{"title":"了解命名实体识别:更好的信息检索","authors":"Borui Zhang","doi":"10.1080/02763869.2024.2335139","DOIUrl":null,"url":null,"abstract":"<p><p>Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.</p>","PeriodicalId":39720,"journal":{"name":"Medical Reference Services Quarterly","volume":"43 2","pages":"196-202"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Getting to Know Named Entity Recognition: Better Information Retrieval.\",\"authors\":\"Borui Zhang\",\"doi\":\"10.1080/02763869.2024.2335139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.</p>\",\"PeriodicalId\":39720,\"journal\":{\"name\":\"Medical Reference Services Quarterly\",\"volume\":\"43 2\",\"pages\":\"196-202\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Reference Services Quarterly\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/02763869.2024.2335139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Reference Services Quarterly","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/02763869.2024.2335139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0
摘要
命名实体识别(NER)是一种功能强大的计算机系统,自 20 世纪 90 年代初以来,它利用各种计算策略从原始文本输入中提取信息。随着人工智能和计算技术的飞速发展,NER 模型获得了极大的关注,并成为众多专业领域的基础工具,用于组织研究和实际应用中的非结构化数据。这一点在医疗和保健领域尤为明显,NER 模型对于从复杂文档中有效提取关键信息至关重要,而这些文档对于人工审核来说具有挑战性。尽管 NER 取得了成功,但在充分理解自然语言的细微差别方面仍存在局限性。不过,开发更先进、更方便用户使用的模型有望大大改善专业用户的工作体验。
Getting to Know Named Entity Recognition: Better Information Retrieval.
Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.
期刊介绍:
This highly acclaimed, peer-reviewed journal is an essential working tool for medical and health sciences librarians. For those professionals who provide reference and public services to health sciences personnel in clinical, educational, or research settings, Medical Reference Services Quarterly covers topics of current interest and practical value in the areas of reference in medicine and related specialties, the biomedical sciences, nursing, and allied health. This exciting and comprehensive resource regularly publishes brief practice-oriented articles relating to medical reference services, with an emphasis on user education, database searching, and electronic information. Two columns feature the Internet and informatics education.