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Named entity recognition for bacterial Type IV secretion systems.

Ananiadou S, Sullivan D, Black W, Levow GA, Gillespie JJ, Mao C, Pyysalo S, Kolluru B, Tsujii J, Sobral B.

PLoS One. 2011 Mar 29;6(3):e14780. doi: 10.1371/journal.pone.0014780.


Assessment of disease named entity recognition on a corpus of annotated sentences.

Jimeno A, Jimenez-Ruiz E, Lee V, Gaudan S, Berlanga R, Rebholz-Schuhmann D.

BMC Bioinformatics. 2008 Apr 11;9 Suppl 3:S3. doi: 10.1186/1471-2105-9-S3-S3.


Accelerating the annotation of sparse named entities by dynamic sentence selection.

Tsuruoka Y, Tsujii J, Ananiadou S.

BMC Bioinformatics. 2008 Nov 19;9 Suppl 11:S8. doi: 10.1186/1471-2105-9-S11-S8.


POSBIOTM-NER: a trainable biomedical named-entity recognition system.

Song Y, Kim E, Lee GG, Yi BK.

Bioinformatics. 2005 Jun 1;21(11):2794-6.


A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, Xu H.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):601-6. doi: 10.1136/amiajnl-2011-000163.


Automated recognition of malignancy mentions in biomedical literature.

Jin Y, McDonald RT, Lerman K, Mandel MA, Carroll S, Liberman MY, Pereira FC, Winters RS, White PS.

BMC Bioinformatics. 2006 Nov 7;7:492.


Extraction of semantic biomedical relations from text using conditional random fields.

Bundschus M, Dejori M, Stetter M, Tresp V, Kriegel HP.

BMC Bioinformatics. 2008 Apr 23;9:207. doi: 10.1186/1471-2105-9-207.


Rutabaga by any other name: extracting biological names.

Hirschman L, Morgan AA, Yeh AS.

J Biomed Inform. 2002 Aug;35(4):247-59.


Various criteria in the evaluation of biomedical named entity recognition.

Tsai RT, Wu SH, Chou WC, Lin YC, He D, Hsiang J, Sung TY, Hsu WL.

BMC Bioinformatics. 2006 Feb 24;7:92.


Improving named entity recognition accuracy for gene and protein in biomedical text literature.

Tohidi H, Ibrahim H, Murad MA.

Int J Data Min Bioinform. 2014;10(3):239-68.


Anatomical entity recognition with a hierarchical framework augmented by external resources.

Xu Y, Hua J, Ni Z, Chen Q, Fan Y, Ananiadou S, Chang EI, Tsujii J.

PLoS One. 2014 Oct 24;9(10):e108396. doi: 10.1371/journal.pone.0108396.


Distinguishing the species of biomedical named entities for term identification.

Wang X, Matthews M.

BMC Bioinformatics. 2008 Nov 19;9 Suppl 11:S6. doi: 10.1186/1471-2105-9-S11-S6.


Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

Han X, Kim JJ, Kwoh CK.

J Biomed Semantics. 2016 Apr 27;7:22. doi: 10.1186/s13326-016-0059-z.


Formative evaluation of ontology learning methods for entity discovery by using existing ontologies as reference standards.

Liu K, Mitchell KJ, Chapman WW, Savova GK, Sioutos N, Rubin DL, Crowley RS.

Methods Inf Med. 2013;52(4):308-16. doi: 10.3414/ME12-01-0029.


Recognizing names in biomedical texts: a machine learning approach.

Zhou G, Zhang J, Su J, Shen D, Tan C.

Bioinformatics. 2004 May 1;20(7):1178-90.


Biological entity recognition with conditional random fields.

He Y, Kayaalp M.

AMIA Annu Symp Proc. 2008 Nov 6:293-7.


Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features.

Tang B, Cao H, Wu Y, Jiang M, Xu H.

BMC Med Inform Decis Mak. 2013;13 Suppl 1:S1. doi: 10.1186/1472-6947-13-S1-S1.


Evaluating word representation features in biomedical named entity recognition tasks.

Tang B, Cao H, Wang X, Chen Q, Xu H.

Biomed Res Int. 2014;2014:240403. doi: 10.1155/2014/240403.


NCBI disease corpus: a resource for disease name recognition and concept normalization.

Do─čan RI, Leaman R, Lu Z.

J Biomed Inform. 2014 Feb;47:1-10. doi: 10.1016/j.jbi.2013.12.006.


Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.

Zhang S, Elhadad N.

J Biomed Inform. 2013 Dec;46(6):1088-98. doi: 10.1016/j.jbi.2013.08.004.

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