Joint unsupervised structure discovery and information extraction

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2011, Pages 541-552

  Cortez, Eli ^a   Oliveira, Daniel ^a   Da Silva, Altigran S ^a   De Moura, Edleno S ^a   Laender, Alberto H F ^b  

^a FEDERAL UNIVERSITY OF AMAZONAS   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

data management; information extraction; text segmentation

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; INFORMATION RETRIEVAL;

BIBLIOGRAPHIC CITATIONS; CLASSIFIED ADS; DELIMITERS; INFORMATION EXTRACTION; INFORMATION EXTRACTION METHODS; NOVEL STRUCTURES; SEMISTRUCTURED DATA; STATE OF THE ART; TEXT SEGMENTATION; USER ASSISTANCE;

INFORMATION MANAGEMENT;

EID: 79959964847     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1989323.1989380     Document Type: Conference Paper

References (19)

1. E. Agichtein and V. Ganti. Mining Reference Tables for Automatic Text Segmentation. In Proc. 10th ACM SIGKDD Intl. Conf. on Knowl. Discov. and Data Mining, pages 20-29, 2004.
2. L. Barbosa and J. Freire. Using Latent-structure to Detect Objects on the Web. In Proc. of the 13th Intl. Workshop on the Web and Databases, pages 1-6, 2010.
3. W. Cohen and S. Sarawagi. Exploiting Dictionaries in Named Entity Extraction: Combining Semi-Markov Extraction Processes and Data Integration Methods. In Proc. 10th ACM SIGKDD Intl. Conf. on Knowl. Discov. and Data Mining, pages 89-98, 2004.
4. A. Doan et. al. Information Extraction Challenges in Managing Unstructured Data. SIGMOD Record, 37(4):14-20, 2008.
5. C. Zhao et. al. Exploiting Structured Reference Data for Unsupervised Text Segmentation with Conditional Random Fields. In Proc. SIAM Intl. Conf. on Data Mining, pages 420-431, 2008.
6. D. Buttler et. al. A Fully Automated Object Extraction System for the World Wide Web. In Proc. of the 21st Intl. Conf. on Dist. Comp. Syst., pages 361-370, 2001.
7. D. W. Embley et. al. Conceptual-model-based data extraction from multiple-record web pages. Data Knowl. Eng., 31(3):227-251, 1999.
8. E. Cortez et. al. ONDUX: On-Demand Unsupervised Learning for Information Extraction. In Proc. ACM SIGMOD Intl. Conf. on Management of Data, pages 807-818, 2010.
9. F. Mesquita et. al. LABRADOR: Efficiently publishing relational databases on the web by using keyword-based query interfaces. Inform. Proc. and Management, 43(4):983-1004, 2007.
10. J. D. Lafferty et. al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. 8th Intl. Conf. on Mach. Learning, pages 282-289, 2001.
11. S. Agrawal et. al. Automated Ranking of Database Query Results. In Proc. of the First Biennial Conf. on Innov. Data Syst. Research, 2003.
12. V. Borkar et. al. Automatic Segmentation of Text into Structured Records. In Proc. ACM SIGMOD Intl. Conf. on Management of Data, pages 175-186, 2001.
13. D. Freitag and A. McCallum. Information Extraction with HMM Structures Learned by Stochastic Optimization. In Proc. of the 17th Nat. Conf. on Art. Intell. and 12th Conf. on Innov. Appl. of Art. Intell., pages 584-589, 2000.
14. I. R. Mansuri and S. Sarawagi. Integrating Unstructured Data into Relational Databases. In Proc. 22nd Intl. Conf. on Data Engineering, page 29, 2006.
15. J. Pearl and G. Shafer. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
16. F. Peng and A. McCallum. Information extraction from research papers using conditional random fields. Inform. Proc. and Management, 42(4):963-979, 2006. (Pubitemid 41828296)
17. S. Sarawagi. Information extraction. Foundations and Trends in Databases, 1(3):261-377, 2008.
18. C. E. Shannon. A mathematical theory of communication. ACM SIGMOBILE Mobile Comp. and Comm. Rev., 5(1):3-55, 2001.
19. S. Soderland. Learning information extraction rules for semi-structured and free text. Mach. Learn., 34:233-272, 1999.