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Expansion Finding for Given Acronyms Using Conditional Random Fields

  • Jie Liu
  • Jimeng Chen
  • Tianbi Liu
  • Yalou Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)

Abstract

There are increasingly amount of acronyms in many kinds of documents and web pages, which is a serious obstacle for the readers. This paper addresses the task of finding expansions in texts for given acronym queries. We formulate the expansion finding problem as a sequence labeling task and use Conditional Random Fields to solve it. Since it is a complex task, our method tries to enhance the performance from two aspects. First,we introduce nonlinear hidden layers to learn better representations of the input data under the framework of Conditional Random Fields. Second, simple and effective features are designed. The experimental results on real data show that our model achieves the best performance against the state-of-the-art baselines including Support Vector Machine and standard Conditional Random Fields.

Keywords

Web-mining text-mining named entities recognition acronym 

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References

  1. 1.
    Taghva, K., Gilbreth, J.: Recognizing acronyms and their definitions. IJDAR 1(4), 191–198 (1999)CrossRefGoogle Scholar
  2. 2.
    Yeates, S.: Automatic extraction of acronyms from text. In: New Zealand Computer Science Research Students’ Conference, pp. 117–124 (1999)Google Scholar
  3. 3.
    Larkey, L.S., Ogilvie, P., Price, M.A., Tamilio, B.: Acrophile: An automated acronym extractor and server. In: Proceedings of the ACM Fifth International Conference on Digital Libraries, DL 2000, Dallas TX, pp. 205–214. ACM Press, New York (2000)Google Scholar
  4. 4.
    Roche, M., Prince, V.: Managing the acronym/expansion identification process for text-mining applications. Int. J. Software and Informatics, 163–179 (2008)Google Scholar
  5. 5.
    Lafferty, J., Mccallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)Google Scholar
  6. 6.
    Tasker, B., Pieter, A., Koller, D.: Discriminative probabilistic models for relational data. In: Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2002), pp. 485–492. Morgan Kaufmann, San Francisco (2002)Google Scholar
  7. 7.
    Peng, F., Mccallum, A.: Information extraction from research papers using conditional random fields. Information Processing & Management 42(4), 963–979 (2006)CrossRefGoogle Scholar
  8. 8.
    Settles, B.: Abner: an open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinformatics (April 2005)Google Scholar
  9. 9.
    Sha, F., Pereira, F.: Shallow parsing with conditional random fields (2003)Google Scholar
  10. 10.
    Sato, K., Sakakibara, Y.: RNA secondary structural alignment with conditional random fields. Bioinformatics 21(suppl. 2), ii237–ii242 (2005)Google Scholar
  11. 11.
    Liu, Y., Carbonell, J., Weigele, P., Gopalakrishnan, V.: Segmentation conditional random fields (scrfs): A new approach for protein fold recognition. In: Proc. of the 9th Ann. Intl. Conf. on Comput. Biol (RECOMB), pp. 14–18. ACM Press, New York (2005)Google Scholar
  12. 12.
    He, X., Zemel, R.S., Carreira-Perpinan, M.A.: Multiscale conditional random fields for image labeling, vol. 2, II-695–II-702 (2004)Google Scholar
  13. 13.
    Kumar, S., Hebert, M.: Discriminative fields for modeling spatial dependencies in natural images (2003)Google Scholar
  14. 14.
    Lafferty, J., Zhu, X., Liu, Y.: Kernel conditional random fields: representation and clique selection. In: ICML (2004)Google Scholar
  15. 15.
    Liu, J., Yu, K., Zhang, Y., Huang, Y.: Training conditional random fields using transfer learning for gesture recognition. In: ICDM 2010: Proceedings of the 10th International Conference on Data Ming, Sydney, Australia (2010)Google Scholar
  16. 16.
    Peng, J., Bo, L., Xu, J.: Conditional neural fields. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 1419–1427 (2009)Google Scholar
  17. 17.
    Vapnik, V.N.: The Nature of Statistical Learning Theory (Information Science and Statistics). Springer, Heidelberg (November 1999)Google Scholar
  18. 18.
    Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  19. 19.
    Ramshaw, L., Marcus, M.: Text chunking using Transformation-Based learning. In: Yarovsky, D., Church, K. (eds.) Proceedings of the Third Workshop on Very Large Corpora, Somerset, New Jersey, pp. 82–94. Association for Computational Linguistics (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jie Liu
    • 1
  • Jimeng Chen
    • 1
  • Tianbi Liu
    • 2
  • Yalou Huang
    • 2
  1. 1.College of Information Technical ScienceNankai UniversityTianjinChina
  2. 2.College of SoftwareNankai UniversityTianjinChina

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