Exploring Natural Language Processing, Image Processing, Machine Learning, and Us

描述（由申请人提供）：大多数生物医学文本挖掘系统只针对文本信息，不提供对其他重要数据（如图）的智能访问。在生物医学文献中，数字通常比任何其他文献都更能代表发现的“证据”。全文生物医学文章几乎总是包含图像，这些图像是生物医学知识发现的关键内容。生物医学科学家需要访问图像来验证研究事实，并制定或测试新的研究假设。评估表明，文献中报告的文本陈述经常是嘈杂的（即，包含“虚假事实”）。捕捉本质上是实验“证据”的图像来支持文本“事实”，将有利于生物医学信息系统，数据库和生物医学科学家。我们正在开发一个生物医学文献数字搜索引擎BioFigureSearch。我们在自然语言处理、图像处理、机器学习和用户界面方面开发创新算法和模型。交付成果将是新颖的生物医学自然语言图形处理（bNLfP）算法和iBioFigureSearch，使生物医学科学家能够有效地访问图形数据，以及开源工具，将增强生物医学信息检索，摘要和问题回答。我们将开发的bNLfP算法可以应用或集成到其他生物医学文本挖掘系统中。 公共卫生相关性：该项目提出了自然语言处理，图像处理，机器学习和用户界面方面的创新算法和模型，以响应生物医学查询返回数字。预计所开发的算法、模型和工具将大大提高生物医学科学家对文献中报告的数字的访问，从而加快生物医学知识的发现。

DESCRIPTION (provided by applicant): Most biomedical text mining systems target only text information and do not provide intelligent access to other important data such as Figures. More than any other documentation, figures usually represent the "evidence" of discovery in the biomedical literature. Full-text biomedical articles nearly always incorporate images that are the crucial content of biomedical knowledge discovery. Biomedical scientists need to access images to validate research facts and to formulate or to test novel research hypotheses. Evaluation has shown that textual statements reported in the literature are frequently noisy (i.e., contain "false facts"). Capturing images that are essentially experimental "evidence" to support the textual "fact" will benefit biomedical information systems, databases, and biomedical scientists. We are developing a biomedical literature figure search engine BioFigureSearch. We develop innovative algorithms and models in natural language processing, image processing, machine learning and user interfacing. The deliverables will be novel biomedical natural language figure processing (bNLfP) algorithms and iBioFigureSearch allowing biomedical scientists to access figure data effectively, and open-source tools that will enhance biomedical information retrieval, summarization, and question answering. The bNLfP algorithms we will be developing can be applied or integrated into other biomedical text-mining systems. PUBLIC HEALTH RELEVANCE: This project proposes innovative algorithms and models in natural language processing, image processing, machine learning, and user interfacing, to return figures in response to biomedical queries. It is anticipated that the algorithms, models, and tools developed will significantly enhance biomedical scientists' access to figures reported in literature, and thereby expedite biomedical knowledge discovery.