Automatic figure ranking and user interfacing for intelligent figure search.
Automatic figure ranking and user interfacing for intelligent figure search.
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DOI：
10.1371/journal.pone.0012983
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发表时间：
2010-10-07
期刊：
影响因子：
3.7
通讯作者：
Ramesh BP
中科院分区：
文献类型：
--
作者：
Yu H;Liu F;Ramesh BP
数字是重要的实验结果，通常在全文生物科学文章中报告。生物科学研究人员需要访问数据来验证研究事实，并制定或测试新的研究假设。另一方面，生物科学文献的庞大数量使得获取数据变得困难。因此，正在开发智能图形搜索引擎（http://figuresearch.askhermes.org）。现有的图检索研究对每个图都是平等对待的，但我们引入了一个新的“图排序”概念：在生物医学全文文章中出现的图可以根据其对知识发现的贡献进行排序。通过对100多名生物科学研究人员的实证验证，提出了基于无监督自然语言处理（NLP）的图像自动排序方法。对202篇全文文章的集合进行评估，其中作者根据重要性对数字进行排名，我们最好的系统实现了0.2的加权错误率，这比我们探索的其他几个基线系统要好得多。我们进一步探索了一个用户界面应用程序，在该应用程序中，我们构建了包含图形排名的新颖用户界面（ui），允许生物科学研究人员有效地访问重要的图形。我们的评价结果显示，92%的生物科学研究人员更喜欢放大最重要数字的用户界面作为前两种选择。使用我们的自动数字排序NLP系统，生物科学研究人员更喜欢我们的NLP系统预测最重要数字的ui，而不是随机分配最重要数字的ui。此外，我们的研究结果表明，生物科学研究人员对自动图排序生成的ui和人工排序标注生成的ui的偏好没有统计学差异。评估结果表明，本研究报告的自动排名和用户界面可以在在线出版中完全实现。结合图形自动排序系统的新型用户界面为生物医学领域的科学信息提供了一种更高效、更强大的访问方式，这将进一步增强我们现有的图形搜索引擎，以更好地方便生物科学家访问感兴趣的图形。
Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of “figure ranking”: figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery. We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation. The evaluation results conclude that automatic figure ranking and user interfacing as we reported in this study can be fully implemented in online publishing. The novel user interface integrated with the automatic figure ranking system provides a more efficient and robust way to access scientific information in the biomedical domain, which will further enhance our existing figure search engine to better facilitate accessing figures of interest for bioscientists.
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影响因子：
5.8
作者：
Hearst, Marti A.;Divoli, Anna;Ye, Jerry
通讯作者：
Ye, Jerry
DOI：
10.1073/pnas.0901533106
发表时间：
2009-05-12
影响因子：
11.1
作者：
Bengoechea, Tasha G.;Chen, Zhijiang;Lee, Kuo-Fen
通讯作者：
Lee, Kuo-Fen
影响因子：
6
作者：
Collobert, R;Bengio, S
通讯作者：
Bengio, S
影响因子：
5
作者：
Kahn, Charles E., Jr.;Thao, Cheng
通讯作者：
Thao, Cheng
DOI：
10.1023/b:inrt.0000009438.69013.fa
发表时间：
2004-01-01
期刊：
INFORMATION RETRIEVAL
影响因子：
--
作者：
Braschler, M;Peters, C
通讯作者：
Peters, C