Towards answering biological questions with experimental evidence: automatically identifying text that summarize image content in full-text articles. Yu, H. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2006.
Towards answering biological questions with experimental evidence: automatically identifying text that summarize image content in full-text articles [link]Paper  abstract   bibtex   
Images (i.e., figures) are important experimental evidence that are typically reported in bioscience full-text articles. Biologists need to access images to validate research facts and to formulate or to test novel research hypotheses. We propose to build a biological question answering system that provides experimental evidences as answers in response to biological questions. As a first step, we develop natural language processing techniques to identify sentences that summarize image content.
@article{yu_towards_2006,
	title = {Towards answering biological questions with experimental evidence: automatically identifying text that summarize image content in full-text articles},
	issn = {1942-597X},
	shorttitle = {Towards answering biological questions with experimental evidence},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839512/},
	abstract = {Images (i.e., figures) are important experimental evidence that are typically reported in bioscience full-text articles. Biologists need to access images to validate research facts and to formulate or to test novel research hypotheses. We propose to build a biological question answering system that provides experimental evidences as answers in response to biological questions. As a first step, we develop natural language processing techniques to identify sentences that summarize image content.},
	language = {ENG},
	journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},
	author = {Yu, Hong},
	year = {2006},
	pmid = {17238458},
	pmcid = {PMC1839512},
	keywords = {Algorithms, Biological Science Disciplines, Information Storage and Retrieval, Medical Illustration, natural language processing},
	pages = {834--838},
}
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