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DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures

Identifieur interne : 000049 ( Main/Exploration ); précédent : 000048; suivant : 000050

DeTEXT: 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 :

RBID : PMC:4423993

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 DeTEXT: 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 DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/.


Url:
DOI: 10.1371/journal.pone.0126200
PubMed: 25951377
PubMed Central: 4423993


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<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
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data and make available evaluation protocols for
<bold>D</bold>
e
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e
<bold>TEXT</bold>
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<name sortKey="Yin, X" uniqKey="Yin X">X Yin</name>
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<name sortKey="Huang, K" uniqKey="Huang K">K Huang</name>
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<name sortKey="Hao, Hw" uniqKey="Hao H">HW Hao</name>
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<name sortKey="Weinman, J" uniqKey="Weinman J">J Weinman</name>
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<author>
<name sortKey="Learned Miller, E" uniqKey="Learned Miller E">E Learned-Miller</name>
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<name sortKey="Yin, Xc" uniqKey="Yin X">XC Yin</name>
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<author>
<name sortKey="Yang, C" uniqKey="Yang C">C Yang</name>
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<author>
<name sortKey="Pei, Wy" uniqKey="Pei W">WY Pei</name>
</author>
<author>
<name sortKey="Hao, Hw" uniqKey="Hao H">HW Hao</name>
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<analytic>
<author>
<name sortKey="Wolf, C" uniqKey="Wolf C">C Wolf</name>
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<author>
<name sortKey="Jolion, J" uniqKey="Jolion J">J Jolion</name>
</author>
</analytic>
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<analytic>
<author>
<name sortKey="Kim, D" uniqKey="Kim D">D Kim</name>
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<author>
<name sortKey="Ramesh, Bp" uniqKey="Ramesh B">BP Ramesh</name>
</author>
<author>
<name sortKey="Yu, H" uniqKey="Yu H">H Yu</name>
</author>
</analytic>
</biblStruct>
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<analytic>
<author>
<name sortKey="Bada, M" uniqKey="Bada M">M Bada</name>
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<name sortKey="Eckert, M" uniqKey="Eckert M">M Eckert</name>
</author>
<author>
<name sortKey="Evans, D" uniqKey="Evans D">D Evans</name>
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<author>
<name sortKey="Garcia, K" uniqKey="Garcia K">K Garcia</name>
</author>
<author>
<name sortKey="Shipley, K" uniqKey="Shipley K">K Shipley</name>
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<name sortKey="Sitnikov, D" uniqKey="Sitnikov D">D Sitnikov</name>
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<li>Massachusetts</li>
</region>
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<li>Amherst (Massachusetts)</li>
<li>Pékin</li>
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<li>Université du Massachusetts à Amherst</li>
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<name sortKey="Man, Haixia" sort="Man, Haixia" uniqKey="Man H" first="Haixia" last="Man">Haixia Man</name>
<name sortKey="Pei, Wei Yi" sort="Pei, Wei Yi" uniqKey="Pei W" first="Wei-Yi" last="Pei">Wei-Yi Pei</name>
<name sortKey="Yang, Chun" sort="Yang, Chun" uniqKey="Yang C" first="Chun" last="Yang">Chun Yang</name>
<name sortKey="Zhang, Jun" sort="Zhang, Jun" uniqKey="Zhang J" first="Jun" last="Zhang">Jun Zhang</name>
</country>
<country name="États-Unis">
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<name sortKey="Learned Miller, Erik" sort="Learned Miller, Erik" uniqKey="Learned Miller E" first="Erik" last="Learned-Miller">Erik Learned-Miller</name>
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<name sortKey="Yu, Hong" sort="Yu, Hong" uniqKey="Yu H" first="Hong" last="Yu">Hong Yu</name>
<name sortKey="Yu, Hong" sort="Yu, Hong" uniqKey="Yu H" first="Hong" last="Yu">Hong Yu</name>
</country>
</tree>
</affiliations>
</record>

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