Abstract
Travelogues represent an important and intensively studied source for scholars in the humanities, as they provide insights into people, cultures, and places of the past. However, existing studies rarely utilize more than a dozen primary sources, since the human capacities of working with a large number of historical sources are naturally limited. In this paper, we define the notion of travelogue and report upon an interdisciplinary method that, using machine learning as well as domain knowledge, can effectively identify German travelogues in the digitized inventory of the Austrian National Library with F1 scores between 0.94 and 1.00. We applied our method on a corpus of 161,522 German volumes and identified 345 travelogues that could not be identified using traditional search methods, resulting in the most extensive collection of early modern German travelogues ever created. To our knowledge, this is the first time such a method was implemented for the bibliographic indexing of a text corpus on this scale, improving and extending the traditional methods in the humanities. Overall, we consider our technique to be an important first step in a broader effort of developing a novel mixed-method approach for the large-scale serial analysis of travelogues.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
We will share the corpus here: https://github.com/Travelogues/travelogues-corpus.
- 2.
The code (as Jupyter notebook) that we used for the classification is available here: https://github.com/Travelogues/identifying-travelogues.
- 3.
- 4.
- 5.
Many works focus on datasets that have more, but shorter documents, c.f. [31] for comparisons of multiple classification methods and datasets.
- 6.
C.f. Sect. 2.1.
- 7.
Depending on the sources, and if additional definitions etc. are needed, between several hours and up to a few weeks of full-time work.
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Agai, B., Conermann, S. (eds.): “Wenn einer eine Reise tut, hat er was zu erzählen”. Präfiguration – Konfiguration – Refiguration in muslimischen Reiseberichten. ebv, Berlin (2013)
Bellingradt, D., Salman, J.: Books and book history in motion: materiality, sociality and spatiality. In: Bellingradt, D., Nelles, P., Salman, J. (eds.) Books in Motion in Early Modern Europe. NDBH, pp. 1–11. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53366-7_1
Chollet, F., et al.: Keras (2015). https://keras.io
Dai, X., Bikdash, M., Meyer, B.: From social media to public health surveillance: word embedding based clustering method for twitter classification. In: SoutheastCon 2017, pp. 1–7. IEEE (2017)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9(Aug), 1871–1874 (2008)
Genz, J., Gévaudan, P.: Medialität, Materialität, Kodierung: Grundzüge einer allgemeinen Theorie der Medien, pp. 201–209. Transcript, Bielefeld (2016)
Greve, A.: Die Konstruktion Amerikas: Bilderpolitik in den “Grands Voyages” aus der Werkstatt de Bry, Europäische Kulturstudien, vol. 14. Böhlau, Köln, Weimar und Wien (2004)
Höfert, A.: Den Feind beschreiben. >>Türkengefahr<< und europäisches Wissen über das Osmanische Reich 1450–1600, pp. 120–122. Ferdinand Schöningh, Paderborn (2014)
Joachims, T.: Text categorization with Support Vector Machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
von Krusenstjern, B.: Was sind Selbstzeugnisse? Begriffskritische und quellenkundliche Überlegungen anhand von Beispielen aus dem 17. Jahrhundert. Forum Historische Anthropol. 2, 462–471 (1994)
Kürbis, H.: Hispania descripta: Von der Reise zum Bericht. Deutschsprachige Reiseberichte des 16. und 17. Jahrhunderts über Spanien. Ein Beitrag zur Struktur und Funktion der frühneuzeitlichen Reiseliteratur, pp. 345–356. Peter Lang, Frankfurt am Main (2004)
Lüdtke, A., et al. (eds.): Selbstzeugnisse der Neuzeit. Böhlau, Weimar (1993)
Mai, F., Galke, L., Scherp, A.: Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text. In: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, pp. 169–178. ACM (2018)
Michel, J.B., et al.: Quantitative analysis of culture using millions of digitized books. Science 331(6014), 176–182 (2011)
Momeni, E., Tao, K., Haslhofer, B., Houben, G.J.: Identification of useful user comments in social media: a case study on flickr commons. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 1–10. ACM (2013)
Nünning, A.: Zur mehrfachen Präfiguration/Prämediation der Wirklichkeitsdarstellung im Reisebericht: Grundzüge einer narratologischen Theorie, Typologie und Poetik der Reiseliteratur. In: Gymnich, M., et al. (eds.) Points of Arrival: Travels in Time, Space, and Self/Zielpunkte: Unterwegs in Zeit, Raum und Selbst, pp. 11–32. Francke, Tübingen (2008)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pfister, M.: Intertextuelles Reisen, oder: Der Reisebericht als Intertext. In: Wetzel, H.H. (ed.) Reisen in den Mittelmeerraum, pp. 55–101. Passavia Universitätsverlag, Passau (1993)
Piera, M.: Travel as episteme–an introductory journey. In: Piera, M. (ed.) Remapping Travel Narratives (1000–1700), pp. 1–22. Arc Humanities Press, Leeds (2018)
Presser, J.: Memoires als geschiedbron. In: Presser, J. (ed.) Uit het werk van Jacob Presser, pp. 277–282. Athenaeum-Polak & Van Gennep, Amsterdam (1969)
Purschwitz, A.: Netzwerke des Wissens – Thematische und personelle Relationen innerhalb der halleschen Zeitungen und Zeitschriften der Aufklärungsepoche (1688–1818). J. Hist. Netw. Res. 2(1), 109–142 (2018)
Salzani, C., Tötösy de Zepetnek, S.: Bibliography for work in travel studies. CLCWeb Library p. travelstudiesbibliography (2010). https://docs.lib.purdue.edu/clcweblibrary/travelstudiesbibliography
Sandrock, K.: Truth and Lying in early modern travel narratives: coryat’s crudities, lithgow’s total discourse and generic change. Eur. J. English Stud. 19(2), 189–203 (2015)
Settles, B.: Active learning. Synth. Lect. Artif. Intell. Mach. Learn. 6(1), 1–114 (2012)
Stagl, J.: Apodemiken. Eine räsonnierte Bibliographie der reisetheoretischen Literatur des 16., 17. und 18. Jahrhunderts, Quellen und Abhandlungen zur Geschichte der Staatsbeschreibung und Statistik (QASS), vol. 2. Ferdinand Schöningh, Paderborn et al. (1983)
Stagl, J.: Eine Geschichte der Neugier. Die Kunst des Reisens 1550–1800, pp. 77–122. Böhlau, Köln, Weimar und Wien (2002)
Treue, W.: Abenteuer und Anerkennung. Reisende und Gereiste in Spätmittelalter und Frühneuzeit (1400–1700), p. 8. Ferdinand Schöningh, Paderborn (2014)
Van Groesen, M.: The Representations of the Overseas World in the De Bry Collection of Voyages (1590–1634). Brill, Boston and Leiden (2008)
Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94. Association for Computational Linguistics (2012)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015)
von Zimmermann, C.: Texttypologische Überlegungen zum frühneuzeitlichen Reisebericht: Annäherung an eine Gattung. Archiv für das Studium der neueren Sprachen und Literaturen 154, 1–20 (2002)
Acknowledgments
The work in the Travelogues project (http://www.travelogues-project.info) is funded through an international project grant by the Austrian Science Fund (FWF, Austria: I 3795) and the German Research Foundation (DFG, Germany: 398697847).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Rörden, J., Gruber, D., Krickl, M., Haslhofer, B. (2020). Identifying Historical Travelogues in Large Text Corpora Using Machine Learning. In: Sundqvist, A., Berget, G., Nolin, J., Skjerdingstad, K. (eds) Sustainable Digital Communities. iConference 2020. Lecture Notes in Computer Science(), vol 12051. Springer, Cham. https://doi.org/10.1007/978-3-030-43687-2_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-43687-2_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43686-5
Online ISBN: 978-3-030-43687-2
eBook Packages: Computer ScienceComputer Science (R0)