DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures
Identifieur interne : 000047 ( Main/Merge ); précédent : 000046; suivant : 000048DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures
Auteurs : Xu-Cheng Yin [République populaire de Chine] ; Chun Yang [République populaire de Chine] ; Wei-Yi Pei [République populaire de Chine] ; Haixia Man [République populaire de Chine] ; Jun Zhang [République populaire de Chine] ; Erik Learned-Miller [États-Unis] ; Hong Yu [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes
Url:
DOI: 10.1371/journal.pone.0126200
PubMed: 25951377
PubMed Central: 4423993
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PMC:4423993Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes <bold>D</bold>
e<bold>TEXT</bold>
: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the <bold>D</bold>
e<bold>TEXT</bold>
data and make available evaluation protocols for <bold>D</bold>
e<bold>TEXT</bold>
. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. <bold>D</bold>
e<bold>TEXT</bold>
is publicly available for downloading at <ext-link ext-link-type="uri" xlink:href="http://prir.ustb.edu.cn/DeTEXT/">http://prir.ustb.edu.cn/DeTEXT/</ext-link>
.</p>
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</author>
<author><name sortKey="Shipley, K" uniqKey="Shipley K">K Shipley</name>
</author>
<author><name sortKey="Sitnikov, D" uniqKey="Sitnikov D">D Sitnikov</name>
</author>
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