A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature

Journal of Cheminformatics

Volumn 7, Issue , 2015, Pages

  Tang, Buzhou ^a,b   Feng, Yudong ^c   Wang, Xiaolong ^a   Wu, Yonghui ^b   Zhang, Yaoyun ^b   Jiang, Min ^b   Wang, Jingqi ^b   Xu, Hua ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF TEXAS   (United States)

^c HARBIN MEDICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84946125762     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-7-S1-S8     Document Type: Article

References (34)

1. Vazquez M, Krallinger M, Leitner F, Valencia A: Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications. Mol Informatics 2011, 30(6-7):506-519.
2. Tjong Kim Sang E, De Meulder F: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. presented at the Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 2003, 4:142-147.
3. Kim J-D, Ohta T, Tsuruoka Y, Tateisi Y, Collier N: Introduction to the bio-entity recognition task at JNLPBA. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications Stroudsburg, PA, USA; 2004, 70-75.
4. Smith L, Tanabe LK, nee Ando RJ, Kuo C-J, Chung I-F, Hsu C-N, Lin Y-S, Klinger R, Friedrich CM, Ganchev K, Torii M, Liu H, Haddow B, Struble CA, Povinelli RJ, Vlachos A, Baumgartner WA Jr, Hunter L, Carpenter B, Tsai RT-H, Dai H-J, Liu F, Chen Y, Sun C, Katrenko S, Adriaans P, Blaschke C, Torres R, Neves M, Nakov P, Divoli A, Maña-López M, Mata J, Wilbur WJ: Overview of BioCreative II gene mention recognition. Genome Biol 2008, 9(Suppl 2):S2.
5. Uzuner Ö, South BR, Shen S, DuVall SL: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Informatics Assoc JAMIA 2011, 18(5):552-556, Oct.
6. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009, , 37 Web Server: W623-633, Jul.
7. Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, Alcántara R, Darsow M, Guedj M, Ashburner M: ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 2008, , 36 Database: D344-350, Jan.
8. Hettne KM, Stierum RH, Schuemie MJ, Hendriksen PJM, Schijvenaars BJA, van Mulligen EM, Kleinjans J, Kors JA: A dictionary to identify small molecules and drugs in free text. Bioinforma Oxf Engl 2009, 25(22):2983-2991, Nov.
9. Pence HE, Williams A: ChemSpider: An Online Chemical Information Resource. J Chem Educ 2010, 87(11):1123-1124.
10. Van Camp AJ: The MeSH Vocabulary File and CHEMLINE. Med Ref Serv Q 1984, 3(1):1-17.
11. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS: DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014, , 42 Database: D1091-1097, Jan.
12. Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A: Text processing through Web services: calling Whatizit. Bioinforma Oxf Engl 2008, 24(2):296-298, Jan.
13. Corbett P, Murray-Rust P: High-Throughput Identification of Chemistry in Life Science Texts. Proceedings of the Second International Conference on Computational Life Sciences Berlin, Heidelberg; 2006, 107-118.
14. Jessop DM, Adams SE, Willighagen EL, Hawizy L, Murray-Rust P: OSCAR4: a flexible architecture for chemical text-mining. J Cheminformatics 2011, 3(1)41.
15. Rocktäschel T, Weidlich M, Leser U: ChemSpot: a hybrid system for chemical named entity recognition. Bioinforma Oxf Engl 2012, 28(12):1633-1640, Jun.
16. McCallum A, Freitag D, Pereira FCN: Maximum Entropy Markov Models for Information Extraction and Segmentation. Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, CA, USA 2000, 591-598.
17. Kolárik C, Klinger R, Friedrich CM, Hofmann-Apitius M, Fluck J: Chemical names: terminological resources and corpora annotation. Workshop on Building and evaluating resources for biomedical text mining (6th edition of the Language Resources and Evaluation Conference) 2008.
18. Klinger R, Kolárik C, Fluck J, Hofmann-Apitius M, Friedrich CM: Detection of IUPAC and IUPAC-like chemical names. Bioinforma Oxf Engl 2008, 24(13): i268-276, Jul.
19. Lafferty J, McCallum A, Pereira FCN: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Dep Pap CIS 2001.
20. Krallinger M, Rabal O, Leitner F, Vazquez M, Oyarzabal J, Valencia A: Overview of the chemical compound and drug name recognition (CHEMDNER) task. Proceddings of the fourth BioCreative challenge evaluation workshop 2013, 2:2-33.
21. Krallinger M, Leitner F, Rabal O, Vazquez M, Oryazabal J, Valencia A: CHEMDNER: The drugs and chemical names extraction challenge. J Cheminformatics 2014.
22. Krallinger M, Rabal O, Leitner F, Vazquez M, Salgado D, Lu Z, Leaman R, Lu Y, Ji D, Lowe DM, Sayle RA, Batista-Navarro RT, Rak R, Huber T, Rocktaschel T, Matos S, Campos D, Tang B, Xu H, Munkhdalai T, Ryu KH, Ramanan SV, Nathan S, Zitnik S, Bajec M, Weber L, Irmer M, Akhondi SA, Kors JA, Xu S, An X, Sikdar UK, Ekbal A, Yoshioka M, Dieb TM, Choi M, Verspoor K, Khabsa M, Giles CL, Liu H, Ravikumar KE, Lamurias A, Couto FM, Dai H, Tsai RT, Ata C, Can T, Usie A, Alves R, Segura-Bedmar I, Martinez P, Oryzabal J, Valencia A: The CHEMDNER corpus of chemicals and drugs and its annotation principles. J Cheminform 2015, 7(Suppl 1):S2.
23. Tsochantaridis I, Joachims T, Hofmann T, Altun Y: Large margin methods for structured and interdependent output variables. J Mach Learn Res 2005, 6:1453-1484.
24. Brown PF, deSouza PV, Mercer RL, Pietra VJD, Lai JC: Class-Based n-gram Models of Natural Language. Comput Linguist 1992, 18467-479.
25. Kanerva P, Kristoferson J, Holst A: Random Indexing of Text Samples for Latent Semantic Analysis. Proceedings of the 22nd Annual Conference of the Cognitive Science Society 2000, 103-6.
26. Mikolov T, Chen K, Corrado G, Dean J: Efficient Estimation of Word Representations in Vector Space. CoRR 2013, abs/1301.3781.
27. Cho H-C, Okazaki N, Miwa M, Tsujii J: Named entity recognition with multiple segment representations. Inf Process Manag 2013, 49(4):954-965.
28. Unified Medical Language System (UMLS) - Home." [Online]. [http://www.nlm.nih.gov/research/umls/], [Accessed: 02-Aug-2013].
29. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Informatics Assoc JAMIA 2010, 17(5)507-513.
30. Aronson AR, Lang F-M: An overview of MetaMap: historical perspective and recent advances. J Am Med Informatics Assoc JAMIA 2010, 17(3)229-236, Jun.
31. Tang B, Cao H, Wang X, Chen Q, Xu H: Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Res Int 2014, 2014, Mar.
32. Tang B, Cao H, Wu Y, Jiang M, Xu H: Clinical entity recognition using structural support vector machines with rich features. Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics, New York, NY, USA 2012, 13-20.
33. Tang B, Cao H, Wu Y, Jiang M, Xu H: Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Med Inform Decis Mak 2013, 13(Suppl 1):S1, Apr.
34. Tang B, Wu Y, Jiang M, Chen Y, Denny JC, Xu H: A hybrid system for temporal information extraction from clinical text. J Am Med Informatics Assoc JAMIA 2013, Apr.