ONER：从PubMed摘要中的关联字符串组织命名实体识别的工具
ONER: Tool for Organization Named Entity Recognition from Affiliation Strings in PubMed Abstracts
摘要：
从与生物医学相关的文章的从属关系句子中自动提取组织名称是医药营销行业、卫生保健资助机构和公共卫生官员的极大兴趣。它也将有助于其他科学家规范作者姓名，自动创建引文，索引文章和识别潜在的资源或合作者。今天，PubMed中有超过1800万篇与生物医学研究相关的文章，从中获得的信息可以有效地用于节省政府机构在了解科学景观方面花费的大量时间和资源，包括关键的意见领袖和卓越中心。我们提取组织名称的过程涉及到多个字典的多层规则匹配。该系统在组织名称提取中达到了99.6%的f-测度。
英文摘要：
Automatically extracting organization names from the affiliation sentences of articles related to biomedicine is of great interest to the pharmaceutical marketing industry, health care funding agencies and public health officials. It will also be useful for other scientists in normalizing author names, automatically creating citations, indexing articles and identifying potential resources or collaborators. Today there are more than 18 million articles related to biomedical research indexed in PubMed, and information derived from them could be used effectively to save the great amount of time and resources spent by government agencies in understanding the scientific landscape, including key opinion leaders and centers of excellence. Our process for extracting organization names involves multi-layered rule matching with multiple dictionaries. The system achieves 99.6% f-measure in extracting organization names.
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。
