Abstract
As chatbots, driverless cars and other robot-like applications become a part of everyday life, we are witnessing an increase in the popularization of Artificial Intelligence (AI) by the mass media. While this has some potential in terms of informing the public about technological development, it also makes the term a buzzword not pointing to any actual object with no agreed-upon meaning. AI is usually deployed as an umbrella term for sealing a variety of analytical tools such as intelligent decision support systems, deep learning, and computational linguistics disregarding their actual denotations. As the popular discourse and media represent its mundane features to connote miracles or apocalypses, AI gains a mythical status that can have different significations according to different cultural contexts. Our aim in this paper is to study the semantic shifts in the meaning of AI in different contexts by examining the mapping of the words to different semantic vector spaces over time.
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
Schutze, H., Hull, D.A., Pedersen, J.O.: A comparison of classifiers and document representations for the routing problem. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1995), pp. 229–237 (1995)
Lewis, D.D., Ringuette, M.: A comparison of two learning algorithms for text categorization. In: Symposium on Document Analysis and Information Retrieval, pp. 81–83 (1994)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, London (2008)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Gulordava, K., Baroni, M.: A distributional similarity approach to the detection of semantic change in the Google Books Ngram corpus. In: Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics (GEMS 2011). pp. 67–71 (2011)
Kulkarni, V., Al-Rfou’, R., Perozzi, B., Skiena, S.: Statistically Significant Detection of Linguistic Change. CoRR abs/1411.3315 (2014)
Kenter, T., Wevers, M., Huijnen, P., de Rijke, M.: Ad Hoc monitoring of vocabulary shifts over time. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1191–1200 (2015)
Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: ACL2016, pp. 1489–1501 (2016)
Jatowt, A., Duh, K.: A framework for analyzing semantic change of words across time. In: JCDL, pp. 229–238. IEEE Computer Society (2014)
Azarbonyad, H., Dehghani, M., Beelen, K., Arkut, A., Marx, M., Kamps, J.: Words are malleable: computing semantic shifts in political and media discourse. In: CIKM 2017, pp. 1509–1518 (2017)
Salton, G.: The SMART retrieval system-experiments in automatic document processing. Prentice-Hall Inc., Upper Saddle River, NJ, USA (1971)
Deerwester, S., Dumais, S., Furnas, G., Landauer, T., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, pp. 391–407 (1990)
Jurafsky, D., James H.M.: Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edn. Prentice-Hall (2009)
Jolliffe, I.T.: Principal Component Analysis, Springer, Berlin (2002). https://doi.org/10.1007/978-3-642-27497-8_5
Stephen, R.B., Jonathan, M.C.: The role of factor analysis in the development and evaluation of personality scales. J. Personal. 54, 106–148 (2007)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 1(5), 135–46 (2017)
Aghabozorgi, S., Shirkhorshidi, A.S.: Time-series clustering - a decade review. Inf. Syst. 53, 16–38 (2015)
Greenacre, M., Blasius, J.: Correspondence Analysis and its Interpretation. In: Correspondence Analysis in the Social Sciences. Academic Press, London (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Suerdem, A., Dalyan, T., Yıldırım, S. (2023). Detection of Change in the Senses of AI in Popular Discourse. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13397. Springer, Cham. https://doi.org/10.1007/978-3-031-23804-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-23804-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23803-1
Online ISBN: 978-3-031-23804-8
eBook Packages: Computer ScienceComputer Science (R0)