A Survey On : Automated Text Mining

Authors

  • Deepti Gupta, Er. Jitendra Dangra Author

Keywords:

Text Mining, Clustering, Information extraction, Information retrieval, Machine learning, Metadata, Natural language processing etc.

Abstract

Text Mining has become an important research area. Text mining is the process of extracting useful information or knowledge or patterns from the unstructured text .Automatic text mining is the discovery by computer of new, previously unknown information, by automatically extracting information from a usually large amount of different unstructured textual resources. Advantages of automatic text mining approach over the manual and semi-automatic approach are increases effectiveness and savings in terms of expert labour power, and straightforward portability to different domains. In this paper, a Survey of Automatic Text Mining techniques and applications and challenges in text mining have been presented.

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References

[1] Vishal Gupta, S Lehal. ‖ A survey of Text Mining Techniques and Applications‖. Journal of EmergingTechnologies in web inteliignce, No.1, August 2009.

[2] Vidya k A,G Aghila, ‖Text Mining Process,Techniques and Tools: an overview‖,International journal of information technology and knowledge management ,july-december 2010,volume 2,no2,pp.613-622.

[3] Ah-hwee Tan, ‖Text Mining:The state of the art and the challenges‖,In proceedings of the PAKDD workshop on Knowledge discovery from advanced databases,pp.65- 70,1999.

[4] Vishwadeepak singh baghela, Dr.s.p.tripathi,‖ International journal of computer science issues‖,vol.9,issue3,pp.545- 552,may2012.

[5] Vallikannu ramanathan, T.Meyyappan,‖International conference on technology and business management‖ pp.508-514,March 2013.

[6] Lokesh kumar,Parul kalra Bhatia,‖Text Mining :concepts,process and applications ― , Journal of global research in computer science ,pp.36-39,march 2013.

[7] Falguni N.patel,Naeha R.soni,‖International journal of Advanced computer research‖, vol.2, pp.243-248, December 2013.

[8] F. Sebastiani, ―Machine learning,‖ ACM Computing Surveys, vol. 1, no. 34, pp. 1–47, 2002.

[9] R. Grishman, ―Information extraction: Techniques and challenges,‖ in Proceedings of the SCIE, 1997, pp. 207– 220.

[10] H. Karanikas, C. Tjortjis, and B. Theodoulidis, ―An approach to text mining using information extraction,‖ in Proceedings of Workshop of Knowledge Management: Theory and Applications in Principles of Data Mining and Knowledge Discovery 4th European Conference, 2000.

[11] H. Liu, S. B. Johnson, and C. Friedman, ―Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS,‖ Journal of the American Medical Informatics Associations (JAMIA), vol. 9, pp. 621–636, 2002.

[12] H. Liu, Z. Hu, M. Torii, C. Wu, and C. Friedman, ―Quantitative assessment of dictionary-based protein named entity tagging,‖ Journal of the American Medical Informatics Associations (JAMIA), vol. 13, pp. 497–507, 2006.

[13] H. M. Alfawareh and S. Jusoh, ―Resolving ambiguous entity through context knowledge and fuzzy approach,‖ International Journal on Computer Science and Engineering (IJCSE), vol. 3, no. 1, pp. 410–422, 2011.

[14] C. Nedellec and A. Nazarenko, ―Ontologies and information extraction: A necessary symbiosis,‖ in Ontology Learning from Text: Methods, Evaluation and Applications, P. Buitelaar, P. Comiano, and B. Magnin, Eds. IOS Press Publication, 2005.

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Published

2015-07-02