The Fudan-UIUC participation in the BioASQ Challenge Task 2a: The antinomyra system

CEUR Workshop Proceedings

Volumn 1180, Issue , 2014, Pages 1311-1318

  Liu, Ke ^a   Wu, Junqiu ^b   Peng, Shengwen ^a   Zhai, Chengxiang ^c   Zhu, Shanfeng ^a  

^a FUDAN UNIVERSITY   (China)

^b CENTRAL SOUTH UNIVERSITY   (China)

^c University of Illinois at Urbana Champaign   (United States)

Author keywords

Learning to rank; Logistic regression; MeSH indexing; Multi label classification

Indexed keywords

ABSTRACTING; CLASSIFICATION (OF INFORMATION); SEMANTICS;

F MEASURE; LEARNING TO RANK; LOGISTIC REGRESSIONS; MEDLINE; MESH TERMS; MULTI LABEL CLASSIFICATION; SEMANTIC INDEXING; TEST SETS;

INDEXING (OF INFORMATION);

EID: 84961303581     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Nelson, S. J., Schopen, M., Savage, A. G., Schulman, J. L., & Arluk, N.: The MeSH translation maintenance system: structure, interface design, and implementation. Medinfo, 11(Pt 1), 67-69. (2004)
2. Gu, J., Feng, W., Zeng, J., Mamitsuka, H., Zhu, S.: Efficient Semi-supervised MEDLINE document clustering with MeSH semantic and global content constraints. IEEE Transactions on Cybernetics, 43(4), 1265-1276, (2013)
3. Zhu, S., Zeng, J., Mamitsuka, H.: Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. Bioinformatics 25(15):1944-1951 (2009)
4. Zhu, S., Takigawa, I., Zeng, J., Mamitsuka, H.: Field independent probabilistic model for clustering multi-field documents. Information Processing & Management. 45(5):555-570 (2009)
5. Huang, X., Zheng, X., Yuan, W., Wang, F., Zhu, S.: Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization. Information Science 181(11):2293-2302 (2011)
6. Rajpal, D. K., Qu, X. A., Freudenberg, J. M., Kumar, V. D.: Mining emerging biomedical literature for understanding disease associations in drug discovery. Methods Mol Biol. 1159:171-206 (2014)
7. Theodosiou, T., Vizirianakis, I. S., Angelis, L., Tsaftaris, A., Darzentas, N.: MeSHy: Mining unanticipated PubMed information using frequencies of occurrences and concurrences of MeSH terms. J Biomed Inform. 44(6):919-26. (2011)
8. Partalas, I., Gaussier, E., & Ngomo, A. C. N.: Results of the First BioASQ Workshop. In: BioASQ@ CLEF (pp. 1-8). (2013)
9. Tsoumakas, G., Laliotis, M., Markantonatos, N., & Vlahavas, I. P.: Large-Scale Semantic Indexing of Biomedical Publications. In BioASQ@ CLEF. (2013)
10. Huang, M., Neveol, A., Lu, Z.: Recommending MeSH terms for annotating biomedical articles. Journal of the American Medical Informatics Association, 18(5):660-667. (2011)
11. Mao, Y., Lu, Z.: NCBI at the 2013 BioASQ challenge task: Learning to rank for automatic MeSH indexing[R]. Technical report. (2013)
12. Mork, J. G., Jimeno-Yepes, A., & Aronson, A. R.: The NLM Medical Text Indexer System for Indexing Biomedical Literature. In BioASQ@ CLEF. (2013)
13. Tang, L., Rajan, S., Narayanan, V. K.: Large scale multi-label classification via metalabeler. Proceedings of the 18th international conference on World Wide Web, 211-220 ACM (2009)
14. Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., Li, H.: Learning to rank: from pairwise approach to listwise approach. Proceedings of the 24th international conference on Machine learning. 129-136. ACM. (2007)
15. Jiang, J., Zhai, C.: An empirical study of tokenization strategies for biomedical information retrieval. Information Retrieval, 10(4-5):341-363. (2007)
16. Burges, C. J.: From RankNet to LambdaRank to LambdaMART: An overview. Technical Report MSR-TR-2010-82, Microsoft Research. (2010)
17. Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., Lin, C. J.: LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9:1871-1874. (2008)
18. Yepes, A. J., Mork, J. G., Demner-Fushman, D., Aronson, A. R.: Comparison and combination of several MeSH indexing approaches. AMIA Annual Symposium Proceedings. 2013:709-718. American Medical Informatics Association. (2013)