skip to main content
10.1145/1298406.1298442acmconferencesArticle/Chapter ViewAbstractPublication Pagesk-capConference Proceedingsconference-collections
Article

Extracting procedures from text

Published: 28 October 2007 Publication History

Abstract

We present our progress on a fully automated system that extracts procedures from text. The system extracts a structured, plan-like graph that represents the procedure described in the text it was given. We hope such a system can be used to populate knowledge bases with real-world procedures that will fuel procedure representation and reasoning research geared towards everyday applications. The results obtained on the training data indicate that the task, and the method presented are feasible and should be investigated further.

References

[1]
D. M. Bikel, R. L. Schwartz, and R. M. Weischedel. An algorithm that learns what's in a name. Machine Learning, 34(1--3):211--231, 1999.
[2]
W. W. Cohen. Minorthird: Methods for identifying names and ontological relations in text using heuristics for inducing regularities from data, 2004.
[3]
S. E. Fahlman. Marker-passing inference in the Scone knowledge--base system. In Proc. of the First International Conference on Knowledge Science, Engineering and Management, pages 114--126. Springer-Verlag, 2006.
[4]
R. E. Fikes and N. J. Nilsson. Strips: A new approach to the application of theorem proving to problem solving. In Proc. of the 2nd IJCAI, pages 608--620, London, UK, 1971.
[5]
D. Klein, J. Smarr, H. Nguyen, and C. Manning. Named entity recognition with character-level models, 2003.
[6]
A. Ratnaparkhi. A maximum entropy model for part-of-speech tagging. In E. Brill and K. Church, editors, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 133--142. Association for Computational Linguistics, Somerset, New Jersey, 1996.
[7]
M. Veloso, J. Carbonell, A. Perez, D. Borrajo, E. Fink, and J. Blythe. Integrating planning and learning: The PRODIGY architecture. Journal of Experimental and Theoretical Artificial Intelligence, 7(1):81--120.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
K-CAP '07: Proceedings of the 4th international conference on Knowledge capture
October 2007
216 pages
ISBN:9781595936431
DOI:10.1145/1298406
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2007

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. knowledge acquisition from text
  2. knowledge extraction

Qualifiers

  • Article

Conference

K-CAP07
Sponsor:
K-CAP07: International Conference on Knowledge Capture 2007
October 28 - 31, 2007
BC, Whistler, Canada

Acceptance Rates

Overall Acceptance Rate 55 of 198 submissions, 28%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 218
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media