ONDUX: On-demand unsupervised learning for information extraction

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2010, Pages 807-818

  Cortez, Eli ^a   Da Silva, Altigran S ^a   Gonçalves, Marcos André ^b   De Moura, Edleno S ^a  

^a FEDERAL UNIVERSITY OF AMAZONAS   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

data management; information extraction; text segmentation

Indexed keywords

ATTRIBUTE VALUES; BIBLIOGRAPHIC INFORMATION; DATA MANAGEMENT; DATA VALUES; DEGREE OF FLEXIBILITY; DIFFERENT DOMAINS; EFFECTIVE MATCHING; EXPERIMENTAL EVALUATION; INFORMATION EXTRACTION; INPUT STRING; LEARNING STRATEGY; ON-DEMAND; PRACTICAL PROBLEMS; PROBABILISTIC APPROACHES; SEMI-STRUCTURED; TEST DATA; TEXT SEGMENTATION;

BIBLIOGRAPHIC RETRIEVAL SYSTEMS; INFORMATION ANALYSIS; REINFORCEMENT;

FEATURE EXTRACTION;

EID: 77954718971     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1807167.1807254     Document Type: Conference Paper

References (20)

1. E. Agichtein and V. Ganti. Mining reference tables for automatic text segmentation. Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 20-29, Seattle, Washington, USA, 2004.
2. S. Agrawal, S. Chaudhuri, G. Das, and A. Gionis. Automated ranking of database query results. Proc. of CIDR 2003, Biennial Conference on Innovative Data Systems Research, 2003.
3. T. Anderson and J. Finn. The New Statistical Analysis of Data. Springer, 1996.
4. V. R. Borkar, K. Deshmukh, and S. Sarawagi. Automatic segmentation of text into structured records. Proc. of the ACM SIGMOD International Conference on Management of Data, pages 175-186, 2001.
5. S. Chuang, K. Chang, and C. Zhai. Context-aware wrapping: synchronized data extraction. Proc. of the 33rd Intl. Conf. on Very Large Databases, pages 699-710, Viena, Austria, 2007.
6. E. Cortez, A. da Silva, M. Gonçalves, F. Mesquita, and E. de Moura. FLUX-CIM: flexible unsupervised extraction of citation metadata. Proc. of the 2007 conference on Digital libraries, pages 215-224, 2007.
7. E. Cortez, A. da Silva, M. Gonçalves, F. Mesquita, and E. de Moura. A flexible approach for extracting metadata from bibliographic citations. Journal of the American Society for Information Science and Technology, Online version, 2009.
8. D. Freitag and A. McCallum. Information extraction with hmm structures learned by stochastic optimization. In Proc. of the 17th National Conf. on Artificial Intelligence and 12th Conf. on Innovative Applications of Artificial Intelligence, pages 584-589, Austin, Texas, USA, 2000.
9. T. Joachims. Transductive inference for text classification using support vector machines. In Proc. of the International Conference on Machine Learning, pages 200-209, Bled, Slovenia, 1999.
10. L. P. Kaelbling, M. L. Littman, and A. P. Moore. Reinforcement learning: A survey. J. Artif. Intell. Res. (JAIR), 4:237-285, 1996.
11. J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. of the Eighteenth International Conference on Machine Learning, pages 282-289, 2001.
12. I. R. Mansuri and S. Sarawagi. Integrating unstructured data into relational databases. In Proc. of the International Conference on Data Engineering, page 29. IEEE Computer Society, 2006.
13. A. McCallum. Cora Information Extraction Collection.
14. F. Mesquita, A. da Silva, E. de Moura, P. Calado, and A. Laender. LABRADOR: Efficiently publishing relational databases on the web by using keyword-based query interfaces. Information Processing and Management, 43(4):983-1004, 2007.
15. I. Muslea. Rise - A Repository of Online Information Sources used in Information Extraction Tasks.
16. U. Nambiar and S. Kambhampati. Answering imprecise queries over autonomous web databases. In Proc. of the International Conference on Data Engineering, page 45, Washington, DC, USA, 2006.
17. J. Pearl and G. Shafer. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann San Mateo, CA, 1988.
18. F. Peng and A. McCallum. Information extraction from research papers using conditional random fields. Information Processing Management, 42(4):963-979, 2006.
19. S. Sarawagi. Information extraction. Foundations and Trends in Databases, 1(3):261-377, 2008.
20. C. Zhao, J. Mahmud, and I. V. Ramakrishnan. Exploiting structured reference data for unsupervised text segmentation with conditional random fields. In Proc. of the SIAM International Conference on Data Mining, pages 420-431, Atlanta, Georgia, USA, 2008.