DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures
نویسندگان
چکیده
منابع مشابه
DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures
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 De...
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BACKGROUND Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures ef...
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Massive amounts of biomedical literature are readily available online in many forms. Huge amounts of valuable knowledge and relationships are embedded in these resources and need to be properly extracted, discovered, and utilized. Recognizing and classifying biomedical entity names and terms are important steps for developing efficient knowledge/information extraction techniques from these repo...
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Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. It requires deeper analysis than key word searches, but its aims fall short of the very hard and long-term problem of full text understanding. Information extraction represents a midpoint on this spectrum, where the aim is to capture structured ...
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The SLIF project combines text-mining and image processing to extract structured information from biomedical literature. SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph or gel). Fluorescence microscopy imag...
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ژورنال
عنوان ژورنال: PLOS ONE
سال: 2015
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0126200