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Generation and Semantic Expansion of Impacts in Arts and Culture

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Advances in Information and Communication (FICC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 438))

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Abstract

The paper aims to offer a solution for the identification and annotation of impacts in the domain of arts and culture. We explore available (ex post) narratives of impactful interventions in society, such as those contained in the body of scientific papers dealing with related topics to arts and culture, and try to disentangle some meaningful descriptions of impact generation mechanisms, using NLP (Natural Language Processing) techniques based on semantic similarity principles. The typology of texts analysed so far are academic papers from peer reviewed journals being focused on the societal impacts of cultural policies and practices. However, the method easily lends itself to being extended to pilot studies and policy documents. Three main categories of societal impact - borrowed from the New European Agenda for Culture - have been considered: impacts on personal well-being, on social cohesion and on urban renovation. Based on prior literature findings, a collection of possible societal impacts was gathered in the form of 100 phrases of two up to eight words. Then we expanded the semantic neighbourhood of each impact utilising continuous space word representations by cosine similarity measures. We show that impacts can be clustered into well separated and defined groups of related concepts. This can be interpreted in two ways: first, the European Agenda points at three, largely independent, impact areas for cultural interventions, with little overlaps to one another; second, little is left out of these categories, which could still be considered as a separate impact area. Finally, we show that proposed procedure can be successfully applied to the task of automatic annotation of documents in the domain of arts and culture.

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References

  1. arvkevi/kneed. GitHub (2017). https://github.com/arvkevi/kneed. Accessed 13 Jul 2021

  2. Babić, K., Guerra, F., Martinčić-Ipšić, S., Meštrović, A.: A comparison of approaches for measuring the semantic similarity of short texts based on word embeddings. J. Inf. Organ. Sci. 44(2), 231–246 (2020)

    Google Scholar 

  3. Bicalho, P., Pita, M., Pedrosa, G., Lacerda, A., Pappa, G.L.: A general framework to expand short text for topic modeling. Inf. Sci. 393, 66–81 (2017). https://www.sciencedirect.com/science/article/abs/pii/S0020025517304206?via%3Dihub

  4. Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python, 1st edn. O’Reilly Media Inc., Sebastopol (2009)

    MATH  Google Scholar 

  5. 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 (2015)

    Google Scholar 

  6. Boyack, K., Small, H., Klavans, R.: Improving the accuracy of co-citation clustering using full text. J. Am. Soc. Inform. Sci. Technol. 64, 1759–1767 (2013)

    Article  Google Scholar 

  7. Budanitsky, A., Hirst, G.: Evaluating Wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)

    Article  Google Scholar 

  8. Chandrasekaran, D., Mago, V.: Evolution of semantic similarity-a survey. ACM Comput. Surv. (CSUR) 54(2), 1–37 (2021)

    Article  Google Scholar 

  9. Colla, D., Mensa, E., Radicioni, D.: Novel metrics for computing semantic similarity with sense embeddings. Knowl.-Based Syst. 206, 106346 (2020)

    Article  Google Scholar 

  10. Concilio, G., Tosoni, I. (eds.): Innovation Capacity and the City: The enabling role of Design. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00123-0. ISBN 978-3-030-00122-3

  11. Council of the European Union: Council conclusions on cultural and creative crossovers to stimulate innovation, economic sustainability and social inclusion. Official Journal of the European Union, C 172, 27 May 2015 (2015)

    Google Scholar 

  12. Council of Europe: Indicator Framework on Culture and Democracy (IFCD) (2013). https://www.coe.int/en/web/culture-and-heritage/indicators-culture-and-democracy

  13. De Boom, C., Canneyt, S.V., Bohez, S., Demeester, T., Dhoedt, B.: Learning semantic similarity for very short texts. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1229–1234. IEEE (2015)

    Google Scholar 

  14. European Commission: Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions. A New European Agenda for Culture. COM/2018/267 final (2018)

    Google Scholar 

  15. European Parliament and Council: Decision No 445/2014/EU of 16 April 2014 establishing a Union action for the European Capitals of Culture for the years 2020 to 2033 and repealing Decision No 1622/2006/EC. Official Journal of the European Union, L 132, 3 May 2014 (2014)

    Google Scholar 

  16. EUROSTAT: The ESS-Net Culture Framework (2012). https://ec.europa.eu/assets/eac/culture/library/reports/ess-net-report_en.pdf

  17. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998). https://doi.org/10.7551/mitpress/7287.001.0001

  18. Florida, R.: Cities and the Creative Class. Routledge, New York (2005). ISBN 0-415-94887-8

    Google Scholar 

  19. Forsberg, F., Gonzalez, P.A.: Unsupervised Machine Learning: An Investigation of Clustering Algorithms on a Small Dataset (2018)

    Google Scholar 

  20. Google’s original C toolkit and word2vec papers. https://code.google.com/archive/p/word2vec/

  21. Grossi, E., Tavano Blessi, G., Sacco, P.L.: Magic moments: determinants of stress relief and subjective wellbeing from visiting a cultural heritage site. Cult. Med. Psychiatry 43(1), 4–24 (2018). https://doi.org/10.1007/s11013-018-9593-8

    Article  Google Scholar 

  22. Gutmann, M., Hyvärinen, A.: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res. 13, 307–361 (2012)

    MathSciNet  MATH  Google Scholar 

  23. Hu, K., et al.: A domain keyword analysis approach extending term frequency-keyword active index with google Word2Vec model. Scientometrics 114(3), 1031–1068 (2017). https://doi.org/10.1007/s11192-017-2574-9

    Article  Google Scholar 

  24. Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall PTR, Hoboken (2000)

    Google Scholar 

  25. Kenter, T., De Rijke, M.: Short text similarity with word embeddings. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1411–1420. Association for Computing Machinery (2015)

    Google Scholar 

  26. Landry, C.: The Creative City: A Toolkit for Urban Innovators. Earthscan Publications, Ltd., London (2000)

    Google Scholar 

  27. Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the ICML 2014 Proceedings of the 31st International Conference on International Conference on Machine Learning, vol. 14, pp. 1188–1196 (2014)

    Google Scholar 

  28. Liang, H., Fothergill, R., Baldwin, T: RoseMerry: a baseline message-level sentiment classification system. In: Cer, M.D., Jurgens, D., Nakon, P., Zesch, T. (eds) Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2015, pp. 551–555. The Association for Computer Linguistics (2015)

    Google Scholar 

  29. Lilleberg, J.; Zhu, Y.; Zhang, Y.: Support vector machines and Word2vec for text classification with semantic features. In: Proceedings of the 2015 IEEE 14th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), pp. 136–140 (2015). https://ieeexplore.ieee.org/document/7259377/

  30. Ma, L., Zhang, Y.: Using Word2Vec to process big text data. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2895–2897. IEEE (2015)

    Google Scholar 

  31. MacKay, D.: Information theory, inference, and learning algorithms. IEEE Trans. Inf. Theory 50, 2544–2545 (2004)

    Article  Google Scholar 

  32. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California 1965/66, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  33. Martinčić-Ipšić S., Miličić T., Todorovski L.: The influence of feature representation of text on the performance of document classification. Appl. Sci. 9(4), 743 (2019). https://doi.org/10.3390/app9040743

  34. Matarasso, F.: Use or Ornament? The social impact of participation in the arts. Comedia (1997)

    Google Scholar 

  35. McKie J: PyMuPDF - the Python bindings for MuPDF (2021). https://buildmedia.readthedocs.org/media/pdf/pymupdf/latest/pymupdf.pdf

  36. Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of the 21st National Conference on Artificial Intelligence AAAI, vol. 1, pp. 775–780 (2006)

    Google Scholar 

  37. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS, vol. 2, pp. 3111–3119 (2013)

    Google Scholar 

  38. Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751. Association for Computational Linguistics (2013)

    Google Scholar 

  39. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the 1st International Conference on Learning Representations, ICLR, pp. 1–12 (2013)

    Google Scholar 

  40. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  41. Navigli, R., Martelli, F.: An overview of word and sense similarity. Nat. Lang. Eng. 25(6), 693–714 (2019). https://doi.org/10.1017/S1351324919000305

    Article  Google Scholar 

  42. Nguyen, H.T., Duong, P.H., Cambria, E.: Learning short-text semantic similarity with word embeddings and external knowledge sources. Knowl.-Based Syst. 182, 104842 (2019)

    Article  Google Scholar 

  43. Pawson, R., Tilley, N.: Realistic Evaluation. Sage Publications Inc., Thousand Oaks (1997)

    Google Scholar 

  44. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  45. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010)

    Google Scholar 

  46. Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Stab, C., Gurevych, I.: Classification and clustering of arguments with contextualized word embeddings. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 567–578. Association for Computational Linguistics (2019)

    Google Scholar 

  47. Ren, Y., Wang, R., Ji, D.: A topic-enhanced word embedding for Twitter sentiment classification. Inf. Sci. 369, 188–198 (2016)

    Article  Google Scholar 

  48. Rexha, A., Kröll, M., Dragoni, M., Kern, R.: Polarity classification for target phrases in tweets: a Word2Vec approach. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 217–223. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47602-5_40

    Chapter  Google Scholar 

  49. Sacco, P.L.: Health and culture welfare: a new policy perspective. Econ. Cult. 27(2), 165–174 (2017)

    Google Scholar 

  50. Satopaa, V., Albrecht, J.R., Irwin, D.E., Raghavan, B.: Finding a “Kneedle” in a haystack: detecting knee points in system behavior. In: 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 166–171. IEEE Computer Society (2011)

    Google Scholar 

  51. Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959–962. ACM (2015). https://dl.acm.org/doi/10.1145/2766462.2767830

  52. Tapscott, D.: The Digital Economy: Promise and Peril In The Age of Networked Intelligence. McGraw-Hill, New York (1997). ISBN 0-07-063342-8

    Google Scholar 

  53. Center for Theory of Change: What is Theory of Change? (2021). https://www.theoryofchange.org/what-is-theory-of-change/

  54. Toffler, A.: The Third Wave. Bantam Books, New York (1980)

    Google Scholar 

  55. Umargono, E., Suseno, J.E., Gunawan, S.: K-means clustering optimization using the elbow method and early centroid determination based-on mean and median. In: Proceedings of the International Conferences on Information System and Technology - CONRIST, pp. 234–240 (2020). https://doi.org/10.5220/0009908402340240. ISBN 978-989-758-453-4

  56. UNESCO Framework for Cultural Statistics (FCS) (2009). http://uis.unesco.org/sites/default/files/documents/measuring-cultural-participation-2009-unesco-framework-for-cultural-statistics-handbook-2-2012-en.pdf

  57. Wang, S., Koopman, R.: Clustering articles based on semantic similarity. Scientometrics 111(2), 1017–1031 (2017). https://doi.org/10.1007/s11192-017-2298-x

    Article  Google Scholar 

  58. Yoshikawa, Y., Iwata, T., Sawada, H.: Latent support measure machines for bag-of-words data classification. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 1961–1969. Curran Associates Inc., Red Hook (2014)

    Google Scholar 

  59. Yuan, C., Yang, H.: Research on k-value selection method of k-means clustering algorithm. J. 2(2), 226–235 (2019). https://doi.org/10.3390/j2020016

  60. Zhang, L., Li, J., Wang, C.: Automatic synonym extraction using Word2Vec and spectral clustering. In: 2017 36th Chinese Control Conference (CCC), pp. 5629–5632. IEEE (2017)

    Google Scholar 

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Acknowledgment

This work has been supported by the H2020 MESOC (Measuring the Social Dimension of Culture) project - under Grant Agreement No. 870935 - and by the uniri-drustv-18-20 project funded by University of Rijeka.

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Correspondence to Petar Kristijan Bogović .

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Bogović, P.K., Molinari, F., Kovačić, B., Martinčić-Ipšić, S. (2022). Generation and Semantic Expansion of Impacts in Arts and Culture. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-98012-2_8

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