Enabling information extraction by inference of regular expressions from sample entities

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 1285-1294

  Brauer, Falk ^a   Rieger, Robert ^a   Mocan, Adrian ^a   Barczynski, Wojciech M ^a  

^a SAP RESEARCH   (Germany)

Author keywords

information extraction; machine learning; minimum description length; regular expressions

Indexed keywords

ENTERPRISE DATABASE; EXPERT KNOWLEDGE; INFORMATION EXTRACTION; MACHINE-LEARNING; MINIMUM DESCRIPTION LENGTH; PRODUCT CATALOGS; PRODUCT NAME; RECALL AND PRECISION; REGULAR EXPRESSIONS; TEXT DATA;

KNOWLEDGE MANAGEMENT;

INFORMATION ANALYSIS;

EID: 83055186929     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063763     Document Type: Conference Paper

References (20)

1. G. J. Bex, F. Neven, T. Schwentick, and S. Vansummeren. Inference of concise regular expressions and dtds. TODS, 35(2):1-47, 2010.
2. F. Brauer, R. Rieger, W. Barczynski, and A. Mocan. Regular language inference for domain-specific named entity recognition. In IADIS WWW/Internet, 2009.
3. J. Y. Chai, A. W. Biermann, and C. I. Guinn. Two dimensional generalization in information extraction. In AAAI, pages 431-438, 1999.
4. F. Ciravegna. Adaptive information extraction from text by rule induction and generalisation. In IJCAI, pages 1251-1256, 2001.
5. H. Fernau. Algorithms for learning regular expressions from positive data. Information and Computation, 207(4):521-541, 2009.
6. D. Freitag. Machine learning for information extraction in informal domains. Machine Learning, 39(2-3):169-202, 2000. (Pubitemid 30594820)
7. M. N. Garofalakis, A. Gionis, R. Rastogi, S. Seshadri, and K. Shim. Xtract: A system for extracting document type descriptors from xml documents. In SIGMOD, pages 165-176, 2000.
8. P. Grünwald. A tutorial introduction to the minimum description length principle. CoRR, math.ST/0406077, 2004.
9. G. Kasneci, F. M. Suchanek, G. Ifrim, S. Elbassuoni, M. Ramanath, and G. Weikum. Naga: Harvesting, searching and ranking knowledge. In SIGMOD, pages 1285-1288. Association for Computing Machinery (ACM), 2008.
10. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, pages 282-289, 2001.
11. Y. Li, R. Krishnamurthy, S. Raghavan, S. Vaithyanathan, and H. V. Jagadish. Regular expression learning for information extraction. In EMNLP, pages 21-30, 2008.
12. I. R. Mansuri and S. Sarawagi. Integrating unstructured data into relational databases. In ICDE, page 29, 2006.
13. P. Palaga, L. Nguyen, U. Leser, and J. Hakenberg. High-performance information extraction with alibaba. In EDBT, pages 1140-1143, 2009.
14. R. Parekh and V. Honavar. Automata induction, grammar inference, and language acquisition. In The Handbook of Natural Language Processing, pages 727-764. Marcel Dekker Inc., New York, Ny, USA, 2000.
15. V. Raman and J. M. Hellerstein. Potter's wheel: An interactive data cleaning system. In VLDB, pages 381-390, 2001.
16. S. Sarawagi. Information extraction. Foundations and Trends in Databases, 1(3):261-377, 2008.
17. S. Soderland. Learning information extraction rules for semi-structured and free text. Machine Learning, 34(1-3):233-272, 1999.
18. P. P. Talukdar, T. Brants, M. Liberman, and F. Pereira. A context pattern induction method for named entity extraction. In CoNLL, pages 141-148, 2006.
19. J. Turmo, A. Ageno, and N. Català. Adaptive information extraction. ACM Computing Surveys, 38(2):4, 2006.
20. H. Yu, X. Zhu, M. Huang, and M. Li. Discovering patterns to extract protein-protein interactions from the literature: Part ii. Bioinformatics, 21(15):3294-3300, 2005. (Pubitemid 41418444)