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An adaptive model for sequential labeling systems

Application for management change event

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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.

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Notes

  1. Available athttps://www.aclweb.org/portal/content/text-analysis-conference-2 (acceded Dec. 2017)

  2. Available athttps://babelnet.org (acceded Dec. 2017)

  3. Available athttps://www.wikipedia.org/ (acceded Dec. 2017)

  4. Available athttps://www.depends-on-the-definition.com (October. 2018)

References

  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). Valletta

  2. Bikel DM, Schwartz R, Weischedel RM (1999) An algorithm that learns what’s in a name. Mach Learn 34(1):211–231

    Article  MATH  Google Scholar 

  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 Science

  4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  5. Bunescu RC, Pasca M (2006) Using encyclopedic knowledge for named entity disambiguation. In: Eacl, vol 6, pp 9–16

  6. Burke E, Wada A, Coe B (2017) Electronic document information extraction. US Patent 9,547,648

  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 152–155

  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 1–7

  9. Dietterich T (2002) Machine learning for sequential data: a review. Structural, Syntactic, and Statistical Pattern Recognition, 227–246

  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):155–170

    MATH  Google Scholar 

  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

    Article  Google Scholar 

  12. Feng X, Qin B, Liu T (2018) A language-independent neural network for event detection. Sci Chin Inf Sci 61(9):092,106

    Article  Google Scholar 

  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 363–370

  14. Goller C, Kuchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. Neural Netw 1:347–352

    Google Scholar 

  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. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neur Comput 9(8):1735–1780

    Article  Google Scholar 

  17. Huang Z, Xu W, Yu K (2015) Bidirectional lstm-crf models for sequence tagging. arXiv:1508.01991

  18. Huffman S (1996) Connectionist, statistical, and symbolic approaches to learning for natural language processing chapter learning information extraction patterns from examples

  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 1–7

  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 1101–1106

  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 698–707

  22. Laerty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML

  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 1378

  24. Ma X, Hovy E (2016) End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv:1603.01354

  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 188–191

  26. McCallum A, Freitag D, Pereira FC (2000) Maximum entropy Markov models for information extraction and segmentation. In: Icml, vol 17, pp 591–598

  27. Mooney RJ, Bunescu RC (2006) Subsequence kernels for relation extraction. In: Advances in neural information processing systems, pp 171–178

  28. Ramshaw LA, Marcus MP (1999) Text chunking using transformation-based learning. In: Natural language processing using very large corpora. Springer, pp 157–176

  29. Richman AE, Schone P (2008) Mining wiki resources for multilingual named entity recognition. In: ACL, pp 1–9

  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

  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 corpora

  32. Yan W, Zhang B, Ma S, Yang Z (2017) A novel regularized concept factorization for document clustering. Knowl-Based Syst 135:147–158

    Article  Google Scholar 

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  1. Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, LR11ES14, Tunisia

    Samir Elloumi

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  1. Samir Elloumi
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Elloumi, S. An adaptive model for sequential labeling systems. Multimed Tools Appl 78, 22183–22197 (2019). https://doi.org/10.1007/s11042-019-7558-8

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