The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
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DOI：
10.1371/journal.pone.0065390
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Jensen LJ
中科院分区：
文献类型：
--
作者：
Pafilis E;Frankild SP;Fanini L;Faulwetter S;Pavloudi C;Vasileiadou A;Arvanitidis C;Jensen LJ
生物医学文献的指数级增长使得对高效、准确的文本挖掘工具的需求越来越明显。在文本中命名生物实体的识别是一个中心和困难的任务。我们已经开发出一种有效的算法和实现的基于字典的方法命名实体识别，我们在这里使用的文本中的物种和其他类群的名称。该工具SPECIES比现有工具快一个数量级以上，并且与现有工具一样准确。在现有的黄金标准语料库和800个摘要的新语料库上评估了精确度和召回率，这些摘要在工具开发后进行了手动注释。该语料库包括来自期刊的摘要，这些期刊被选择来代表许多分类学组，这使得人们能够深入了解哪些类型的生物体名称难以检测，哪些类型的生物体名称容易检测。最后，我们在整个Medline数据库中标记了生物体名称，并开发了一个网络资源ORGANISMS，使生物学家的广泛社区可以访问结果。SPECIES软件是开放源代码的，可以从http://species.jensenlab.org沿着字典文件和手动注释的黄金标准语料库下载。ORGANISMS网络资源可在http://organisms.jensenlab.org上找到。
The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.
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