none 10.1075/cilt.356.10kon John Benjamins Publishing Company John Benjamins Publishing Company 1757 131844510 3923719 1419350 202507022007000005 10.1075 2025-07-02T18:09:09Z 2021-11-08T14:37:53Z 1 0304-0763 10.1075/cilt https://benjamins.com/ catalog/cilt Adam Pawłowski University of Wrocław https://ror.org/00yae6e25 https://orcid.org/0000-0002-0804-5698 Jan Mačutek Mathematical Institute of Slovak Academy of Sciences https://ror.org/02fhy7464 Constantine the Philosopher University in Nitra https://ror.org/038dnay05 https://orcid.org/0000-0003-1712-4395 Sheila Embleton York University https://ror.org/05fq50484 https://orcid.org/0000-0001-7245-1845 George Mikros Hamad Bin Khalifa University https://ror.org/03eyq4y97 Data, models, information and applications Specialists in quantitative linguistics the world over have recourse to a solid and universal methodology. These days, their methods and mathematical models must also respond to new communication phenomena and the flood of data produced daily. While various disciplines (computer science, media science) have different ways of processing this onslaught of information, the linguistic approach is arguably the most relevant and effective. This book includes recent results from many renowned contemporary practitioners in the field. Our target audiences are academics, researchers, graduate students, and others involved in linguistics, digital humanities, and applied mathematics. 356 12 22 2021 11 8 2021 9789027210104 9789027258380 John Benjamins Publishing Company Amsterdam 10.1075/cilt.356 http://www.jbe-platform.com/content/books/9789027258380 https://www.jbe-platform.com/deliver/fulltext/9789027258380.pdf https://www.degruyter.com/document/doi/10.1075/cilt.356/html Maxim Konca Goethe University Frankfurt https://ror.org/04cvxnb49 Alexander Mehler Goethe University Frankfurt https://ror.org/04cvxnb49 Daniel Baumartz Goethe University Frankfurt https://ror.org/04cvxnb49 Wahed Hemati Goethe University Frankfurt https://ror.org/04cvxnb49 A quantitative text model for detecting random texts Abstract We present a study of the distinctiveness of random and non-random texts based on text characteristics of quantitative linguistics. We additionally experiment with text features that evaluate contiguity associations among sentences by means of BERT (Bidirectional Encoder Representations from Transformers). To this end, we experiment with generative models for random texts as currently discussed in the context of neural networks. The chapter contributes to the clarification of deficits of existing random text models and of the informativeness of quantitative text features. 12 22 2021 11 8 2021 145 162 10.1075/cilt.356.10kon https://benjamins.com/catalog/cilt.356.10kon https://www.degruyter.com/document/doi/10.1075/cilt.356.10kon/html Wiederholungen in Texten Altmann 1988 10.1093/llc/11.3.121 arXiv preprint arXiv:1409.0473 Bahdanau 2014 Neural machine translation by jointly learning to align and translate Journal of Machine Learning Research Bengio 3 1137 2003 A neural probabilistic language model Human language technologies 2007: The conference of the North American chapter of the association for computational linguistics; proceedings of the main conference Biemann 105 2007 A random text model for the generation of statistical language invariants 10.1145/130385.130401 10.1023/A:1010933404324 10.1016/j.envsoft.2006.10.004 10.1515/9783110362879-006 Czech and Slovak Linguistic Review Čech 3 4 2013 Methods of analysis of a thematic concentration of the text 10.1145/1961189.1961199 10.18653/v1/D16-1053 10.1177/001316446002000104 arXiv preprint arXiv:1810.04805 Devlin 2018 Bert: Pre-training of deep bidirectional transformers for language understanding Kybernetika Esteban 31 4 337 1995 A summary on entropy statistics Proceedings of the twenty-first national conference on artificial intelligence Gabrilovich 1301 2006 Overcoming the brittleness bottleneck using Wikipedia: Enhancing text categorization with encyclopedic knowledge 10.1073/pnas.0507655102 10.1162/neco.1997.9.8.1735 10.1007/978-1-4615-0907-3 Quita. Quantitative Index Text Analyzer Kubát 2014 10.2307/1932674 Quantitative linguistik. ein internationales handbuch / quantitative linguistics. An international handbook Mehler 325 2005 Eigenschaften der textuellen Einheiten und Systeme [Properties of textual units and systems] Journal for Language Technology and Computational Linguistics (JLCL) Mehler 22 2 51 2007 10.21248/jlcl.22.2007.95 Structural classifiers of text types: Towards a novel model of text representation Forschungsinfrastrukturen und digitale Informationssysteme in der germanistischen Sprachwissenschaft Mehler 149 2018 VienNA: Auf dem Weg zu einer Infrastruktur für die verteilte interaktive evolutionäre Verarbeitung natürlicher Sprache 10.1515/9783110573565-016 arXiv preprint arXiv:1611.02163 Metz 2016 Unrolled generative adversarial networks 10.1080/00401706.1991.10484804 Sankhyā: The Indian Journal of Statistics, Series A Parzen 383 1963 On spectral analysis with missing observations and amplitude modulation Word frequency studies Popescu 2009 Glottometrics Popescu 13 23 2006 Some aspects of word frequencies Glottometrics Popescu 15 71 2007 Writer’s view of text generation Issues in quantitative linguistics Popescu 2 110 2011 Thematic concentration in texts The lambda-structure of texts Popescu 2011 OpenAI Blog Radford 1 8 9 2019 Language models are unsupervised multitask learners 10.1017/S1351324997001502 10.1016/S0010-4655(02)00280-1 10.1016/j.cpc.2009.09.018 10.1023/B:STCO.0000035301.49549.88 10.1016/S0378-4754(00)00270-6 10.1214/009053607000000505 Advances in neural information processing systems Vaswani 5998 2017 Attention is all you need Quantitative Linguistik: Ein internationales Handbuch Wimmer 361 2005 The type-token-relation 10.1109/ICCV.2015.11