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Cited by 120

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Information Extraction: Distilling structured data from unstructured text

McCallum, Andrew·2005·Queue
DOI 10.1145/1105664.1105679
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Abstract

In 2001 the U.S. Department of Labor was tasked with building a Web site that would help people find continuing education opportunities at community colleges, universities, and organizations across the country. The department wanted its Web site to support fielded Boolean searches over locations, dates, times, prerequisites, instructors, topic areas, and course descriptions. Ultimately it was also interested in mining its new database for patterns and educational trends. This was a major data-integration project, aiming to automatically gather detailed, structured information from tens of thousands of individual institutions every three months.

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Research works printed but not linked: 19

  1. McCallum A., International Joint Conferences on Artificial Intelligence.
  2. Lafferty J., Proceedings of the ICML: 282--289
  3. 10.3115/1119176.1119204 · DOI · Paper page
  4. 10.1145/1134271.1134276 · doi:10.1145/1134271.1134276
  5. 10.3115/974557.974586 · DOI · Paper page
  6. McCallum A., IJCAI Workshop on Learning Statistical Models from Relational Data.
  7. 10.1109/2.769447 · DOI · Paper page
  8. Soderland S. and Lehnert W. G. 1994. Corpus-driven knowledge acquisition for discourse analysis. AAAI. Soderland S. and Lehnert W. G. 1994. Corpus-driven knowledge acquisition for discourse analysis. AAAI.
  9. 10.1145/775047.775061 · DOI · Paper page
  10. Carvalho V. R., Conference on E-mail and Spam (CEAS).
  11. Califf M. E., Proceedings of the National Conference on Artificial Intelligence.
  12. Freitag D., 1999, Proceedings of the AAAI Workshop on Machine Learning for Information Extraction.
  13. 10.3115/1072228.1072379 · DOI
  14. Nahm U. Y. and Mooney R. J. 2000. A mutually beneficial integration of data mining and information extraction. AAAI/IAAI: 627--632. Nahm U. Y. and Mooney R. J. 2000. A mutually beneficial integration of data mining and information extraction. AAAI/IAAI: 627--632.
  15. 10.3115/1218955.1219009 · DOI · Paper page
  16. 10.3115/1219840.1219841 · DOI · Paper page
  17. McCallum A., Proceedings of ICML: 591--598
  18. Wellner B., Conference on Uncertainty in Artificial Intelligence (UAI).
  19. Kristjannson T., 2004, Nineteenth National Conference on Artificial Intelligence.

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Metadata

Authors
McCallum, A.
Venue
Queue
Publisher
Association for Computing Machinery (ACM)
Published
2005-11
Volume
3
Issue
9
Pages
48-57
ISSN
  • 1542-7730
  • 1542-7749
DOI
10.1145/1105664.1105679
Cited by
120
References
0
Language
en
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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CoChat. (2026). Citation check: Information Extraction: Distilling structured data from unstructured text. https://app.cochat.ai/papers/10.1145/1105664.1105679
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McCallum, A. (2005). Information Extraction: Distilling structured data from unstructured text. Queue. https://doi.org/10.1145/1105664.1105679

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.