Beyond Abstracts: Issues in Mining Full Texts

超越摘要：挖掘全文的问题

• 批准号: 7287359
• 负责人: LAWRENCE E HUNTER
• 金额: $ 35.06万
• 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 2009-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7287359
• 关键词: Adoption; Affect; Agreement; Arts; Biomedical Research; Body of uterus; Collection; Computational Technique; Data; Development; Evaluation; Gold; Growth; Human; Judgment; Language; Lead; Literature; Machine Learning; Memory; Methods; Metric; Mining; Modeling; Molecular Biology; Numbers; Pattern; Peer Review; Performance; Play; Process; Public Health; Publishing; Range; Representations, Knowledge (Computer); Research; Retrieval; Review Literature; Role; Sampling; Scheme; Scientist; Standards of Weights and Measures; System; Techniques; Technology; Testing; Text; Training; Work; abstracting; base; concept; improved; information organization; interest; journal article; language processing; novel strategies; prototype; size; technology development; text searching; tool

DESCRIPTION (provided by applicant): Biomedical language processing, the application of computational techniques to human-generated texts in biomedicine, is an increasingly important enabling technology for basic and applied biomedical research. The exponential growth of the peer-reviewed literature and the breakdown of disciplinary boundaries associated with high-throughput techniques have increased the importance of automated tools for keeping scientists abreast of all of the published material relevant to their work. However, despite decades of research, the performance of state-of-the-art tools for basic language processing tasks like information extraction and document retrieval remain below the level necessary for adequate utility and widespread adoption of this technology. The development, performance and evaluation of text mining systems depend crucially on the availability of appropriate corpora: collections of representative documents that have been annotated with human judgments relevant to a language-processing task. Corpora play two roles in the development of this technology: first, they act as "gold standards" by which alternative automated methods can be fairly compared, and second, they provide data for the training of statistical and machine learning systems that create empirical models of patterns in language use. The conventional view is that corpora are neutral, random samples of the domain of interest. Our preliminary work suggests that the restrictions in size, quality, genre, and representational schema of the small number of existing corpora are themselves a critical limiting factor for near-term breakthroughs in biomedical text processing technology. Therefore, we propose to test the following hypothesis: Creation of large, high-quality, biomedical corpora from multiple genres will lead to significant improvements in the performance of biomedical text mining systems and the creation of new approaches to text mining tasks. Specific aims include constructing several large corpora covering a range of genres and incorporating a rich knowledge representation; identifying factors that affect differential performance on full text versus abstracts; and developing new methods for language processing, especially of full text. Because improvements in the ability to automatically extract information from many textual genres will assist scientists and clinicians in the crucial task of keeping up with the burgeoning biomedical literature, the potential public health impact is quite large.

描述（由申请人提供）： 生物医学语言处理是将计算技术应用于生物医学中人类生成的文本，是基础和应用生物医学研究中越来越重要的使能技术。同行评审文献的指数增长和与高通量技术相关的学科界限的打破增加了自动化工具的重要性，使科学家能够及时了解与其工作相关的所有已发表材料。然而，尽管经过数十年的研究，用于信息提取和文档检索等基本语言处理任务的最先进工具的性能仍然低于充分利用和广泛采用该技术所需的水平。文本挖掘系统的开发、性能和评估关键取决于适当语料库的可用性：已用与语言处理任务相关的人类判断注释的代表性文档的集合。语料库在这项技术的发展中扮演着两个角色：第一，它们作为“黄金标准”，可以公平地比较替代的自动化方法;第二，它们为统计和机器学习系统的训练提供数据，这些系统可以创建语言使用模式的经验模型。传统的观点认为语料库是兴趣领域的中性随机样本。我们的初步工作表明，在规模，质量，体裁和现有语料库的数量少的代表性模式的限制本身是一个关键的限制因素，在生物医学文本处理技术的近期突破。因此，我们建议测试以下假设：创建大型，高质量的，生物医学语料库从多个流派将导致显着改善的性能，生物医学文本挖掘系统和创建新的方法来文本挖掘任务。具体目标包括构建几个大型语料库，涵盖一系列体裁，并纳入丰富的知识表示;确定影响全文与摘要差异表现的因素;开发语言处理，特别是全文的新方法。由于从许多文本类型中自动提取信息的能力的提高将有助于科学家和临床医生完成跟上新兴生物医学文献的关键任务，因此潜在的公共卫生影响相当大。