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Explainable and Transferrable Text Categorization

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Data Management Technologies and Applications (DATA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1255))

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Abstract

Automated argument stance (pro/contra) detection is a challenging text categorization problem, especially if said arguments are to be detected for new topics. In previous research, we designed and evaluated an explainable machine learning based classifier. It was capable to achieve 96% F1 for argument stance recognition within the same topic and 60% F1 for previously unseen topics, which informed our hypothesis, that there are two sets of features in argument stance recognition: General features and topic specific features. An advantage of the described system is its quick transferability to new problems. Besides providing further details about the developed C3 TFIDF-SVM classifier, we investigate the classifiers effectiveness for different text categorization problems spanning two natural languages. Besides the quick transferability, the generation of human readable explanations about why specific results were achieved is a key feature of the described approach. We further investigate the generated explanation understandability and conduct a survey about how understandable the classifier’s explanations are.

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References

  1. US National Library of Medicine National Institutes of Health pubmed.gov. https://www.ncbi.nlm.nih.gov/pubmed/. Accessed 17 Sep 2019

  2. European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance); OJ L, 4 May, 2016, vol. 119, pp. 1–88 (2016)

    Google Scholar 

  3. Clos, J., Wiratunga, N., Massie, S.: Towards explainable text classification by jointly learning lexicon and modifier terms. In: IJCAI-17 Workshop on Explainable AI (XAI) (2017)

    Google Scholar 

  4. Lippi, M., Torroni, P.: Argument mining: a machine learning perspective. In: Black, E., Modgil, S., Oren, N. (eds.) TAFA 2015. LNCS (LNAI), vol. 9524, pp. 163–176. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-28460-6_10

    Chapter  Google Scholar 

  5. Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 31–41 (2016)

    Google Scholar 

  6. Eljasik-Swoboda, T., Engel, F., Hemmje, M.: Using topic specific features for argument stance recognition. In: Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019), pp. 13–22 (2019). ISBN:978-989-758-377-3

    Google Scholar 

  7. Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. Argument. Soc. Media 17, 1–23 (2016)

    Google Scholar 

  8. Stab, C., Miller, T., Schiller, B., Rai, P., Gurevych, I.: Cross-topic argument mining from heterogeneous sources. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2018) (2018)

    Google Scholar 

  9. Same Side Stance Classification. https://sameside.webis.de/. Accessed 24 Sep 2019

  10. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)

    Article  Google Scholar 

  11. Bader, S., Hitzler, P.: Dimensions of neural-symbolic integration – a structured survey. arXiv preprint arXiv:cs/0511042 (2005)

  12. Swoboda, T., Kaufmann, M., Hemmje, M.: Toward cloud-based classification and annotation support. In: Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016), vol. 2, pp. 131–237 (2016)

    Google Scholar 

  13. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  14. Helbig, H., Scherer, A.: Kurs 1830: Neuronale Netze. University of Hagen, Germany (2011)

    Google Scholar 

  15. Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning – a new frontier in artificial intelligence research. In: IEEE Computational Intelligence Magazine, USA, November issue, pp. 13–18 (2010)

    Google Scholar 

  16. Vapnik, V.N., Chervonenkis, A.Y.: On a class of algorithms of learning pattern recognition. Framework of the Generalised Portrait Method, Oб oднoм клacce aлгopитмoв oбyчeния pacпoзнaвaнию oбpaзoв, Aвтoмaтикa и тeлeмexaникa (1964)

    Google Scholar 

  17. Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

  18. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representation in vector space. In: Proceedings of Workshop at ICLR (2013)

    Google Scholar 

  19. Pennington, J., Socher, R., Manning, C.: GloBe: global vectors for word representation. In: Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)

    Google Scholar 

  20. Zanzotto, F.M., Korkontzelos, I., Fallucchi, F., Manandhar, S.: Estimating linear models for compositional distributed semantics. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 1263–1271 (2010)

    Google Scholar 

  21. Kusner, M.J., Sun, Y., Kolkin, N., Weinberger, K.Q.: From word embeddings to document distances. In: Proceedings of the 32nd International Conference on Machine Learning (2015)

    Google Scholar 

  22. Dai, X., Bikdash, M., Meyer, M.: From social media to public health surveillance: word embedding based clustering method for twitter classification. In: Proceedings of SoutheastCon, pp. 1–7 (2017). https://doi.org/10.1109/secon.2017.7925400

  23. Eljasik-Swoboda, T., Kaufmann, M., Hemmje, M.: No target function classifier – fast unsupervised text categorization using semantic spaces. In: Proceedings of the 7th International Conference on Data Science, Technology and Applications (DATA 2018), pp. 35–46 (2018)

    Google Scholar 

  24. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805v2 (2019)

  25. Wolff, E.: Microservices – Flexible Software Architecture. Pearson Education, USA (2017)

    Google Scholar 

  26. Dropwizard: Production-ready, out of the box. https://dropwizard.io. Accessed 12 Sep 2019

  27. Enterprise Container Platform | Docker. https://www.docker.com/. Accessed 30 Sep 2019

  28. Peldszus, A.: An annotated corpus of argumentative microtexts. https://github.com/peldszus/arg-microtexts. Accessed 15 Mar 2019

  29. Wiegand, M., Siegel, M., Ruppenhofer, J.: Overview of the GermEval 2018 shared task on the identification of offensive language. In: Proceedings of the GermEval, Vienna, Austria (2018)

    Google Scholar 

  30. Coucke, A., et al.: Snipts voice platform, an embedded spoken language understanding system for private-by-design voice interfaces. arXiv:1805.10190 (2018)

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Acknowledgements

This work has been funded by the Deutsche Forschungsgemeinschaft (DFG) within the project Empfehlungsrationalisierung, Grant Number 376059226, as part of the Priority Program “Robust Argumentation Machines (RATIO)” (SPP-1999).

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Hagen, Hagen, Germany

    Tobias Eljasik-Swoboda

  2. FTK e.V. Forschungsinstitut für Telekommunikation und Kooperation, Dortmund, Germany

    Felix Engel & Matthias Hemmje

Authors
  1. Tobias Eljasik-Swoboda
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  2. Felix Engel
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  3. Matthias Hemmje
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Corresponding author

Correspondence to Tobias Eljasik-Swoboda .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Hochschule Niederrhein, University of Applied Sciences, Krefeld, Nordrhein-Westfalen, Germany

    Christoph Quix

  3. Centre for Informatics and Systems, University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

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Eljasik-Swoboda, T., Engel, F., Hemmje, M. (2020). Explainable and Transferrable Text Categorization. In: Hammoudi, S., Quix, C., Bernardino, J. (eds) Data Management Technologies and Applications. DATA 2019. Communications in Computer and Information Science, vol 1255. Springer, Cham. https://doi.org/10.1007/978-3-030-54595-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-54595-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-54594-9

  • Online ISBN: 978-3-030-54595-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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