High Performance Text Mining for Translator

我们建议建立一个知识提供商，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的Biolink兼容模型。 翻译者目前挖掘我们想要的生物医学文献的问题 解决方案包括：(1)框架可扩展性和基准测试方面的缺陷 集成和验证新的文本挖掘方法很困难；(2)许可有问题 软件、术语和其他资源不能充分支持FIRE(和TLC) 最佳做法；(3)只处理PubMed的标题和摘要，不处理全文出版物；(4) 翻译人员使用较旧的NLP技术，性能相对较差；(5)缺乏 关于错误和其他问题的社区反馈机制；(6)缺乏连续性 更新以添加来自新出版物的知识；(7)输出知识表示，即 简单化和含糊，未能反映科学文献中所表达的内容的丰富性。

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.