Cross media entity extraction and linkage for chemical documents

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2011, Pages 1455-1460

  Yan, Su ^a   Spangler, W Scott ^a   Chen, Ying ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ENTITY; CHEMICAL LITERATURE; CHEMICAL PATENTS; COMBINED INFORMATIONS; CROSS-MEDIA; ENTITY EXTRACTIONS; IMAGE PROCESSING TECHNIQUE; MINING SYSTEMS; SCIENTIFIC LITERATURE; SEMANTIC UNDERSTANDING; TEXT ANALYSIS; TEXTUAL CONTENT;

ARTIFICIAL INTELLIGENCE; DATA MINING; PATENTS AND INVENTIONS; SEMANTICS;

IMAGE PROCESSING;

EID: 80055056068     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Banville, D. 2006. Mining chemical structural information from the drug literature. Drug Discovery Today 11(1-2):35-42. (Pubitemid 43227850)
2. Brecher, J. 1999. Name=struct: A practical approach to the sorry state of real-life chemical nomenclature. Journal of Chemical Information and Computer Science 39(6):943-950. (Pubitemid 129579827)
3. Chen, Y.; Spangler, S.; Kreulen, J.; and etc. 2009. Simple: A strategic information mining playform for ip excellence. IBM Research Report.
4. Cohen, W. W.; Wang, R.; and Murphy, R. F. 2003. Understanding captions in biomedical publications. In ACM SIGKDD international conference on Knowledge discovery and data mining, 499-504.
5. Deserno, T. M.; Antani, S.; and Long, L. R. 2009. Content-based image retrieval for scientific literature access. Methods of Information in Medicine 48(4):371-80.
6. Filippov, I. V., and Nicklaus, M. C. 2009. Optical structure recognition software to recover chemical information: Osra, an open source solution. Journal of Chemical Information and Modeling 49(3):740-743.
7. Futrelle, R. P. 2004. Handling figures in document summarization. In Text Summarization Branches Out Workshop, 42nd Annual Meeting of the Association for Computational Linguistics, 61-65.
8. Hamon, T., and Grabar, N. 2010. Linguistic approach for identification of medication names and related information in clinical narratives. Journal of the American Medical Informatics Association 17(5):549-554.
9. Hua, G., and Tian, Q. 2009. What can visual content analysis do for text based image search? In IEEE international conference on Multimedia and Expo, 1480-1483.
10. Klinger, R.; Kolářik, C.; Fluck, J.; Hofmann-Apitius, M.; and Friedrich, C. M. 2008. Detection of iupac and iupac-like chemical names. Bioinformatics 24:i268-i276. (Pubitemid 351911682)
11. Krauthammer, M.; Rzhetsky, A.; Morozov, P.; and Friedman, C. 2000. Using BLAST for identifying gene and protein names in journal articles. Gene 259(1-2):245-252. (Pubitemid 32119256)
12. Lafferty, J. D.; McCallum, A.; and Pereira, F. C. N. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning, 282-289.
13. Lu, X.; Wang, J. Z.; Mitra, P.; and Giles, C. L. 2007. Deriving knowledge from figures for digital libraries. In International conference on World Wide Web, 1229-1230.
14. Park, J.; Rosania, G. R.; Shedden, K. A.; Nguyen, M.; Lyu, N.; and Saitou, K. 2009. Automated extraction of chemical structure information from digital raster images. Chemistry Central journal 3(1).
15. Sun, B.; Tan, Q.; Mitra, P.; and Giles, C. L. 2007. Extraction and search of chemical formulae in text documents on the web. In International conference on World Wide Web, 251-260.
16. Wagner, R. A., and Fischer, M. J. 1974. The String-to-String Correction Problem. Journal of the ACM 21(1):168-173.
17. Yan, R., and Naphade, M. 2005. Multi-modal video concept extraction using co-training. Multimedia and Expo, IEEE International Conference on.
18. Zimmermann, M., and Hofmann-Apitius, M. 2007. Automated extraction of chemical information from chemical structure depictions. Drug Discovery 12-15.