Abstract
This article deals with the principles of automatic label assignment for e-hypertext markup. We’ve identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label’s triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms’ results: at the first stage, two experts assessed the triplet’s relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0» – a label doesn’t match a topic; «1» – a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.
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
References
Nelson, T.: Literary Machines. Mindful Press, Sausalito (1993)
Salmerón, L., Kintsch, W., Cañas, J.J.: Reading strategies and prior knowledge in learning from hypertext. Mem. Cognit. 34, 1157–1171 (2006)
Vandendorpe, C.: From Papyrus to Hypertext: Toward the Universal Digital Library (Topics in the Digital Humanities). University of Illinois Press (2009)
Shulginov, V.A., Shulginov, V.A., Mitrofanova, O.A.: Topic organization of e-hypertext media: corpus driven research. In: R. Piotrowski’s Readings in Language Engineering and Applied Linguistics (PRLEAL 2019), CEUR Workshop Proceedings, vol. 2552, pp. 299–312 (2019)
BeautifulSoup. https://pypi.org/project/beautifulsoup4/. Accessed 08 July 2020
NLTK. https://www.nltk.org/. Accessed 08 July 2020
re. https://github.com/python/cpython/blob/3.8/Lib/re.py. Accessed 08 July 2020
genism. https://radimrehurek.com/gensim. Accessed 08 July 2020
t-Distributed Stochastic Neighbor Embedding. https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE. Accessed 08 July 2020
DBSCAN clustering. https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html. Accessed 08 July 2020
Non-negative matrix factorization. https://radimrehurek.com/gensim/models/nmf.html. Accessed 08 July 2020
Lau, J.H., Newman, D., Karimi, S., Baldwin, T.: Best topic word selection for topic labelling. In: COLING’10 Proceedings of the 23rd International Conference on Computational Linguistics, Stroudsburg, PA, Association for Computational Linguistics, pp. 605–613 (2010)
Aletras, N., Stevenson, M., Court, R.: Labelling topics using unsupervised graph-based methods. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, ACL, pp. 631–636 (2014)
Mei, Q., Shen, X., Zhai, C.: Automatic labeling of multinomial topic models. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2007. pp. 490–499. ACM Press, New York (2007)
Cano Basave, A.E., He, Y., Xu, R.: Automatic labelling of topic models learned from twitter by summarisation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Stroudsburg, PA, USA, Association for Computational Linguistics, pp. 618–624 (2014)
Kou, W., Li, F., Baldwin, T.: Automatic labelling of topic models using word vectors and letter trigram vectors. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds.) AIRS 2015. LNCS, vol. 9460, pp. 253–264. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-28940-3_20
Nolasco, D., Oliveira, J.: Detecting knowledge innovation through automatic topic labeling on scholar data. In: 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, pp. 358–367. IEEE Computer Society (2016)
Magatti, D., Calegari, S., Ciucci, D., Stella, F.: Automatic labeling of topics. In: ISDA 2009 9th International Conference on Intelligent Systems Design and Applications, Pisa, pp. 1227–1232. IEEE (2009)
Lau, J.H., Grieser, K., Newman, D., Baldwin, T.: Automatic labelling of topic models. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA, vol. 1, pp. 1536–1545. Association for Computational Linguistics (2011)
Hulpus, I., Hayes, C., Karnstedt, M., Greene, D.: Unsupervised graph-based topic labelling using DBpedia. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining WSDM 2013, pp. 465–474 (2013)
Bhatia, S., Lau, J.H., Baldwin, T.: Automatic labelling of topics with neural embeddings. In: 26th COLING International Conference on Computational Linguistics, 2016, pp. 953–963 (2016)
Allahyari, M., Pouriyeh, S., Kochut, K., Arabnia, H.R.: A knowledge-based topic modeling approach for automatic topic labeling. Int. J. Adv. Comput. Sci. Appl. 8(9), 335–349 (2017)
Mao, X., Hao, Y.-J., Zhou, Q., Yuan, W., Yang, L., Huang, H.: A novel fast framework for topic labeling based on similarity-preserved hashing. In: COLING 2016, pp. 3339–3348 (2016)
Kriukova, A., Erofeeva, A., Mitrofanova, O., Sukharev, K.: Explicit semantic analysis as a means for topic labelling. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds.) AINL 2018. CCIS, vol. 930, pp. 110–116. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01204-5_11
Mirzagitova, A., Mitrofanova, O.: Automatic assignment of labels in topic modelling for Russian corpora. In: Proceedings of 7th Tutorial and Research Workshop on Experimental Linguistics, ExLing 2016/A. Botinis, ed. Saint Petersburg: International Speech Communication Association, 2016, pp. 115–118 (2016)
Erofeeva, A., Mitrofanova, O.: Automatic Topic label assignment in topic models for russian text corpora. In: Structural and Applied Linguistics, Saint-Petersburg, vol. 12, pp. 122−147 (2019). (in Russian)
Kriukova, A., Mitrofanova, O., Sukharev, K.: Measuring semantic relatedness of russian texts by means of explicit semantic analysis. In: Kalinichenko, L., Manolopoulos, Y., Stupnikov, S., Skvortsov, N., Sukhomlin, V. (eds.) Data Analytics and Management in Data Intensive Domains: XX International Conference DAMDID/RCDL’2018 (October 9–12, 2018, Moscow, Russia): Conference Proceedings /, pp. 284–288. FRC CSC RAS, Moscow (2018)
Kriukova, A., Mitrofanova, O., Sukharev, K., Roschina, N.: Using explicit semantic analysis and Word2Vec in measuring semantic relatedness of russian paraphrases. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O. (eds.) DTGS 2018. CCIS, vol. 859, pp. 350–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02846-6_28
Scikit-learn. https://scikit-learn.org/stable/. Accessed 08 July 2020
Korobov, M.: Morphological analyzer and generator for Russian and Ukrainian languages. In: Khachay, M.Y., Konstantinova, N., Panchenko, A., Ignatov, D.I., Labunets, V.G. (eds.) AIST 2015. CCIS, vol. 542, pp. 320–332. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26123-2_31
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1606–1611 (2007)
RIA News. https://ria.ru/20200115/1563456719.html. Accessed 08 July 2020
Acknowledgements
The reported study was funded by RFBR according to the research project № 18-312-00010. The authors express their deep gratitude to Aliia Erofeeva (CCG.ai, Cambridge, UK) and Kirill Sukharev (ETU «LETI», Saint-Petersburg, Russia) for their help in the development of topic labelling software.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mitrofanova, O., Kriukova, A., Shulginov, V., Shulginov, V. (2021). E-hypertext Media Topic Model with Automatic Label Assignment. In: van der Aalst, W.M.P., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2020. Communications in Computer and Information Science, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-71214-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-71214-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71213-6
Online ISBN: 978-3-030-71214-3
eBook Packages: Computer ScienceComputer Science (R0)