Home   >   CSC-OpenAccess Library   >    Manuscript Information
The Process of Information extraction through Natural Language Processing
Sandigdha Acharya , Smita Rani Parija
Pages - 40 - 51     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 1   Issue - 1    |    Publication Date - December 2010  Table of Contents
Natural language Processing(NLP), Information retrieval, Text Zoning
Information Retrieval (IR) is the discipline that deals with retrieval of unstructured data, especially textual documents, in response to a query or topic statement, which may itself be unstructured, e.g., a sentence or even another document, or which may be structured, e.g., a boolean expression. The need for effective methods of automated IR has grown in importance because of the tremendous explosion in the amount of unstructured data, both internal, corporate document collections, and the immense and growing number of document sources on the Internet.. The topics covered include: formulation of structured and unstructured queries and topic statements, indexing (including term weighting) of document collections, methods for computing the similarity of queries and documents, classification and routing of documents in an incoming stream to users on the basis of topic or need statements, clustering of document collections on the basis of language or topic, and statistical, probabilistic, and semantic methods of analyzing and retrieving documents. Information extraction from text has therefore been pursued actively as an attempt to present knowledge from published material in a computer readable format. An automated extraction tool would not only save time and efforts, but also pave way to discover hitherto unknown information implicitly conveyed in this paper. Work in this area has focused on extracting a wide range of information such as chromosomal location of genes, protein functional information, associating genes by functional relevance and relationships between entities of interest. While clinical records provide a semi-structured, technically rich data source for mining information, the publications, in their unstructured format pose a greater challenge, addressed by many approaches.
CITED BY (4)  
1 Antunes, F., Freire, M., & Costa, J. P. (2015). Semantic web and decision support systems. Journal of Decision Systems, 1-15.
2 Jali, N., Greer, D., & Hanna, P. (2014, September). Class Responsibility Assignment (CRA) for Use Case Specification to Sequence Diagrams (UC2SD). In Software Engineering Conference (MySEC), 2014 8th Malaysian (pp. 13-18). IEEE.
3 Antunes, F., Freire, M., & Costa, J. P. (2014). Semantic Web Tools and Decision-Making. In Group Decision and Negotiation. A Process-Oriented View (pp. 270-277). Springer International Publishing.
4 Abebe, A. (2013). Concept-based Amharic Documents Similarity (CADS) (Doctoral dissertation, Addis Ababa University).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
7 PdfSR 
Lum et.al.: An architecture for a multimedia DBMS supporting content search. In the Proceedings of International Conference on Computing and Information (ICCI'90), LNCS Vol.468, Springer-Verlag, 1990.
M. Iwayama and T. Tokunaga. Cluster-based text categorization: A comparison of category search strategies. In ACM SIGIR '95, pages 273-280,1995.
A. Ralescu and A. Fadlalla.: The issue of semantic distance in knowledge representation with conceptual graphs. In Proceedings of Fifth Annual Workshop on Conceptual Structures, pages 141--142, 1990.
Cohen K. Bretonel and Lawrence Hunter. Natural language processing and system biology, 2004.
D. A. Hull, J. O. Pedersen, and H. Shutze. Method combination for document filtering. In Proceedings of SIGIR, pages 279-298, 1996.
D. Dubin. Document analysis for visualization. In ACM SIGIR '95, pages 199-204, 1995
D. R. Cutting, D. R. Karger, J. O. Pedersen, and J. W. Tukey. Scatter/gather: A cluster-based approach to browsing large document collections. In ACM SIGIR '92, pages 318-329, 1992. [8] J. J. Daniels and E. L. Rissland. A case-based approach to intelligent information retrieval. In ACM SIGIR '95, pages 238-245, 1995.
George A.Miller.: WordNet: An On-line Lexical Database. In the International Journal of Lexicography, Vol.3, No.4, 1990.
I.H. Witten, A. Moffat, and T.C. Bell. Managing Gigabytes: Compressing and IndexingDocuments and Images. Van Nostrand Reinhold, New York, 1999.
In Proceedings of the Conference on Human Factors in Computing Systems
J. F. Sowa.: Conceptual Structures: Information Processing in Mind and Machine, AddisonWesley. 1984.
J. Hammer, H. Garcia-Molina, K. Ireland, Y. Papakonstantinou, J. Ullman, and J. Widom. Information translation, mediation, and mosaic-based browsing in the tsimmis system. In Exhibits Program of the Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 483^87, 1995.
Jianming Li, Lei Zhang and Yong Yu.: Learning to Generate Semantic Annotation for Domain Specific Sentences. In the Workshop on Knowledge Markup and semantic Annotation, the First International Conference on Knowledge Capture (K-CAP2001),Victoria B.C., Canada, Oct.2001.
John F. Sowa.: Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole Publishing Co., Pacific Grove, CA, 1999.
Jonathan Poole and J. A. Campbell.: A Novel Algorithm for Matching Conceptual and Related Graphs. In G. Ellisetaleds, Conceptua lStructures: Applications, Implementation and Theory, pp. 293- —307, Santa Cruz, CA, USA. Springer-Verlag, LNAI 954, 1995.
Lei Zhang and Yong Yu.: Learning to Generate CGs from Domain Specific Sentences. In proceeding of the 9th International Conference on Conceptual Structures, (ICCS2001), LNAI Vol.2120, Springer-Verlag, 2001
M. Hemmje, C. Kunkel, and A. Willett. Lyberworld - a visualization user interface supporting fulltext retrieval. In ACM SIGIR '94, pages 249-259, 1994.
Motwani, and T.Winograd.: The Page Rank citation ranking: Bringing order to the web. Technical report, Stanford University, 1998. Available at http://www-db.stanford.edu/~backrub/pageranksub.ps.
N. Guarino, C. Masolo, and G. Vetere.: OntoSeek: ”Content-Based Access to the Web. IEEE Intelligent Systems” 14(3), pp.70--80.
Norman Foo, B. Garner, E. Tsui and A. Rao.: Semantic Distance in Conceptual Graphs. In J. Nagle and T. Nagle, editors, Fourth Annual Workshop on Conceptual Structures, 1989.
R. Richardson, A. F. Smeaton and J. Murphy.: Using WordNet as a Knowledge Basefor Measuring Semantic Similarity between Words. In the Proceedings of AICS Conference, Trinity College, Dublin, Ireland, September 1994.
R. S. Flournoy, R. Ginstrom, K. Imai, S. Kaufmann, G. Kikui, S. Peters, H. Schiitze, and Y. Takayama. Personalization and users' semantic expectations. In Query Input and User Expectations, Proceedings of SIGIR Workshop, pages 31-35, 1998.
Richard Harshman. Using latent semantic analysis to improve access to textual information.
S. Dao and B. Perry. Applying a data miner to heterogeneous schema integration. In Proceedings of First International Conference on Knowledge Discovery and Data Mining, pages 63-68, 1995.
S. Deerwester, S. T. Adumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. JASIS, 41(6):391^07, 1990.
Survey Paper 2, Boolean Retrival, Christopher D. Manning.
Survey Paper1, A Conceptual Graph Matching For Semantic SearchJiwei Zhong, Haiping Zhu, Jianming Li andYong Yu.
Susan T. Dumais, George W. Furnas, Thomas K. Landauer, Scott Deerwester, and
T.H.Cormen, C.E.Leiserson and R.L.Rivest.: Introduction to Algorithms. The MIT Press, 1994
U. Fayyad and R. Uthurusamy. Data mining and knowledge discovery in databases. Communications of the ACM, 39(11), 1996.
W. Daelemans, S. Buchholz, and J. Veenstra.: Memory-Based Shallow Parsing.In Proceedings of EMNLP/VLC-99, pages 239-246, University of Maryland, USA,June1999.
Y. A. Aslandogan, C. Thier, C. T. Yu, C. Liu, and K. R. Nair.: Design, implementation and evaluation of SCORE(A System for COntent based REtrieval of pictures). In Eleventh International Conference on Data Engineering, pages 280—-287, Taipei, Taiwan, March 199
Z. Hasan, A. O. Mendelzon, and D. Vista. Applying database visualization to the world wide web. SIGMOD Record, 25(4):45-49, 1996.
] N. Kushmerick, Daniel S. Weld and Robert B. Doorenbos.: Wrapper Induction for Information Extraction. Intl. Joint Conference on Artificial Intelligence pp.729—-737.
Mr. Sandigdha Acharya
- India
Mr. Smita Rani Parija
- India

View all special issues >>