和主要发现，为未来在专业领域NER的相关研究提供了宝贵的见解和方向。此外，由于我们的方法所需人力极少，我们相信它可以推广到其他专业领域。
英文摘要：
Here we present the training and evaluation of NanoNER, a Named Entity Recognition (NER) model for Nanobiology. NER consists in the identification of specific entities in spans of unstructured texts and is often a primary task in Natural Language Processing (NLP) and Information Extraction. The aim of our model is to recognise entities previously identified by domain experts as constituting the essential knowledge of the domain. Relying on ontologies, which provide us with a domain vocabulary and taxonomy, we implemented an iterative process enabling experts to determine the entities relevant to the domain at hand. We then delve into the potential of distant supervision learning in NER, supporting how this method can increase the quantity of annotated data with minimal additional manpower. On our full corpus of 728 full-text nanobiology articles, containing more than 120k entity occurrences, NanoNER obtained a F1-score of 0.98 on the recognition of previously known entities. Our model also demonstrated its ability to discover new entities in the text, with precision scores ranging from 0.77 to 0.81. Ablation experiments further confirmed this and allowed us to assess the dependency of our approach on the external resources. It highlighted the dependency of the approach to the resource, while also confirming its ability to rediscover up to 30% of the ablated terms. This paper details the methodology employed, experimental design, and key findings, providing valuable insights and directions for future related researches on NER in specialized domain. Furthermore, since our approach require minimal manpower , we believe that it can be generalized to other specialized fields.
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。
