Biomedical language processing: what's beyond PubMed?

By L. Hunter, K. Cohen. Molecules and Cells, 2006

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0c50b4d7b21faf8d2769f74ec5933b98745a2277

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Citations in the corpus

Is searching full text more effective than searching abstracts? (2009, background): “The exponential growth of peer-reviewed literature and the breakdown of disciplinary boundaries heralded by genome-scale instruments have made it harder than ever for scientists to find and assimilate all the publications important to their research [1].”
Semi-automated screening of biomedical citations for systematic reviews (2010, background): “Due to the exponential growth of available biomedical literature [6], much work has been done in automatically mining and learning from published manuscripts.”
Frontiers of biomedical text mining: current progress (2007, methodology): “We refer the interested reader to previous surveys [2, 5–15] (marked S in the bibliography) and to the abovementioned journals and conferences.”
Understanding PubMed® user search behavior through log analysis (2009, background): “The challenge is ever increasing, both in the scope of topical coverage and in the fast-growing volume of biomedical literature (1).”
Data-driven Summarization of Scientific Articles (2018, background): “In the domain of scientific literature, the rate of publications grows exponentially (Hunter and Cohen, 2006), which calls for efficient automatic summarization tools.”
Extracting interactions between proteins from the literature (2008, neutral): “…so far rely on the techniques from one or more areas [29] [30] [31] [32] including Information Retrieval (IR) [27] [33], Machine Learning (ML) [34] [35], Natural Language Processing (NLP) [36] [37] [38], Information Extraction (IE) [39] [40] [41] [42] and Text Mining [43] [44] [45] [46] [47] [48].”
Concept annotation in the CRAFT corpus (2012, background): “Substantial progress in biomedical natural-language processing (NLP), particularly in the tasks of information retrieval, concept recognition, and information extraction [1-5] raises the possibility of creating formal representations for the entire biomedical literature.”