PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Genomics & Informatics10.5808/gi.2019.17.2.e202019172e20Improving methods for normalizing biomedical text entities with concepts from an ontology with (almost) no training data at BLAH5 the CONTESArnaud Ferré, Mouhamadou Ba, Robert Bossyhttp://genominfo.org/upload/pdf/gi-2019-17-2-e20.pdf, http://genominfo.org/journal/view.php?doi=10.5808/GI.2019.17.2.e20, http://genominfo.org/upload/pdf/gi-2019-17-2-e20.pdf
Computational Intelligence10.1111/coin.122142019An innovative hybrid approach for extracting named entities from unstructured text dataAnu Thomas, S. Sangeethahttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fcoin.12214, https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12214, https://onlinelibrary.wiley.com/doi/full-xml/10.1111/coin.12214, https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12214
Journal of Biomedical Informatics: X10.1016/j.yjbinx.2019.10000520191100005Comparing breast cancer treatments using automatically detected surrogate and clinically relevant outcomes entities from textCatherine Blake, Rebecca Kehmhttps://api.elsevier.com/content/article/PII:S2590177X19300046?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S2590177X19300046?httpAccept=text/plain
10.1007/978-0-387-39252-32006Ontology Learning and Population from Texthttp://link.springer.com/content/pdf/10.1007/978-0-387-39252-3.pdf, http://link.springer.com/content/pdf/10.1007/978-0-387-39252-3
Applied Intelligence10.1007/s10489-017-0920-520174881965-1978Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical textAbbas Akkasi, Ekrem Varoğlu, Nazife Dimililerhttp://link.springer.com/article/10.1007/s10489-017-0920-5/fulltext.html, http://link.springer.com/content/pdf/10.1007/s10489-017-0920-5.pdf, http://link.springer.com/content/pdf/10.1007/s10489-017-0920-5.pdf
Microprocessors and Microsystems10.1016/j.micpro.2016.03.003201646202-210Leveraging MapReduce to efficiently extract associations between biomedical concepts from large text dataYanqing Ji, Yun Tian, Fangyang Shen, John Tranhttps://api.elsevier.com/content/article/PII:S0141933116300072?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0141933116300072?httpAccept=text/plain
Data Mining and Knowledge Discovery10.1007/s10618-014-0396-42015294976-998Mining strong relevance between heterogeneous entities from unstructured biomedical dataMing Ji, Qi He, Jiawei Han, Scott Spanglerhttp://link.springer.com/content/pdf/10.1007/s10618-014-0396-4.pdf, http://link.springer.com/article/10.1007/s10618-014-0396-4/fulltext.html, http://link.springer.com/content/pdf/10.1007/s10618-014-0396-4
Journal of Integrative Bioinformatics10.1515/jib-2014-24720141131-16Identifying interactions between chemical entities in biomedical textAndre Lamurias, João D. Ferreira, Francisco M. Coutohttp://www.degruyter.com/view/j/jib.2014.11.issue-3/jib-2014-247/jib-2014-247.xml, http://www.degruyter.com/view/j/jib.2014.11.issue-3/jib-2014-247/jib-2014-247.pdf
Data Mining and Knowledge Discovery10.1007/s10618-014-0363-02014285-61222-1265Ontology of core data mining entitiesPanče Panov, Larisa Soldatova, Sašo Džeroskihttp://link.springer.com/content/pdf/10.1007/s10618-014-0363-0.pdf, http://link.springer.com/article/10.1007/s10618-014-0363-0/fulltext.html, http://link.springer.com/content/pdf/10.1007/s10618-014-0363-0
Data & Knowledge Engineering10.1016/j.datak.2006.06.0072007612228-262Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text miningMuhammad Abulaish, Lipika Deyhttps://api.elsevier.com/content/article/PII:S0169023X06000929?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0169023X06000929?httpAccept=text/plain