Advertisement

An adaptive model for sequential labeling systems

Application for management change event
  • Samir ElloumiEmail author
Article
  • 20 Downloads

Abstract

There are many levels in the task of information extraction. Level 1 deals with named entities such as PERSON, ORG, DATE, etc. Level 2 concerns the role played by the named entities wrt a specific event. For instance, in a management change event, a PERSON might be either the new coming person to the company or the leaving one. Building learning models for event extraction without considering the different levels is completely misleading. In this paper, the reasons for considering these levels are explained, and an adaptive model for event extraction is proposed. It could be applied on any sequence labeleling system, e.g., CRF-based classifier, RNN, LSTM, etc. The experimental results show that the adaptive model outperforms the direct model in terms of efficiency and gives comparable results compared to GLA2E, an expert’s pattern based event extractor.

Keywords

Event extraction Adaptive model Sequence labeling GLA2E 

Notes

References

  1. 1.
    Besanċon R, De Chalendar G, Ferret O, Gara F, Mesnard O, Laïb M, Semmar N (2010) Lima: a multilingual framework for linguistic analysis and linguistic resources development and evaluation. In: Proceedings of the seventh conference on international language resources and evaluation (LREC’10). VallettaGoogle Scholar
  2. 2.
    Bikel DM, Schwartz R, Weischedel RM (1999) An algorithm that learns what’s in a name. Mach Learn 34(1):211–231CrossRefGoogle Scholar
  3. 3.
    Borthwick A, Grishman R (1999) A maximum entropy approach to named entity recognition. Ph.D. thesis, New York University Graduate School of Arts and ScienceGoogle Scholar
  4. 4.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  5. 5.
    Bunescu RC, Pasca M (2006) Using encyclopedic knowledge for named entity disambiguation. In: Eacl, vol 6, pp 9–16Google Scholar
  6. 6.
    Burke E, Wada A, Coe B (2017) Electronic document information extraction. US Patent 9,547,648Google Scholar
  7. 7.
    Carreras X, Màrquez L, Padró L (2003) A simple named entity extractor using adaboost. In: Proceedings of the seventh conference on natural language learning at HLT-NAACL 2003, vol 4. Association for Computational Linguistics, pp 152–155Google Scholar
  8. 8.
    Chieu HL, Ng HT (2002) Named entity recognition: a maximum entropy approach using global information. In: Proceedings of the 19th international conference on computational linguistics, vol 1. Association for Computational Linguistics, pp 1–7Google Scholar
  9. 9.
    Dietterich T (2002) Machine learning for sequential data: a review. Structural, Syntactic, and Statistical Pattern Recognition, 227–246Google Scholar
  10. 10.
    Ekbal A, Bandyopadhyay S (2010) Named entity recognition using support vector machine: a language independent approach. Int J Electric Comput Syst Eng 4 (2):155–170zbMATHGoogle Scholar
  11. 11.
    Elloumi S, Jaoua A, Ferjani F, Semmar N, Besanson R, Jaam J, Hammami H (2012) General learning approach for event extraction: case of management change event. Journal of Information Science (JIS) 39:211–224.  https://doi.org/10.1177/0165551512464140 CrossRefGoogle Scholar
  12. 12.
    Feng X, Qin B, Liu T (2018) A language-independent neural network for event detection. Sci Chin Inf Sci 61(9):092,106CrossRefGoogle Scholar
  13. 13.
    Finkel JR, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 363–370Google Scholar
  14. 14.
    Goller C, Kuchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. Neural Netw 1:347–352Google Scholar
  15. 15.
    Grishman R, Sundheim B (1996) Message understanding conference-6: a brief history. In: Proceedings of the 16th conference on computational linguistics - volume 1, COLING ’96. Association for Computational Linguistics, Stroudsburg, pp 466–471,  https://doi.org/10.3115/992628.992709
  16. 16.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neur Comput 9(8):1735–1780CrossRefGoogle Scholar
  17. 17.
    Huang Z, Xu W, Yu K (2015) Bidirectional lstm-crf models for sequence tagging. arXiv:1508.01991
  18. 18.
    Huffman S (1996) Connectionist, statistical, and symbolic approaches to learning for natural language processing chapter learning information extraction patterns from examplesGoogle Scholar
  19. 19.
    Isozaki H, Kazawa H (2002) Efficient support vector classifiers for named entity recognition. In: Proceedings of the 19th international conference on computational linguistics, vol 1. Association for Computational Linguistics, pp 1–7Google Scholar
  20. 20.
    Jaoua A, Jaam J, Hammami H, Ferjani F, Laban F, Semmar N, Essafi H, Elloumi S (2010) Financial events detection by conceptual news categorization. In: Proceedings of the international conference on intelligent systems design and applications (ISDA’2010). Cairo, pp 1101–1106Google Scholar
  21. 21.
    Kazama J, Torisawa K (2007) Exploiting wikipedia as external knowledge for named entity recognition. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 698–707Google Scholar
  22. 22.
    Laerty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICMLGoogle Scholar
  23. 23.
    Liu H, Yang Z, Wu Z, et al. (2011) Locality-constrained concept factorization. In: IJCAI Proceedings-international joint conference on artificial intelligence, vol 22, p 1378Google Scholar
  24. 24.
    Ma X, Hovy E (2016) End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv:1603.01354
  25. 25.
    McCallum A, Li W (2003) Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of the seventh conference on natural language learning at HLT-NAACL 2003, vol 4. Association for Computational Linguistics, pp 188–191Google Scholar
  26. 26.
    McCallum A, Freitag D, Pereira FC (2000) Maximum entropy Markov models for information extraction and segmentation. In: Icml, vol 17, pp 591–598Google Scholar
  27. 27.
    Mooney RJ, Bunescu RC (2006) Subsequence kernels for relation extraction. In: Advances in neural information processing systems, pp 171–178Google Scholar
  28. 28.
    Ramshaw LA, Marcus MP (1999) Text chunking using transformation-based learning. In: Natural language processing using very large corpora. Springer, pp 157–176Google Scholar
  29. 29.
    Richman AE, Schone P (2008) Mining wiki resources for multilingual named entity recognition. In: ACL, pp 1–9Google Scholar
  30. 30.
    Rodriguez-Esteban R (2017) Text mining applications. In: Reference module in life sciences. Elsevier.  https://doi.org/10.1016/B978-0-12-809633-8.12372-6 CrossRefGoogle Scholar
  31. 31.
    Sekine S, Grishman R, Shinnou H (1998) A decision tree method for finding and classifying names in Japanese texts. In: Proceedings of the sixth workshop on very large corporaGoogle Scholar
  32. 32.
    Yan W, Zhang B, Ma S, Yang Z (2017) A novel regularized concept factorization for document clustering. Knowl-Based Syst 135:147–158CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Sciences of TunisUniversity of Tunis El ManarTunisTunisia

Personalised recommendations