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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 8,August 2012
Issues and challenges of Research: Originality Based Document Classification and Information Extraction
Full Text(PDF, )  PP.204-206  
Author(s)
Hussam Eddin Alfitouri Elgatait& Wan Mohd Fauzy
KEYWORDS
INFORMATION EXTRACTION; SENTENCE CLASSIFICATION; COMPUTATIONAL LINGUISTIC; NATURAL LANGUAGE PROCESSING (NLP); KNOWLEDGE DOMAIN.
ABSTRACT
As The Number Of Publications In The Knowledge Domain Is Still Increasing, Natural Language Processing (Nlp) And Techniques For Information Extraction From Texts Body Have Been Applied Widely In Different Fields. In Addition, The Scientific Writing Requires Ar-guments And Evidence, The Researchers Must Be Certain That The Source Of Information Is Reliable, Genuine And Valid In Order To Con-vince The Reader And Enrich The Knowledge. Therefore, This Paper Highlights The Current Issues And Challenges Of Using Different Information Extraction Techniques.
References
[1] J. Turmo, et al., "Adaptive information extraction," ACM Comput. Surv., vol. 38, p. 4, 2006.

[2] Z. Ni and H. Xu, "Automatic Citation Metadata Extraction Using Hidden Markov Models," presented at the Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering, 2009.

[3] R. Williams and J. Nash, "Computer-Based Assessment: From Objective Tests to Automated Essay Grading. Now for Automated Essay Writing?," in Information Systems: Modeling, Development, and Integration. vol. 20, J. Yang, et al., Eds., ed: Springer Berlin Heidelberg, 2009, pp. 214-221.

[4] R. Mihalcea, et al., "Corpus-based and knowledge-based measures of text semantic similarity," presented at the Proceedings of the 21st national conference on Artificial intelligence -Volume 1, Boston, Massachusetts, 2006.

[5] N. K. Nikitas, "Computer Assisted Assessment (CAA) of Free-Text: Literature Review and the Specification of an Alternative CAA System," 2010, pp. 116-118.

[6] P. Dessus, "AnOverview of LSA-Based Systems for Supporting Learning and Teaching," presented at the Proceeding of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, 2009.

[7] A. A. Bahrami, "A Blocking Scheme for Identification of Components and Sub-Components of Semi-Structured E-Documents," in Fifth International Conference on Information Technology: New Generations, NW-US, 2008, pp. 943-948.

[8] F. Mahdavi, et al., "Semi-Automatic Trend Detection in Scholarly Repository Using Semantic Approach," World Academy of Science, Engineering and Technology, vol. 5, pp. 224-226, 2009.

[9] B. Shaparenko and T. Joachims, "Identifying the original contribution of a document via language modeling," Machine Learning and Knowledge Discovery in Databases, pp. 350-365, 2009.

[10] J. Van, et al., "Document signature using intrinsic features for counterfeit detection," Computational Forensics, pp. 47-57, 2008.

[11] J. Jaya, "Plagiarism Detection Techniques," Cochin University Of Science And Technology, 2007.

[12] B. Aleman-Meza, et al., "Template based semantic similarity for security applications," Intelligence and Security Informatics, pp. 621-622, 2005.

[13] K. Verma, et al., "Meteor-s wsdi: A scalable p2p infrastructure of registries for semantic publication and discovery of web services," Information Technology and Management, vol. 6, pp. 17-39, 2005.

[14] M. A. Ismail, et al., "Semantic support environment for research activity," Journal of US-CHINA Education Review, vol. 5, pp. 36-51, 2008.

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