Generation of answer in QA system isone of the most challenging research areas in natural language and performing in the medical domain is more difficult. The main reason is: patient has faith in doctor’s information but may doubt on the machine. Also, accuracy of the system is restricted in natural language. The computer may find the information of a disease but not as efficient as human. In this paper, various well known question answering systems are discussed and analyzed with their benefits and limitations. Finally, in this review, a new approach is visualized to overcome the limitations of present question answering system.
 Libbus, Thomas C. Rindflesch, “NLP-Based Information Extraction for Managing the Molecular Biology Literature”, AMIA 2002 Annual Symposium Proceedings
 Ronen Feldman, Yizhar Regev, Michal Finkelstein Landau, Eyal Hurvitz, Boris Kogan, “Mining Biomedical Literature using Information Extraction”, ClearForest Corp, USA, Israel, October 2002.
 Latha.K, Kalimuthu.S, Dr.Rajaram.R, “Information Extraction from Biomedical Literature using Text Mining Framework”, International Journal of Imaging Science and Engineering, GA, USA, ISSN: 1934-9955, VOL.1, NO.1, January 2007.
 Purabi Kalita, Rashmi Choudhury, “ Information Extraction for Biomedical and Biological Literature”, International Journal of Computer Applications (IJCA). National Conference cum Workshop on Bioinformatics and Computational Biology, NCWBCB- 2014.
 Settles, B. (2005). ABNER: An open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics, 21(14), 3191–3192.
 Song,Y., Kim, E., Lee, G.G., &Yi, B. (2005). POSBIOTM—NER:A trainablebiomedical named-entity recognition system. Bioinformatics, 21(11),2794–2796.
 Tsai, R.T., Sung, C.L., Dai, H.J., Hung, H.C., Sung, T.Y., & Hsu, W.L. (2006). NERBio: Using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition.BMC Bioinformatics, 7(Suppl. 5), S11.
 Team Genia. (2006). GENIA corpus—Genia Project Homepage. Retrieved from http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/home/wi ki.cgi?page= GENIA+corpus
 Krallinger, M. (2006). BioCreAtIvE homepage. Retrieved from http:// biocreative.sourceforge.net/index.html
 Hatzivassiloglou, V., Duboué, P.A., & Rzhetsky, A. (2001). Disambiguating proteins, genes, and RNA in text: A machine learning approach. Bioinformatics, 17(Suppl. 1), S97–S106.
 Borlawsky, T., Friedman, C., & Lussier, Y.A. (2006). Generating executable knowledge for evidence-based medicine using natural language and semantic processing. In Proceedings ofAmerican Medical Informatics Association (pp. 56–60). Bethesda, MD: AMIA.
 Pratt,W.,&Yetisgen-Yildiz, M. (2003).A study of biomedical concept identification:MetaMap vs. people. In Proceedings of the Annual AmericanMedical Informatics Association Symposium (pp. 529–533). Bethesda,MD: AMIA.
 Lertnattee, V., & Theeramunkong, T. (2004). Multidimensional text classification for drug information. IEEE Transactions on Information Technology in Biomedicine, 8(3), 306–312
B.Tech from B.I.T Sindri, Dhanbad and pursuing M.Tech from Shree Institute of science & technology, Bhopal, India
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