Advertisement

An Information Extraction Customizer

  • Ralph Grishman
  • Yifan He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

When an information extraction system is applied to a new task or domain, we must specify the classes of entities and relations to be extracted. This is best done by a subject matter expert, who may have little training in NLP. To meet this need, we have developed a toolset which is able to analyze a corpus and aid the user in building the specifications of the entity and relation types.

Keywords

information extraction distributional analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Akbik, A., Konomi, O., Melnikov, M.: Propminer: A workflow for interactive information extraction and exploration using dependency trees. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: Systems Demonstrations, pp. 157–162 (2013)Google Scholar
  3. 3.
    Bunescu, R., Mooney, R.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 724–731 (2005)Google Scholar
  4. 4.
    Freedman, M., Ramshaw, L., Boschee, E., Gabbard, R., Kratkiewicz, G., Ward, N., Weischedel, R.: Extreme Extraction – Machine Reading in a Week. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1437–1446 (2011)Google Scholar
  5. 5.
    Fu, L., Grishman, R.: An Efficient Active Learning Framework for New Relation Types. In: Proceedings of International Joint Conference on Natural Language Processing (IJCNLP), Nagoya, Japan, pp. 692–698 (2013)Google Scholar
  6. 6.
    Goldberg, Y., Elhadad, M.: An efficient algorithm for easy-first non-directional dependency parsing. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, pp. 742–750 (2010)Google Scholar
  7. 7.
    Grishman, R.: Information Extraction: Capabilities and Challenges. The 2012 International Winter School in Language and Speech Technologies, Rovira i Virgili University, Spain (2012), http://cs.nyu.edu/grishman/survey.html
  8. 8.
    Java Extraction Toolkit, http://cs.nyu.edu/grishman/jet/jet.html
  9. 9.
    Lehmann, J., Monahan, S., Nezda, L., Jung, A., Shi, Y.: Approaches to Knowledge Base Population at TAC 2010. In: Proceedings of the 2010 Text Analysis Conference (2010)Google Scholar
  10. 10.
    Li, Y., Chiticariu, L., Yang, H., Reiss, F., Carreno-fuentes, A.: WizIE: A best practices guided development environment for information extraction. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 109–114 (2012)Google Scholar
  11. 11.
    Min, B., Grishman, R.: Fine-grained entity refinement with user feedback. In: Proceedings of RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition, pp. 2–6 (2011)Google Scholar
  12. 12.
    Shinyama, Y., Sekine, S.: Preemptive information extraction using unrestricted relation discovery. In: Proceedings of the Human Language Technology Conference of the NAACL, pp. 304–311 (2006)Google Scholar
  13. 13.
    Sun, A., Grishman, R.: Active learning for relation type extension with local and global data views. In: Proc. 21st ACM International Conf. on Information and Knowledge Management (CIKM 2012), pp. 1105–1112 (2012)Google Scholar
  14. 14.
    Surdeanu, M., Harabagiu, S.: Infrastructure for Open-Domain Information Extraction. In: Proceedings of the Second International Conference on Human Language Technology Research, HLT 2002, pp. 325–330 (2002)Google Scholar
  15. 15.
    Tratz, S., Hovy, E.: A fast, effective, non-projective, semantically-enriched parser. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK, pp. 1257–1268 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ralph Grishman
    • 1
  • Yifan He
    • 1
  1. 1.Department of Computer ScienceNew York UniversityNew YorkUSA

Personalised recommendations