Abstract
Chat conversations between more than two participants are often used in Computer Supported Collaborative Learning (CSCL) scenarios because they enhance collaborative knowledge sharing and sustain creativity. However, multi-participant chats are more difficult to follow and analyze due to the complex ways in which different discussion threads and topics can interact. This paper introduces a novel method based on neural networks for detecting implicit links that uses features computed with string kernels and word embeddings. In contrast to previous experiments with an accuracy of 33%, we obtained a considerable increase to 44% for the same dataset. Our method represents an alternative to more complex deep neural networks that cannot be properly used due to overfitting on limited training data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Holmer, T., Kienle, A., Wessner, M.: Explicit referencing in learning chats: needs and acceptance. In: Nejdl, W., Tochtermann, K. (eds.) EC-TEL 2006. LNCS, vol. 4227, pp. 170–184. Springer, Heidelberg (2006). https://doi.org/10.1007/11876663_15
Manning, C.D., Schtze, H.: Foundations of statistical Natural Language Processing. MIT Press, Cambridge (1999)
Masala, M., Ruseti, S., Rebedea, T.: Sentence selection with neural networks using string kernels. Procedia Comput. Sci. 112, 1774–1782 (2017)
Gutu, G., Dascalu, M., Rebedea, T., Trausan-Matu, S.: Time and semantic similarity what is the best alternative to capture implicit links in cscl conversations? In: 12th International Conference on Computer-Supported Collaborative Learning (CSCL 2017), p. 223230. ISLS (2017)
Gutu, G., Dascalu, M., Ruseti, S., Rebedea, T., Trausan-Matu, S.: Unlocking the power of word2vec for identifying implicit links. In: 17th IEEE International Conference on Advanced Learning Technologies (ICALT2017), p. 199200. IEEE (2017)
Trausan-Matu, S., Rebedea, T.: A polyphonic model and system for inter-animation analysis in chat conversations with multiple participants. In: Gelbukh, A. (ed.) CICLing 2010. LNCS, vol. 6008, pp. 354–363. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12116-6_29
Elsner, M., Charniak, E.: Disentangling chat. Comput. Linguist. 36(3), 389–409 (2010)
Mehri, S., Carenini, G.: Chat disentanglement: identifying semantic reply relationships with random forests and recurrent neural networks. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 615–623 (2017)
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. J. Mach. Learn. Res. 2, 419–444 (2002)
Ionescu, R.T., Popescu, M., Cahill, A.: Can characters reveal your native language? a language-independent approach to native language identification. In: EMNLP, pp. 1363–1373 (2014)
Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)
Beck, D., Cohn, T.: Learning kernels over strings using gaussian processes. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), vol. 2, pp. 67–73 (2017)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp. 2042–2050 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162
Acknowledgements
This research was partially supported by the following projects: FP7-212578 LTfLL, EC H2020-644187 Realising an Applied Gaming Eco-system (RAGE), and POC-2015 P39-287 IAVPLN.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Masala, M., Ruseti, S., Gutu-Robu, G., Rebedea, T., Dascalu, M., Trausan-Matu, S. (2018). Identifying Implicit Links in CSCL Chats Using String Kernels and Neural Networks. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-93846-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93845-5
Online ISBN: 978-3-319-93846-2
eBook Packages: Computer ScienceComputer Science (R0)