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 Bossy,,
Computational Intelligence10.1111/coin.122142019An innovative hybrid approach for extracting named entities from unstructured text dataAnu Thomas, S. Sangeetha,,,
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 Kehm,
10.1007/978-0-387-39252-32006Ontology Learning and Population from Text,
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 Dimililer,,
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 Tran,
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 Spangler,,
Journal of Integrative Bioinformatics10.1515/jib-2014-24720141131-16Identifying interactions between chemical entities in biomedical textAndre Lamurias, João D. Ferreira, Francisco M. Couto,
Data Mining and Knowledge Discovery10.1007/s10618-014-0363-02014285-61222-1265Ontology of core data mining entitiesPanče Panov, Larisa Soldatova, Sašo Džeroski,,
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 Dey,