Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-16T08:21:21.443Z Has data issue: false hasContentIssue false

Pull out all the stops: Textual analysis via punctuation sequences

Published online by Cambridge University Press:  21 September 2020

ALEXANDRA N. M. DARMON
Affiliation:
Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK emails: [email protected], [email protected]
MARYA BAZZI
Affiliation:
Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK emails: [email protected], [email protected] The Alan Turing Institute, London NW1 2DB, UK email: [email protected] Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
SAM D. HOWISON
Affiliation:
Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK emails: [email protected], [email protected]
MASON A. PORTER
Affiliation:
Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK emails: [email protected], [email protected] Department of Mathematics, University of California, Los Angeles, Los Angeles, California 90095, USA email: [email protected]

Abstract

Whether enjoying the lucid prose of a favourite author or slogging through some other writer’s cumbersome, heavy-set prattle (full of parentheses, em dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvellously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in stylometry, the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)?

In our investigation, we examine a large corpus of documents from Project Gutenberg (a digital library with many possible editorial influences). We extract punctuation sequences from each document in our corpus and record the number of words that separate punctuation marks. Using such information about punctuation-usage patterns, we attempt both author and genre recognition, and we also examine the evolution of punctuation usage over time. Our efforts at author recognition are particularly successful. Among the features that we consider, the one that seems to carry the most explanatory power is an empirical approximation of the joint probability of the successive occurrence of two punctuation marks. In our conclusions, we suggest several directions for future work, including the application of similar analyses for investigating translations and other types of categorical time series.

Type
Papers
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altmann, E. G., Dias, L. & Gerlach, M. (2017) Generalized entropies and the similarity of texts. J. Stat. Mech. Theory Exp. 1, 014002.CrossRefGoogle Scholar
Arun, R., Suresh, V. & Madhavan, C. E. V. (2009) Stopword graphs and authorship attribution in text corpora. In: Proceedings of the 2009 IEEE International Conference on Semantic Computing, pp. 192196.CrossRefGoogle Scholar
Calhoun, A. J. (2016) Punctuation code. Available at https://github.com/adamjcalhoun/punctuation.Google Scholar
Calhoun, A. J. (2016) Punctuation in novels. Available at https://medium.com/ @neuroecology/punctuation-in-novels-8f316d542ec4#.brev0b3w1.Google Scholar
Calhoun, A. J. (2016) What does punctuation tell us about Republicans and Democrats? Avai-lable at https://medium.com/@neuroecology/what-does-punctuation-tell-us-about-republicans-and-democrats-bd46b9f98220.Google Scholar
Can, F. & Patton, J. M. (2004) Change of writing style with time. Comput. Human. 38, 6182.CrossRefGoogle Scholar
Chaski, C. E. (2001) Empirical evaluation of language-based author identification techniques. Forensic Linguist. 8, 165.Google Scholar
Chevyreva, I. & Kormilitzin, A. (2016) A primer on the signature method in machine learning. arXiv:1603.03788.Google Scholar
Chiang, H., Ge, Y. & Wu, C. (2015) Classification of Book Genres by Cover and Title. Class report, Computer Science 229, Stanford University. Available at http://cs229.stanford.edu/proj2015/127_report.pdf.Google Scholar
Cover, T. M. & Thomas, J. A. (1991) Elements of Information Theory, John Wiley & Sons, Inc., New York City, NY, USA.CrossRefGoogle Scholar
Duda, R. O., Hart, P. E. & Stork, D. G. (2001) Pattern Classification, John Wiley & Sons, Inc., New York City, NY, USA.Google Scholar
Ebeling, W. & Pöschel, T. (1994) Entropy and long-range correlations in literary English. Europhysics Lett. 26, 241246.CrossRefGoogle Scholar
Forsyth, R. S. (1999) Stylochronometry with substrings, or: a poet young and old. Literary Linguist. Comput. 14, 467477.CrossRefGoogle Scholar
Fowler, H. W. & Fowler, F. G. (1906) The King’s English, Oxford University Press, Oxford, UK.Google Scholar
Gerlach, M. & Font-Clos, F. (2020) A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics. Entropy 22, 126.CrossRefGoogle ScholarPubMed
Gerlach, M., Font-Clos, F. & Altmann, E. G. (2016) Similarity of symbol frequency distributions with heavy tails. Phys. Rev. X 6, 021009.Google Scholar
Grieve, J. (2007) Quantitative authorship attribution: an evaluation of techniques. Literary Linguist. Comput. 22, 251270.CrossRefGoogle Scholar
Hart, M. S. (1971) Project Gutenberg. Available at https://www.gutenberg.org.Google Scholar
Hartman, C. O. (2015) Verse: An Introduction to Prosody, Wiley-Blackwell, Hoboken, NJ, USA.Google Scholar
Holmes, D. I. (1998) The evolution of stylometry in humanities scholarship. Literary Linguist. Comput. 50, 111117.CrossRefGoogle Scholar
Honnibal, M. (2017) spaCy. Available at https://spacy.io.Google Scholar
Hughes, J. M., Foti, N. J., Krakauer, D. C. & Rockmore, D. N. (2012) Quantitative patterns of stylistic influence in the evolution of literature. Proc. Natl. Acad. Sci. U S A 109, 76827686.CrossRefGoogle Scholar
Jackson, M. P. (2002) Pause patterns in Shakespeare’s verse: canon and chronology. Literary Linguist. Comput. 17, 3746.CrossRefGoogle Scholar
Kessler, B., Nunberg, G. & Schutze, H. (1996) Automatic detection of text genre. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics.CrossRefGoogle Scholar
Kjell, B. (1994) Authorship attribution of text samples using neural networks and Bayesian classifiers. In: Proceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, pp. 16601664.CrossRefGoogle Scholar
Kullback, S. & Leibler, R. A. (1951) On information and sufficiency. Ann. Math. Stat. 22, 7986.CrossRefGoogle Scholar
Lai, S., Xu, L., Liu, K. & Zhao, J. (2015) Recurrent convolutional neural networks for text classification. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI ’15), pp. 22672273.Google Scholar
Lawler, J. (2006) Punctuation. In: Ken Brown (editor), Encyclopedia of Language & Linguistics, 2nd ed., Elsevier, Amsterdam, The Netherlands.Google Scholar
Lesne, A. (2014) Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics. Math. Struct. Comput. Sci. 24, e240311.CrossRefGoogle Scholar
Lewis, T. (1979) Notes on punctuation. In: The Medusa and the Snail: More Notes of a Biology Watcher, Viking Press, New York City, NY, USA.Google Scholar
Lin, J. (1991) Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37, 145151.Google Scholar
Lyons, T. (2014) Rough paths, signatures and the modelling of functions on streams. In: Proceedings of the International Congress of Mathematicians 2014, Korea. Available at http://www.icm2014.org/download/Proceedings_Volume_IV.pdf.Google Scholar
Mendenhall, T. C. (1887) The characteristic curves of composition. Science 9, 237249.CrossRefGoogle ScholarPubMed
Mosteller, F. & Wallace, D. L. (1964) Inference and Disputed Authorship: The Federalist, Addison-Wesley, Reading, MA, USA.Google Scholar
Neal, T., Sundararajan, K., Fatima, A. & Woodard, D. (2018) Surveying stylometry techniques and applications. ACM Comput. Surv. 50, 86.CrossRefGoogle Scholar
Neidorf, L., Krieger, M. S., Yakubek, M., Chaudhuri, P. & Dexter, J. P. (2019) Large-scale quantitative profiling of the old English verse tradition. Nat. Hum. Behav. 3, 560567.CrossRefGoogle ScholarPubMed
Nunberg, G. (1990) The Linguistics of Punctuation, Center for the Study of Language and Information, Stanford, CA, USA.Google Scholar
Parkes, M. B. (editor) (1992) Pause and Effect: An Introduction to the History of Punctuation in the West, University of California Press, Berkeley, CA, USA.Google Scholar
Pullum, G. & Huddleston, R. (2001) The Cambridge Grammar of the English Language, Cambridge University Press, The Other Place, UK.Google Scholar
Qian, C., He, T. & Zhang, R. (2017) Deep Learning Based Authorship Identification. Class report, Computer Science 224, Stanford University. Available at https://pdfs.semanticscholar.org/ab0e/be094ec0a44fb0013d640b344d8cfd7adc81.pdf?_ga=2.215953495.1190289256.1578845031-6826891.1578845031.Google Scholar
Santini, M. (2004) A shallow approach to syntactic feature extraction for genre classification. In: Proceedings of the 7th Annual Colloquium for the UK Special Interest Group for Computational Linguistics.Google Scholar
Santini, M. State-of-the-Art on Automatic Genre Identification, Information Technology Research Institute (ITRI) Technical Report Series 04-03, University of Brighton, UK, (2004). Available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.7680.Google Scholar
Shannon, C. E. (1948) A mathematical theory of communication. Bell Syst. Tech. J., 379–423, 623656.CrossRefGoogle Scholar
Shlens, J. (2014) Notes on Kullback–Leibler divergence and likelihood theory. arXiv:1404.2000.Google Scholar
Stamatatos, E. (2009) A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Tech. 60, 538556.CrossRefGoogle Scholar
Stamou, C. (2008) Stylochronometry: stylistic development, sequence of composition, and relative dating. Literary Linguist. Comput. 23, 181199.CrossRefGoogle Scholar
Truss, L. (2004) Eats, Shoots and Leaves: The Zero Tolerance Approach to Punctuation, Profile Books, London, UK.Google Scholar
Vieira, D. S., Picoli, S. & Mendes, R. S. (2018) Robustness of sentence length measures in written texts. Physica A 506, 749754.CrossRefGoogle Scholar
Watson, C. (2019) Semicolon: The Past, Present, and Future of a Misunderstood Mark, Ecco Press, New York, NY, USA.Google Scholar
Whissell, C. (1996) Traditional and emotional stylometric analysis of the songs of Beatles Paul McCartney and John Lennon. Comput. Human. 30, 257265.CrossRefGoogle Scholar
Yang, A. C.-C., Peng, C.-K., Yien, H.-W. and Goldberger, A. (2003) Information categorization approach to literary authorship disputes. Physica A 329, 473483.CrossRefGoogle Scholar
Zhao, Y., Zobel, J. & Vines, P. (2006) Using relative entropy for authorship attribution. In: Proceedings of the Third Asia Conference on Information Retrieval Technology (AIRS ’06), pp. 92105.CrossRefGoogle Scholar