Abstract
The term Text Mining or Text Analytics refers to the process of extracting useful patterns or knowledge from text. The data in textual documents can be of two types, either it can be unstructured or semi-structured. Unstructured data is freely naturally occurring text, whereas web documents data (HTML or XML) is semi structured. Since the natural language text is not organized and does not represent context, it needs to be converted into structured form to perform data analysis and mine useful patterns from it. The field of text mining deals with mining useful patterns or knowledge from unstructured text.
In this paper, we propose a framework for the conversion of the unstructured text documents to a structured form. We present a generalized framework called U – STRUCT which translates unstructured text into structured form. This framework analyses the text documents from different views: lexically, syntactically and semantically and produces a generalized intermediate form of documents. Further, we also discuss the opportunities and challenges in the field of text mining.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kroeze, J.H., Matthee, M.C., Bothma, T.J.D.: Differentiating between data-mining and text-mining terminology. In: SAICSIT 2003, pp. 93–101. ACM Digital Library (2003)
Chen, H.: Knowledge management systems: a text mining perspective. Knowledge Computing Corporation (2001)
Stavrianou, A., Andritsos, P., Nicoloyannis, N.: Overview and Semantic Issues of Text Mining. SIGMOD Record 36, 3 (2007)
Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. Morgan Kaufmann. The University of Illinois at Urbana-Champaign (2006)
Pujari, A.K.: Data Mining Techniques. University Press (2002)
Gupta, V., Lehal, G.S.: A Survey of Text Mining Techniques and Applications. Journal of Emerging Technologies in Web Intelligence 1(1) (2009)
Berry, M.W., Browne, M.: Understanding Search Engines: Mathematical Modeling and Text Retrieval. SIAM, Philadelphia (1999)
Lee, S., Song, J., Kim, Y.: An Empirical Comparison of Four Text Mining Methods. In: 43rd Hawaii International Conference on HICSS, pp. 1–10 (2010)
Chen, M.S., Han, J., Yu, P.: Data Mining: A Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering 8(6) (1996)
Hearst, M.A.: Untangling text data mining. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL 1999, pp. 3–10 (1999) ISBN:1-55860-609-3
Witten, I.H.: Text mining. In: Practical Handbook of Internet Computing, pp. 14-1–14-22. Chapman & Hall/CRC Press, Boca Raton, Florida (2005)
Fan, W., Wallace, L., Rich, S., Zhang: Tapping the power of text mining. Communications of the ACM - Privacy and Security in Highly Dynamic Systems 49(9), 76–82 (2006)
Balakrishnan, K., Sreedhanya, S., Soman, K.P.: Effect Of Pre-Processing On Historical Sanskrit Text Documents. International Journal of Engineering Research and Applications (IJERA) 2(4), 1529–1534 (2012) ISSN: 2248-9622
Torunoglu, D.: Analysis of preprocessing methods on classification of Turkish texts. In: International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 112–117 (2011)
Sagar, S., Imambi, S.T.: Pre processing of Medical Documents and Reducing Dimensionality. Advanced Computing: An International Journal (ACIJ) 2(5) (2011)
Farooq, F., Govindaraju, V., Perrone, M.: Pre-processing methods for handwritten Arabic documents. In: Proceedings of Eighth International Conference on Document Analysis and Recognition, vol. 1, pp. 267–271 (2005)
Suliman, A., Sulaiman, M.N., Othman, M.: Chain Coding and Pre Processing Stages of Handwritten Character Image File. Electronic Journal of Computer Science and Information Technology (eJCSIT) 2(1) (2010)
Feldman, R., Dagan, I.: Knowledge Discovery in Textual Databases (KDT). In: Proceedings of KDD 1995 (1995)
Shatkay, H., Feldman, R.: Mining the Biomedical Literature in the Genomic Era: An Overview. Journal of Computational Biology 10(6), 821–855 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jindal, R., Taneja, S. (2013). U-STRUCT: A Framework for Conversion of Unstructured Text Documents into Structured Form. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication, and Control. ICAC3 2013. Communications in Computer and Information Science, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36321-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-36321-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36320-7
Online ISBN: 978-3-642-36321-4
eBook Packages: Computer ScienceComputer Science (R0)