Biomedical Language Processing Writ Large: Scaling to all of PubMedCentral

生物医学语言处理显而易见：扩展到所有 PubMedCentral

• 批准号: 7781934
• 负责人: LAWRENCE E HUNTER
• 金额: $ 50.56万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2014-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7781934
• 关键词: Biological; Collection; Complex; Computer Analysis; Computers; Data; Data Set; Databases; Development; Disease; Evaluation Research; Funding; Gene Expression; Genes; Genome; Goals; Harvest; Health; High Performance Computing; Human; Imagery; Journals; Knowledge; Language; Linguistics; Literature; Methods; Molecular; Natural Language Processing; Nature; Pharmaceutical Preparations; Process; Publications; Reading; Research; Research Personnel; Resolution; Resources; Staging; System; Techniques; Technology; Text; Work; biomedical ontology; clinically relevant; information organization; knowledge base; language processing; scale up; text searching; tool

DESCRIPTION (provided by applicant): Recent developments in text mining research, and in scientific publication, have brought us to the moment when the long-standing potential of natural language processing technology to benefit biomedical researchers may finally be realized. Technological advances, recent results in computational linguistics, maturation of biomedical ontology, and the advent of resources such as PubMedCentral have set the stage for an attempt at an integrated computational analysis of a large proportion of the full text biomedical literature. Such an analysis has the potential to dramatically extend the way that biomedical researchers can effectively use the scientific literature, particularly in the analysis of genome-scale datasets, broadly accelerating and increasing the efficiency of scientific discovery. We hypothesize that it is now possible to extract a wide variety of ontologically-grounded entities and relationships by processing the entire PubMedCentral document collection accurately and with good coverage, to use this extracted information to produce new genres of scientifically valuable tools and analysis techniques, and to demonstrate its utility in the analysis of genome-scale data. The challenges that we plan to overcome range from fundamental linguistic issues (e.g. cross- document coreference resolution) to high-performance computing (e.g. scaling up integrated processing to include millions of complex documents), to fielding practical systems that can exploit enormous knowledge-bases to accelerate the analysis of very large molecular data sets.

描述（由申请人提供）： 文本挖掘研究和科学出版物的最新发展，使我们看到了自然语言处理技术长期以来造福生物医学研究人员的潜力终于可以实现的时刻。技术的进步，最近的结果在计算语言学，生物医学本体的成熟，和资源的出现，如PubMedCentral已经设置了一个阶段，试图在一个综合的计算分析的大部分全文生物医学文献。这种分析有可能极大地扩展生物医学研究人员有效利用科学文献的方式，特别是在分析基因组规模的数据集时，从而广泛加速和提高科学发现的效率。我们假设，现在可以通过准确地处理整个PubMedCentral文档集并具有良好的覆盖率来提取各种各样的基于本体的实体和关系，使用这些提取的信息来产生新类型的有科学价值的工具和分析技术，并证明其在基因组规模数据分析中的实用性。我们计划克服的挑战范围从基本的语言学问题（例如跨文档共指消解）到高性能计算（例如扩大集成处理以包括数百万复杂文档），再到部署可以利用巨大知识库来加速分析非常大的分子数据集的实用系统。