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International Conference on Human-Computer Interaction

HCII 2021: Adaptive Instructional Systems. Design and Evaluation pp 334–345Cite as

Croatian POS Tagger as a Prerequisite for Knowledge Extraction in Intelligent Tutoring Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12792))

Abstract

In this article we present an knowledge extraction approach that can be used in systems that implement teaching in a fully automated manner. These systems are called Intelligent Tutoring Systems (ITS) and are conceived around the idea of one-to-one teaching. Many such systems use natural language processing to improve the communication interface between student and the system. These techniques can be also used on the content creator side to semi-automate or fully automate the task of teaching content creation. In such systems the knowledge representation plays a crucial role to successfully implement teaching and encourage learning. The output of the knowledge extraction phase is a knowledge in the form of a hyper graph that can be used for adaption to the students current knowledge level. We present a deep neural network architecture for precise POS tagging of words written in languages that are morphologically rich. Using sparse representations for words in this task increases the vector space and makes learning more complex. This problem can be solved to some extent by using traditional vector representations but there is also the problem with representing words that are ambiguous. Proposed architecture uses a Bidirectional Encoder Representations from Transformers (BERT) model that is pre-trained on Croatian language to achieve state-of-the-art accuracy for POS tagging.

The paper is part of the work supported by the Office of Naval Research Grant No. N00014-20-1-2066.

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References

  1. Graesser, A.C., VanLehn, K., Rose, C.P., Jordan, P.W., Harter, D.: Intelligent tutoring systems with conversational dialogue. AI Mag. 22(4), 39 (2001). https://doi.org/10.1609/aimag.v22i4.1591

    Article  Google Scholar 

  2. Graesser, A.C., Chipman, P., Haynes, B.C., Olney, A.: AutoTutor: an intelligent tutoring system with mixed-initiative dialogue. IEEE Trans. Educ. 48(4), 612–618 (2005). https://doi.org/10.1109/TE.2005.856149

    Article  Google Scholar 

  3. Žitko, B.: Model inteligentnog tutorskog sustava zasnovan na obradi kontroliranog jezika nad ontologijom, Fakultet elektrotehnike i računarstva, Zagreb (2010)

    Google Scholar 

  4. Menezes, T., Roth, C.: Semantic hypergraphs, CoRR, vol. abs/1908.10784 (2019). http://arxiv.org/abs/1908.10784

  5. Erjavec, T.: "MULTEXT-East: morphosyntactic resources for Central and Eastern European languages. Lang. Resour. Eval. 46, 131–142 (2012). https://doi.org/10.1007/s10579-011-9174-8. LNCS, vol. 9999, pp. 1–13

    Article  Google Scholar 

  6. Ljubešić, N., Agić, Ž., Klubička, F., Batanović, V., Erjavec, T.: Training corpus hr500k 1.0. In: Slovenian Language Resource Repository CLARIN.SI (2018). http://hdl.handle.net/11356/1183

  7. Charniak, E.: Statistical techniques for natural language parsing. AI Mag. 18(4) (1997). https://doi.org/10.1609/aimag.v18i4.1320

  8. DeRose, S.J.: Grammatical category disambiguation by statistical optimization. In: Computational Linguistics, vol. 14, pp. 31–39 (1988). https://www.aclweb.org/anthology/J88-1003

  9. Giménez, J., Màrquez, L.: SVMTool: a general POS tagger generator based on Support Vector Machines. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (2004)

    Google Scholar 

  10. Spoustoví, D., Hajič, J., Raab, J., Spousta, M.: Semi-supervised training for the averaged perceptron POS tagger. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009), pp. 763–771 (2009)

    Google Scholar 

  11. Agić, Ž., Ljubešić, N., Merkler, D.: Lemmatization and morphosyntactic tagging of Croatian and Serbian. In: Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing (BSNLP 2013), Sofia, Bulgaria, pp. 48–57. Association for Computational Linguistics (2013)

    Google Scholar 

  12. Ljubešić, N., Klubička, F., Agić, Ž., Jazbec, I.: New inflectional lexicons and training corpora for improved morphosyntactic annotation of Croatian and Serbian. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 4264–4270. European Language Resources Association (ELRA). https://www.aclweb.org/anthology/L16-1676

  13. Ljubešić, N.: The CLASSLA-StanfordNLP model for lemmatisation of standard Croatian 1.1, Slovenian language resource repository CLARIN.SI (2020). http://hdl.handle.net/11356/1287

  14. Jacob, D., Ming-Wei, C., Kenton, L., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), vol. 1, pp. 4171–4186 (2019). https://doi.org/10.18653/v1/N19-1423, https://www.aclweb.org/anthology/N19-1423

  15. Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), vol. 1, pp. 2227–2237. https://doi.org/10.18653/v1/N18-1202, https://www.aclweb.org/anthology/N18-1202

  16. Ulčar, M., Robnik-Šikonja, M.: High Quality ELMo embeddings for seven less-resourced languages. In: Proceedings of the 12th Language Resources and Evaluation Conference (2020). https://www.aclweb.org/anthology/2020.lrec-1.582

  17. Ulčar, M., Robnik-Šikonja, M.: FinEst BERT and CroSloEngual BERT. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds.) TSD 2020. LNCS (LNAI), vol. 12284, pp. 104–111. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58323-1_11

    Chapter  Google Scholar 

  18. Zarita, Z., Ong, P.: Function approximation using artificial neural networks, pp. 140–145. World Scientific and Engineering Academy and Society (WSEAS) (2007). https://doi.org/10.5555/1376368.1376392

  19. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9 (1998). https://doi.org/10.1162/neco.1997.9.8.1735

  20. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014). https://doi.org/10.3115/v1/D14-1179

  21. Svenstrup, D., Hansen, J., Winther, O.: Hash embeddings for efficient word representations. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4935–4943 (2017). https://doi.org/10.5555/3295222.3295246

  22. Goodfellow, I., Warde-Farley, D., Mehdi M., Courville, A., Bengio, Y.: Maxout networks. In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, vol. 28, pp. 1319–1327 (2013)

    Google Scholar 

  23. spaCy - Industrial-strength Natural Language Processing in Python. https://nightly.spacy.io/. Accessed 14 Jan 2021

  24. Ljubešić, N.: Word embeddings CLARIN.SI-embed.hr 1.0, Slovenian language resource repository CLARIN.SI (2018). http://hdl.handle.net/11356/1205

  25. Dozat, T., Manning, C.: Simpler but more accurate semantic dependency parsing. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 484–490 (2018). https://doi.org/10.18653/v1/P18-2077

  26. Ruder, S.: An overview of gradient descent optimization algorithms, CoRR, vol. abs/1609.04747 (2016)

    Google Scholar 

  27. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings 3rd International Conference on Learning Representations, ICLR 2015 (2015). http://arxiv.org/abs/1412.6980

  28. Vasić, D., Žitko, B., Gašpar, A., Ljubešić, N., Štrkalj Despot, K., Merkler, D.: Semantic hypergraph corpus SemCRO 1.0, Slovenian language resource repository CLARIN.SI (2020). http://hdl.handle.net/11356/1377

  29. Baisa, V., Kovář, V.: Information extraction for Czech based on syntactic analysis. In: Vetulani, Z., Mariani, J. (eds.) LTC 2011. LNCS (LNAI), vol. 8387, pp. 155–165. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08958-4_13

    Chapter  Google Scholar 

  30. Straková, J., Pecina, P.: Czech information retrieval with syntax-based language models. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010) (2010)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Science and Education, University of Mostar, 88000, Mostar, Bosnia and Herzegovina

    Daniel Vasić

  2. Faculty of Science, University of Split, 21000, Split, Croatia

    Branko Žitko, Ani Grubišić, Slavomir Stankov, Ivan Peraić & Matea Markić-Vučić

  3. Catholic Faculty of Theology, University of Split, 21000, Split, Croatia

    Angelina Gašpar, Ines Šarić-Grgić & Suzana Tomaš

  4. Faculty of Humanities and Social Sciences, University of Split, 21000, Split, Croatia

    Daniel Vasić, Branko Žitko, Ani Grubišić, Slavomir Stankov, Angelina Gašpar, Ines Šarić-Grgić, Suzana Tomaš & Matea Markić-Vučić

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  1. Daniel Vasić
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  2. Branko Žitko
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  4. Slavomir Stankov
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  5. Angelina Gašpar
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  6. Ines Šarić-Grgić
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  7. Suzana Tomaš
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  9. Matea Markić-Vučić
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Correspondence to Daniel Vasić .

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Editors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Vasić, D. et al. (2021). Croatian POS Tagger as a Prerequisite for Knowledge Extraction in Intelligent Tutoring Systems. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Design and Evaluation. HCII 2021. Lecture Notes in Computer Science(), vol 12792. Springer, Cham. https://doi.org/10.1007/978-3-030-77857-6_23

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  • DOI: https://doi.org/10.1007/978-3-030-77857-6_23

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  • Online ISBN: 978-3-030-77857-6

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