Glad you enjoyed it! Unfortunately a paper whose takeaway message is "existing simple models work well on this new task" is not as easy to get published (and usually does not make as big a splash) as the "cool complex model obtains 0.5 F1 improvement" paper.
Tim ________________________________ From: buddha <[email protected]> Sent: Wednesday, July 6, 2016 8:00 PM To: [email protected] Subject: Re: update on temporal relations Hi Tim, I live for papers that include a variant of your phrase "First, it is very interesting that the best per- forming systems are the simplest and fastest. Despite the theoretical advantages of the con- ditional random field's global sequence opti- mization, the BIO approaches using local clas- sifiers typically obtain the best performance." I really enjoyed the paper! ~~~~~ May All Your Sequences Converge On Jul 6, 2016, at 6:35 AM, Miller, Timothy <[email protected]<mailto:[email protected]>> wrote: Sean, for temporal expressions specifically, see this paper from BioNLP last year: http://aclweb.org/anthology/W/W15/W15-3809.pdf<https://urldefense.proofpoint.com/v2/url?u=http-3A__aclweb.org_anthology_W_W15_W15-2D3809.pdf&d=CwMFaQ&c=qS4goWBT7poplM69zy_3xhKwEW14JZMSdioCoppxeFU&r=Heup-IbsIg9Q1TPOylpP9FE4GTK-OqdTDRRNQXipowRLRjx0ibQrHEo8uYx6674h&m=ynYoryhR7Jc3mvPFCGBAp1o0JUPGbchTFnPifSLa1kQ&s=Bc2CRWQJd5P4XJZ1j7u9m1_maRY1OdPysu9HFMBHmiw&e=> Timothy Miller; Steven Bethard; Dmitriy Dligach; Chen Lin; Guergana Savova Extracting Time Expressions from Clinical Text in short, yes the SVM-based BIO taggers showed better performance than the CRF or constituency-tree classifiers. Tim On Tue, 2016-07-05 at 21:41 +0000, Mullane, Sean *HS wrote: Thank you for the links. Does that mean that the SVM-based approach from your first link has performed better than the CRF-based approach in CRFTimeAnnotator.java? Thanks, Sean From: Savova, Guergana [mailto:[email protected]] Sent: Friday, June 24, 2016 6:24 PM To: '[email protected]' Subject: RE: update on temporal relations The best performing methods thus far have been released. They are described in http://www.ncbi.nlm.nih.gov/pubmed/26521301 The results are state-of-the-art, see the recent community shared task http://alt.qcri.org/semeval2016/task12/ We are actively working on the topic and will be releasing novel methods as we investigate them. Hope this helps. --Guergana Guergana Savova, PhD, FACMI Associate Professor PI Natural Language Processing Lab Boston Children's Hospital and Harvard Medical School 300 Longwood Avenue Mailstop: BCH3092 Enders 144.1 Boston, MA 02115 Tel: (617) 919-2972 Fax: (617) 730-0817 Harvard Scholar: http://scholar.harvard.edu/guergana_k_savova/biocv From: Mullane, Sean *HS [mailto:[email protected]] Sent: Friday, June 24, 2016 5:04 PM To: '[email protected]' <[email protected]> Subject: update on temporal relations There was some mention previously (June 2015, I think) on the mailing list about the next release of cTAKES including improved temporal relation extraction. Can anyone share information about what improvements or new features will be included and when the release is expected? Thanks, Sean
