Text Mining
Across all realms of the sciences, the rapid growth in the number of works published digitally presents new challenges and opportunities for making sense of this wealth of textual information. The maturing field of Text Mining aims to solve problems concerning the retrieval, extraction and analysis of unstructured information in digital text, and revolutionize how scientists access and interpret data that might otherwise remain buried in the literature. PLOS acknowledges the growing body of work in the area of Text Mining by bringing together reviews and research studies to create the PLOS Text Mining Collection. It is no coincidence that research in Text Mining is burgeoning: the widespread uptake of the Open Access publishing model developed by PLOS and other publishers now makes it easier than ever to obtain, mine and redistribute data from published texts.
Image Credit: PLOS
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PLOS Computational Biology Getting Started in Text Mining
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PLOS Computational Biology Getting Started in Text Mining: Part Two
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PLOS Computational Biology Open Access: Taking Full Advantage of the Content
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PLOS Computational Biology Biomedical Text Mining and Its Applications
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PLOS Biology Tough Mining
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PLOS Biology Facts from Text—Is Text Mining Ready to Deliver?
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PLOS ONE Text Mining the History of Medicine
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PLOS Computational Biology ‘HypothesisFinder:’ A Strategy for the Detection of Speculative Statements in Scientific Text
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PLOS Computational Biology Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts