ONDUX: on-demand unsupervised learning for information extraction

ABSTRACT

Information extraction by text segmentation (IETS) applies to cases in which data values of interest are organized in implicit semi-structured records available in textual sources (e.g. postal addresses, bibliographic information, ads). It is an important practical problem that has been frequently addressed in the recent literature. In this paper we introduce ONDUX (On Demand Unsupervised Information Extraction), a new unsupervised probabilistic approach for IETS. As other unsupervised IETS approaches, ONDUX relies on information available on pre-existing data to associate segments in the input string with attributes of a given domain. Unlike other approaches, we rely on very effective matching strategies instead of explicit learning strategies. The effectiveness of this matching strategy is also exploited to disambiguate the extraction of certain attributes through a reinforcement step that explores sequencing and positioning of attribute values directly learned on-demand from test data, with no previous human-driven training, a feature unique to ONDUX. This assigns to ONDUX a high degree of flexibility and results in superior effectiveness, as demonstrated by the experimental evaluation we report with textual sources from different domains, in which ONDUX is compared with a state-of-art IETS approach.

References

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. Laerty, 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.

Index Terms

• Computing methodologies

  • Machine learning

• Information systems

  • Data management systems