High Performance Text Mining for Translator

翻译者的高性能文本挖掘

• 批准号: 10548337
• 负责人: LAWRENCE E HUNTER
• 金额: $ 46.61万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-23 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10548337
• 关键词: Performance; Text; text searching

We propose to build a knowledge provider that will seek out, integrate and provide AIready, BioLink-compatible models via high-performance text-mining of the biomedical literature. Problems with Translator’s current mining of the biomedical literature that we intend to solve include: (1) weaknesses in framework extensibility and benchmarking that make integrating and validating new text-mining approaches difficult; (2) problematic licensing of software, terminologies and other resources that do not adequately support FAIR (and TLC) best practices; (3) processing only PubMed titles and abstracts, not full text publications; (4) Translator’s use of older NLP technology with relatively poor performance; (5) lack of a mechanism for community feedback regarding errors and other problems; (6) lack of continuous updates to add knowledge from new publications; (7) output knowledge representation that is simplistic and vague, failing to reflect the richness of what is expressed in scientific documents.

我们建议建立一个知识提供者，它将寻找、整合和提供 AIready、 通过生物医学文献的高性能文本挖掘实现 BioLink 兼容模型。 Translator目前对我们打算挖掘的生物医学文献存在的问题 解决的问题包括：（1）框架可扩展性和基准测试方面的弱点 集成和验证新的文本挖掘方法很困难； (2) 许可问题 不能充分支持 FAIR（和 TLC）的软件、术语和其他资源 最佳实践； (3) 仅处理 PubMed 标题和摘要，而不处理全文出版物； (4) 翻译器使用较旧的NLP技术，性能相对较差； (5) 缺乏 有关错误和其他问题的社区反馈机制； (6)缺乏连续性 更新以添加新出版物中的知识； (7) 输出知识表示即 简单化和模糊，未能反映科学文献中表达内容的丰富性。