A Frame Work for Text Mining using Learned Information Extraction System

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A Frame Work for Text Mining using Learned Information Extraction System

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Abstract

Text mining is a very exciting research area as it tries to discover knowledge from unstructured texts. These texts can be found on a computer desktop, intranets and the internet. The aim of this paper is to give an overview of text mining in the contexts of its techniques, application domains and the most challenging issue. The Learned Information Extraction (LIE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DTEX (Discovery Text Extraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DTEX integrates an LIE module acquired by an LIE learning system, and a standard rule induction module. In addition, rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of the underlying extraction system. Applying these techniques best results are presented to a corpus of computer job announcement postings from an Internet newsgroup.

References

37 Cites in Article
  1. 1. R Agrawal,R Srikant (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Databases (VLDB-94), 487-499.
  2. 2. R Baeza-Yates,B Ribeiro-Neto (1999). Modern Information RetrLIEval. Modern Information RetrLIEval
  3. 3. S Basu,R Mooney,K Pasupuleti,J Ghosh (2001). Evaluating the novelty of text-mined rules using lexical knowledge. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), 233-239.
  4. 4. M Berry (2003). Third IEEE International Conference on Data Mining. Third IEEE International Conference on Data Mining
  5. 5. M Califf (1999). Papers from the Sixteenth National Conference on Artificial Intelligence (AAAI-99) Workshop on Machine Learning for Information Extraction. Papers from the Sixteenth National Conference on Artificial Intelligence (AAAI-99) Workshop on Machine Learning for Information Extraction
  6. 6. M Califf,R Mooney (1999). Relational learning of pattern-match rules for information extraction. Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), 328-334.
  7. 7. C Cardlie (1997). Empirical methods in information extraction. AI Magazine, 18(4), 65-79.
  8. 8. C Cardlie,R Mooney (1999). Machine learning and natural language (Introduction to special issue on natural language learning). Machine Learning, 34, 5-9.
  9. 9. F Ciravegna,N Kushmerick (2003). Papers from the 14th European Conference on Machine Learning(ECML-2003) and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases(PKDD-2003) Workshop on Adaptive Text Extraction and Mining. Papers from the 14th European Conference on Machine Learning(ECML-2003) and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases(PKDD-2003) Workshop on Adaptive Text Extraction and Mining
  10. 10. W Cohen (1995). Fast effective rule induction. Proceedings of the Twelfth International Conference on Machine Learning (ICML-95), 115-123.
  11. 11. W Cohen (1996). Learning to classify English text with ILP methods. Advances in Inductive Logic Programming, 124-143.
  12. 12. W Cohen (2003). Improving a page classifLIEr with anchor extraction and link analysis. Advances in Neural Information Processing Systems, 15, 1481-1488.
  13. 13. (1998). Proceedings of the Seventh Message Understanding Evaluation and Conference (MUC-98). Proceedings of the Seventh Message Understanding Evaluation and Conference (MUC-98)
  14. 14. Ronen Feldman,Moshe Fresko,Yakkov Kinar,Yehuda Lindell,Orly Liphstat,Martin Rajman,Yonatan Schler,Oren Zamir (1998). Text mining at the term level. Lecture Notes in Computer Science, 65-73.
  15. 15. D Freitag,N Kushmerick (2000). Boosted wrapper induction. Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), 577-583.
  16. 16. R Ghani,A Fano (2002). Using text mining to infer semantic attirbutes for retail data mining. Proceedings of the 2002 LIEEE International Conference on Data Mining (ICDM-2002), 195-202.
  17. 17. R Ghani,R Jones,D Mladenic´,K Nigam,S Slattery (2016). Data mining on symbolic knowledge Year. Data mining on symbolic knowledge Year
  18. 18. C A Frame (2000). Work for Text Mining using Learned Information Extraction System extracted from the Web. Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining, 29-36.
  19. 19. M Grobelnik (2001). Proceedings of LIEEE International Conference on Data Mining (ICDM2001) Workshop on Text Mining (TextDM'2001). Proceedings of LIEEE International Conference on Data Mining (ICDM2001) Workshop on Text Mining (TextDM'2001)
  20. 20. M Grobelnik (2003). Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence(IJCAI-2003) Workshop on Text Mining and Link Analysis (TextLink-2003). Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence(IJCAI-2003) Workshop on Text Mining and Link Analysis (TextLink-2003)
  21. 21. J Han,M Kamber (2000). Data Mining: Concepts and Techniques. Data Mining: Concepts and Techniques
  22. 22. Marti Hearst (1999). Untangling text data mining. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics -, 3-10.
  23. 23. Marti Hearst (2003). Text Data Mining. What is text mining?
  24. 24. N Kushmerick (2001). Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001) Workshop on Adaptive Text Extraction and Mining. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001) Workshop on Adaptive Text Extraction and Mining
  25. 25. Stanley Loh,Leandro Wives,José De Oliveira (2000). Concept-based knowledge discovery in texts extracted from the Web. ACM SIGKDD Explorations Newsletter, 2(1), 29-39.
  26. 26. A Mccallum,D Jensen (2003). A note on the unification of information extraction and data mining using conditional-probability, relational models. Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data
  27. 27. K Mccallum,Nigam (1998). A comparison of event models for naive Bayes text classification. Papers from the AAAI-98 Workshop on Text Categorization, 41-48.
  28. 28. D Mladenic´ (2000). Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining. Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining
  29. 29. R Mooney,L Roy (2000). Content-based book recommending using learning for text categorization. Proceedings of the Fifth ACM Conference on Digital LibrarLI Es, 195-204.
  30. 30. U Nahm,R Mooney (2000). A mutually beneficial integration of data mining and information extraction. Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), 627-632.
  31. 31. U Nahm,R Mooney (2000). Using information extraction to aid the discovery of prediction rules from texts. Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining, 51-58.
  32. 32. Un Nahm,Raymond Mooney (2001). Mining soft-matching association rules. Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02, 979-984.
  33. 33. U Nahm,R Mooney (2002). Mining soft-matching association rules. Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM2002), 681-683.
  34. 34. J Plierre Mining knowledge from text collections using automatically generated metadata. Mining knowledge from text collections using automatically generated metadata
  35. 35. (2002). Practical Aspects of Knowledge Management.
  36. 36. J Quinlan C4.5: Programs for Machine Learning. C4.5: Programs for Machine Learning
  37. 37. Morgan Kaufmann (1993). Unknown Title.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Sathish Kuppani. 2016. "A Frame Work for Text Mining using Learned Information Extraction System". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C3).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-C Classification I.2.4
D.3.3
Version of record

v1.2

Issue date
July 1, 2016

Language
English
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A Frame Work for Text Mining using Learned Information Extraction System

Sathish Kuppani
Sathish Kuppani SV University