BioRAT: extracting biological information from full-length papers
BioRAT: extracting biological information from full-length papers
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DOI：
10.1093/bioinformatics/bth386
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发表时间：
2004-11-22
期刊：
影响因子：
5.8
通讯作者：
Jones, DT
中科院分区：
文献类型：
--
作者：
Corney, DPA;Buxton, BF;Jones, DT
动机：将期刊中大量的自由格式文本转换为简洁、结构化的格式，使研究人员更容易获得信息。最近，已经开发了几个信息提取系统，试图简化生物和医学数据的检索和分析。由于获取的便利和数据的质量，这项工作的大部分都是单独使用摘要。摘要通常可以通过中央收藏方便地直接获取(例如PubMed)。全文论文包含了更多的信息，但分布在许多地方(如出版商的网站、期刊网站和本地知识库)，使得访问变得更加困难。本文介绍了一种新的信息提取工具Biorat，它专门用于执行生物医学信息抽取，能够定位和分析摘要和全文论文。Biorat是一个用于文本挖掘的生物学研究助理，并将文档搜索功能与特定领域的IE相结合。结果：首先，我们表明，当应用于摘要时，Biorat的性能与现有系统一样好；其次，Biorat通过全文论文获得的信息明显多于仅通过摘要获得的信息。通常，只有不到一半的可用信息是从摘要中提取的，而大部分信息来自每篇论文的正文。总体而言，Biorat从摘要中召回了20.31%的目标事实，准确率为55.07%，对全文论文的召回率为43.6%，准确率为51.25%。
Motivation: Converting the vast quantity of free-format text found in journals into a concise, structured format makes the researcher's quest for information easier. Recently, several information extraction systems have been developed that attempt to simplify the retrieval and analysis of biological and medical data. Most of this work has used the abstract alone, owing to the convenience of access and the quality of data. Abstracts are generally available through central collections with easy direct access (e.g. PubMed). The full-text papers contain more information, but are distributed across many locations (e.g. publishers' web sites, journal web sites and local repositories), making access more difficult.In this paper, we present BioRAT, a new information extraction (IE) tool, specifically designed to perform biomedical IE, and which is able to locate and analyse both abstracts and full-length papers. BioRAT is a Biological Research Assistant for Text mining, and incorporates a document search ability with domain-specific IE.Results: We show first, that BioRAT performs as well as existing systems, when applied to abstracts; and second, that significantly more information is available to BioRAT through the full-length papers than via the abstracts alone. Typically, less than half of the available information is extracted from the abstract, with the majority coming from the body of each paper. Overall, BioRAT recalled 20.31% of the target facts from the abstracts with 55.07% precision, and achieved 43.6% recall with 51.25% precision on full-length papers.