论文标题
Hunflair：最先进的生物医学命名实体识别的易于使用的工具
HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
论文作者
论文摘要
摘要：命名实体识别（NER）是生物医学信息提取管道中的重要一步。 NER的工具应易于使用，涵盖多种实体类型，高度准确且适合文本类型和样式的变化。为此，我们提出了饥饿，这是一个NER标记器，涵盖了多种实体类型，该类型集成了广泛使用的NLP框架天赋。饥饿的表现优于其他最先进的独立工具，其平均增益比下一个最佳工具平均增益为7.26 pp，可以使用一个命令安装，仅使用四行代码应用。可用性：通过MIT许可证可以通过Flair框架免费获得饥饿：https：//github.com/flairnlp/flair，并且与所有主要操作系统兼容。联系人：{Weberple，Saengema，Alan.akbik}@Informatik.hu-berlin.de
Summary: Named Entity Recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, highly accurate, and robust towards variations in text genre and style. To this end, we propose HunFlair, an NER tagger covering multiple entity types integrated into the widely used NLP framework Flair. HunFlair outperforms other state-of-the-art standalone NER tools with an average gain of 7.26 pp over the next best tool, can be installed with a single command and is applied with only four lines of code. Availability: HunFlair is freely available through the Flair framework under an MIT license: https://github.com/flairNLP/flair and is compatible with all major operating systems. Contact:{weberple,saengema,alan.akbik}@informatik.hu-berlin.de
