Beyond captions: linking figures with abstract sentences in biomedical articles.
Beyond captions: linking figures with abstract sentences in biomedical articles.
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DOI：
10.1371/journal.pone.0039618
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Yu H
中科院分区：
文献类型：
--
作者：
Bockhorst JP;Conroy JM;Agarwal S;O'Leary DP;Yu H
虽然科技文章中的图形信息含量高，能够简洁地传达许多重要的研究成果，但目前文献检索系统对图形的利用不足。许多系统忽略数字，而那些不这样做的系统通常只考虑标题文本。本研究描述并评估了一种全自动方法，用于将生物医学文章正文中的数字与摘要中的句子相关联。我们使用监督方法来学习概率语言模型、隐马尔可夫模型和条件随机场，以预测抽象句子和图形之间的关联。本文使用了三种证据：抽象句子和图形中的文本，句子和图形的相对位置，以及整篇文章中句子/图形的关联模式。每个信息源都具有预测价值，使用各种证据的模型比不使用的模型更准确。我们最准确的方法在交叉验证实验中有69%的得分，与人类专家的准确性相竞争，比最先进的方法具有更好的预测准确性，并且使用户能够访问与抽象句子相关的数字，平均减少1.82次鼠标点击。用户评价表明，人类用户发现我们的系统是有益的。该系统可在http://FigureItOut.askHERMES.org上获得。
Although figures in scientific articles have high information content and concisely communicate many key research findings, they are currently under utilized by literature search and retrieval systems. Many systems ignore figures, and those that do not typically only consider caption text. This study describes and evaluates a fully automated approach for associating figures in the body of a biomedical article with sentences in its abstract. We use supervised methods to learn probabilistic language models, hidden Markov models, and conditional random fields for predicting associations between abstract sentences and figures. Three kinds of evidence are used: text in abstract sentences and figures, relative positions of sentences and figures, and the patterns of sentence/figure associations across an article. Each information source is shown to have predictive value, and models that use all kinds of evidence are more accurate than models that do not. Our most accurate method has an -score of 69% on a cross-validation experiment, is competitive with the accuracy of human experts, has significantly better predictive accuracy than state-of-the-art methods and enables users to access figures associated with an abstract sentence with an average of 1.82 fewer mouse clicks. A user evaluation shows that human users find our system beneficial. The system is available at http://FigureItOut.askHERMES.org.
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影响因子：
14.9
作者：
Harris, MA;Clark, J;White, R
通讯作者：
White, R
DOI：
10.1196/annals.1310.013
发表时间：
2004-01-01
期刊：
APPLICATIONS OF BIOINFORMATICS IN CANCER DETECTION
影响因子：
--
作者：
Murphy, RF
通讯作者：
Murphy, RF
影响因子：
14.9
作者：
Blake, JA;Richardson, JE;Eppig, JT
通讯作者：
Eppig, JT
影响因子：
5.8
作者：
Hearst, Marti A.;Divoli, Anna;Ye, Jerry
通讯作者：
Ye, Jerry
影响因子：
14.9
作者：
Alfarano C;Andrade CE;Anthony K;Bahroos N;Bajec M;Bantoft K;Betel D;Bobechko B;Boutilier K;Burgess E;Buzadzija K;Cavero R;D'Abreo C;Donaldson I;Dorairajoo D;Dumontier MJ;Dumontier MR;Earles V;Farrall R;Feldman H;Garderman E;Gong Y;Gonzaga R;Grytsan V;Gryz E;Gu V;Haldorsen E;Halupa A;Haw R;Hrvojic A;Hurrell L;Isserlin R;Jack F;Juma F;Khan A;Kon T;Konopinsky S;Le V;Lee E;Ling S;Magidin M;Moniakis J;Montojo J;Moore S;Muskat B;Ng I;Paraiso JP;Parker B;Pintilie G;Pirone R;Salama JJ;Sgro S;Shan T;Shu Y;Siew J;Skinner D;Snyder K;Stasiuk R;Strumpf D;Tuekam B;Tao S;Wang Z;White M;Willis R;Wolting C;Wong S;Wrong A;Xin C;Yao R;Yates B;Zhang S;Zheng K;Pawson T;Ouellette BF;Hogue CW
通讯作者：
Hogue CW