Instance Pruning by Filtering Uninformative Words: An Information Extraction Case Study

  • Alfio Massimiliano Gliozzo
  • Claudio Giuliano
  • Raffaella Rinaldi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3406)


In this paper we present a novel instance pruning technique for Information Extraction (IE). In particular, our technique filters out uninformative words from texts on the basis of the assumption that very frequent words in the language do not provide any specific information about the text in which they appear, therefore their expectation of being (part of) relevant entities is very low. The experiments on two benchmark datasets show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy. We also report an improvement in accuracy for one task.


Frequent Word Information Extraction GENIA Task Word Sense Disambiguation GENIA Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cancedda, N., Gaussier, E., Goutte, C., Renders, J.M.: Word sequence kernels. Journal of Machine Learning Research 3, 1059–1082 (2003)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Ciravegna, F.: Learning to tag for information extraction. In: Ciravegna, F., Basili, R., Gaizauskas, R. (eds.) Proceedings of the ECAI workshop on Machine Learning for Information Extraction, Berlin (2000)Google Scholar
  3. 3.
    Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain (2004)Google Scholar
  4. 4.
    Dagan, I., Itai, A.: Word sense disambiguation using a second language monolingual corpus. Computational Linguistics 20(4), 536–596 (1994)Google Scholar
  5. 5.
    Finn, A., Kushmerick, N.: Multi-level boundary classification for information. In: AAAI 2004 Workshop on Adaptive Text Extraction and Mining (ATEM 2004), San Jose, California (2004)Google Scholar
  6. 6.
    Freitag, D.: Machine Learning for Information Extraction in Informal Domains. PhD thesis, Carnegie Mellon University (1998)Google Scholar
  7. 7.
    Freitag, D., Kushmerick, N.: Boosted wrapper induction. In: AAAI/IAAI, pp. 577–583 (2000)Google Scholar
  8. 8.
    Freitag, D., McCallum, A.: Information extraction with HMM structures learned by stochastic optimization. In: AAAI/IAAI, pp. 584–589 (2000)Google Scholar
  9. 9.
    Gliozzo, A., Strapparava, C., Dagan, I.: Unsupervised and supervised exploitation of semantic domains in lexical disambiguation. Computer Speech and Language 18(3), 275–299 (2004)CrossRefGoogle Scholar
  10. 10.
    Kim, T.O.J., Tateishi, Y., Tsujii, J.: Genia corpus - a semantically annotated corpus for bio-textmining. Bioinformatics 19(Suppl.1), 180–182 (2003)CrossRefGoogle Scholar
  11. 11.
    Joachims, T.: Making large-scale support vector machine learning practical. In: Schölkopf, A.S.B., Burges, C. (eds.) Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge (1998)Google Scholar
  12. 12.
    Kim, J., Ohta, T., Tsuruoka, Y., Tateisi, Y., Collier, N.: Introduction to the bio-entity recognition task at JNLPBA. In: Collier, N., Ruch, P., Nazarenko, A. (eds.) Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA), Geneva, Switzerland, August 28–29, pp. 70–75 (2004); held in conjunction with COLING 2004Google Scholar
  13. 13.
    Lavelli, A., Califf, M., Ciravegna, F., Freitag, D., Giuliano, C., Kushmerick, N., Romano, L.: IE evaluation: Criticisms and recommendations. In: AAAI 2004 Workshop on Adaptive Text Extraction and Mining (ATEM 2004), San Jose, California (2004)Google Scholar
  14. 14.
    Leskovec, J., Shawe-Taylor, J.: Linear programming boosting for uneven datasets. In: Fawcett, T., Mishra, N. (eds.) Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), Washington, DC, USA, August 21-24, pp. 456–463. AAI Press (2003)Google Scholar
  15. 15.
    Roth, D., tau Yih, W.: Relational learning via propositional algorithms: An information extraction case study. In: Seventeenth International Joint Conf. on Artificial Intelligence, 2001 (2001)Google Scholar
  16. 16.
    Song, Y., Yi, E., Kim, E., Lee, G.G.: Posbiotm-ner: A machine learning approach for bio-named entity recognition. In: The 20th International Conference on Computational Linguistics (2004)Google Scholar
  17. 17.
    Yarowsky, D.: One sense per collocation. In: ARPA Workshop on Human Language Technology (1993)Google Scholar
  18. 18.
    Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley, Reading (1949)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alfio Massimiliano Gliozzo
    • 1
  • Claudio Giuliano
    • 1
  • Raffaella Rinaldi
    • 1
  1. 1.Istituto per la Ricerca Scientifica e TecnologicaITC-irstTrentoItaly

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