ITR: Mining the Bibliome -- Information Extraction from the Biomedical Literature

ITR：挖掘文献库——从生物医学文献中提取信息

• 批准号: 0205448
• 负责人: Aravind Joshi
• 金额: $ 349.98万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205448&HistoricalAwards=false
• 关键词: ITR; Mining; Bibliome; Information; Extraction

EIA-0205448Joshi, AravindUniversity of PennsylvaniaITR: Mining the Bibliome -- Information Extraction from the BiomedicalLiterature The major goal is the development of qualitatively better methods for automatically extracting information from the biomedical literature, relying on recent research in high-accuracy parsing and shallow semantic analysis. The special focus will be on information relevant to drug development, in collaboration with researchers in the Knowledge Integration and DiscoverySystems group at GlaxoSmithKline. This project will also address several database research problems, including methods for modeling complex, incomplete and changing information using semistructured data, and also ways to connect the text analysis process to an information integration environment that can deal with the wide variety of extant bioinformatic data models, formats, languages and interfaces.The engine of recent progress in language processing research has been linguistic data: text corpora, treebanks, lexicons, test corpora for information retrieval and information extraction, and so on. Much of this data has been created by Penn researchers and published by Penn's Linguistic Data Consortium. Hence, one of our major goals is to develop and publish new linguistic resources in three categories: a large corpus of biomedical text annotated with syntactic structures `Treebank' and shallow semantic structures (proposition bank or `Propbank'; several large sets of biomedical abstracts and full-text articles annotated with entities and relations of interest to drug developers, such as enzyme inhibition by various compounds or genotype/phenotype connections `Factbanks'; and broad-coverage lexicons and tools for the analysis of biomedical texts.

EIA-0205448Joshi，阿拉伯宾夕法尼亚大学ITR：挖掘书目--从生物医学文献中提取信息主要目标是开发从生物医学文献中自动提取信息的定性更好的方法，依赖于最近在高精度语法分析和浅层语义分析方面的研究。与葛兰素史克知识集成和发现系统组的研究人员合作，特别关注与药物开发相关的信息。该项目还将解决几个数据库研究问题，包括使用半结构化数据对复杂、不完整和不断变化的信息进行建模的方法，以及如何将文本分析过程连接到可以处理各种现有生物信息数据模型、格式、语言和接口的信息集成环境中。语言处理研究的最新进展的引擎是语言数据：文本语料库、树库、词典、用于信息检索和信息提取的测试语料库等。其中大部分数据是由宾夕法尼亚大学的研究人员创建的，并由宾夕法尼亚大学的语言数据联盟发布。因此，我们的主要目标之一是开发和出版三类新的语言资源：用句法结构(Treebank)和浅层语义结构(命题库或Propbank)注释的大型生物医学文本语料库；几大套生物医学摘要和全文文章，注释实体和药物开发人员感兴趣的关系，例如各种化合物或基因/表型连接‘Factbank’的酶抑制；以及用于分析生物医学文本的广泛词典和工具。