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Argumentation Mining in Parliamentary Discourse

  • Nona NaderiEmail author
  • Graeme Hirst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9935)

Abstract

We examine whether using frame choices in forum statements can help us identify framing strategies in parliamentary discourse. In this analysis, we show how features based on embedding representations can improve the discovery of various frames in argumentative political speech. Given the complex nature of the parliamentary discourse, the initial results that are presented here are promising. We further present a manually annotated corpus for frame recognition in parliamentary discourse.

Notes

Acknowledgements

This work is supported by the Natural Sciences and Engineering Research Council of Canada and by the Social Sciences and Humanities Research Council. We thank Patricia Araujo Thaine, Krish Perumal, and Sara Scharf for their contributions to the annotation of parliamentary statements, and Tong Wang for sharing the syntactic embeddings. We also thank Tong Wang and Ryan Kiros for fruitful discussions, and Christopher Cochrane for insightful comments.

References

  1. 1.
    Boltužić, F., Šnajder, J.: Back up your stance: recognizing arguments in online discussions. In: Proceedings of the First Workshop on Argumentation Mining, pp. 49–58 (2014)Google Scholar
  2. 2.
    Boltužić, F., Šnajder, J.: Identifying prominent arguments in online debates using semantic textual similarity. In: Proceedings of the 2nd Workshop on Argumentation Mining, pp. 110–115. Association for Computational Linguistics, Denver, June 2015Google Scholar
  3. 3.
    Cabrio, E., Villata, S.: Combining textual entailment and argumentation theory for supporting online debates interactions. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, pp. 208–212. Association for Computational Linguistics (2012)Google Scholar
  4. 4.
    De Marneffe, M.C., Manning, C.D.: The stanford typed dependencies representation. In: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, COLING 2008, pp. 1–8. Association for Computational Linguistics (2008)Google Scholar
  5. 5.
    Entman, R.M.: Framing: toward clarification of a fractured paradigm. J. Commun. 43(4), 51–58 (1993)CrossRefGoogle Scholar
  6. 6.
    Hasan, K.S., Ng, V.: Why are you taking this stance? Identifying and classifying reasons in ideological debates. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 751–762. Association for Computational Linguistics, Doha, October 2014Google Scholar
  7. 7.
    Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R.S., Torralba, A., Urtasun, R., Fidler, S.: Skip-thought vectors. arXiv preprint arXiv:1506.06726 (2015)
  8. 8.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  9. 9.
    Misra, A., Anand, P., Tree, J.E.F., Walker, M.A.: Using summarization to discover argument facets in online idealogical [sic] dialog. In: The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015, Denver, Colorado, USA, May 31–June 5, 2015, pp. 430–440 (2015)Google Scholar
  10. 10.
    Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: Association for Computational Linguistics, pp. 236–244 (2008)Google Scholar
  11. 11.
    Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 151–161. Association for Computational Linguistics (2011)Google Scholar
  12. 12.
    Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)
  13. 13.
    Wang, T., Mohamed, A., Hirst, G.: Learning lexical embeddings with syntactic and lexicographic knowledge. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 2: Short Papers), pp. 458–463. Association for Computational Linguistics, Beijing, July 2015Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of TorontoTorontoCanada

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