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Boundary detection of multiple related temporal duration of schedules in email

Published:26 June 2011Publication History

ABSTRACT

Emails are very popular method for information exchange between people. In this paper, an approach to annotate the starting time (stime) and ending time (etime) of duration in schedule notices is proposed. Most related works have reported on only seminar announcements, most of which contain only one schedule per announcement and are written in very restricted format. Different from those seminar announcements, an email frequently contains information about multiple schedules with highly complex format. To process the emails, the proposed system first detects and normalizes all time expressions of the email using regular expression patterns, and then determines which time expression actually represents stime and etime information of schedules. Evaluation is carried out on newly constructed Korean email corpus, and it shows 87.35 % of F1-score for stime and 85.13 % for etime.

References

  1. 1} C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.Google ScholarGoogle Scholar
  2. H. L. Chieu and H. T. Ng. A maximum entropy approach to information extraction from semistructured and free text. In Proceedings of the 18th National Conference On Artificial Intelligence, pages 786--791, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Freed, J. Carbonell, G. Gordon, J. Hayes, B. Myers, D. Siewiorek, S. Smith, A. Steinfeld, and A. Tomasic. Radar: A personal assistant that learns to reduce email overload. In Proceedings of the 23rd national conference on Artificial Intelligence, pages 1287--1293, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. Han, D. Gates, and L. Levin. From language to time: A temporal expression anchorer. In Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning, pages 196--203, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. K. McCallum. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu, 2002.Google ScholarGoogle Scholar
  6. L. Peshkin and A. Pfeffer. Bayesian information extraction network. In Proceedings of the 18th International Joint Conference On Artificial Intelligence, pages 421--426, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Roth and W. Yih. Relational learning via propositional algorithms: An information extraction case study. In Proceedings of the 15th International Conference On Artificial Intelligence, pages 1257--1263, San Francisco, CA, August 2001. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C. Sutton and A. McCallum. Composition of conditional random fields for transfer learning. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 748--754, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Turmo, A. Ageno, and N. Catala. Adaptive information extraction. ACM Computing Surveys, 38(2), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. B. Yuan, Q. Chen, X. Wang, and L. Han. Extracting event temporal information based on web. In Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling, pages 346--350, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          K-CAP '11: Proceedings of the sixth international conference on Knowledge capture
          June 2011
          212 pages
          ISBN:9781450303965
          DOI:10.1145/1999676

          Copyright © 2011 ACM

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          Publication History

          • Published: 26 June 2011

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