=Paper=
{{Paper
|id=Vol-2200/paper4
|storemode=property
|title=Traveling through Space and Time, or: Making Historical Travelogues Accessible
|pdfUrl=https://ceur-ws.org/Vol-2200/paper4.pdf
|volume=Vol-2200
|authors=Jan Rörden,Bernhard Haslhofer,Rainer Simon,Sven Schlarb
|dblpUrl=https://dblp.org/rec/conf/ercimdl/RordenHSS18
}}
==Traveling through Space and Time, or: Making Historical Travelogues Accessible==
https://ceur-ws.org/Vol-2200/paper4.pdf
Traveling through Space and Time, or: Making
Historical Travelogues Accessible.
Jan Rörden1[0000−0002−5824−8397] , Bernhard Haslhofer1[0000−0002−0415−4491] ,
Rainer Simon1[0000−0002−4116−9684] , and Sven Schlarb1
AIT Austrian Institute of Technology, Vienna, Austria
{first}.{last}@ait.ac.at
Abstract. Investigating perceptions of Otherness is the overarching goal
of the Travelogues project. It studies a corpus comprising of thousands
of recently digitized travelogues dating back to the 16th century held
by the Austrian National Library. Driven by an interdisciplinary team
of historians and data scientists, it aims at making knowledge that is
now hidden in a huge text corpus accessible to researchers. In the cur-
rent, initial project phase, we explore how statistical methods, such as
word embeddings, can be used to assess the structure and semantics of
large text corpora in order to make those resources accessible. We devel-
oped an initial methodology that combines visual and statistical cues for
identifying possible starting points for a more fine-grained text corpus
exploration. Ultimately, this data-driven approach is expected to result
in new and possibly unexpected insights stemming from resources that
were previously de-facto inaccessible.
Keywords: Digital Humanities · Machine learning · Information extrac-
tion.
1 Introduction
Close reading (the careful and exhaustive interpretation of a passage of text) of
source material is a key methodology and daily routine for historians. However,
this methodology has its natural limits when being applied on large digitized
book holdings such as the Austrian Books Online1 , which comprises more than
400,000 volumes including titles from the early 16th century up to the second
half of the 19th century.
The Travelogues2 project focuses on historical travel reports ranging from the
16th to late 19th century, published in German language, and works towards de-
veloping a toolbox that helps exploring source materials and understanding their
semantics. Aiming for qualitative insights into historical sources, we describe a
set of methods and their combination that can help domain experts (in our case,
historians) in finding answers to research questions based on source material
hidden in huge text corpora.
1
https://www.onb.ac.at/digitale-bibliothek-kataloge/austrian-books-online-abo/
2
http://www.travelogues-project.info
2 J. Rörden et al.
In addition to applying well-known information retrieval methods, we also
aim at enabling the presentation of results in ways that are easily accessible by
non-experts as well. Uncovering previously hidden knowledge is the priority of
this project.
2 The Dataset
The corpus in question consists of more than 150,000 recently digitized works
in German language (out of a total 419,000 for the given time frame), which
are part of the Austrian National Library’s inventory, with all works published
between 1500 and 1875.3 Out of those, an estimated 1,000 to 2,000 books can be
classified as travel reports, either completely or containing chapters describing
travels. Travel reports are an important source genre in the historical science [3],
and serial analysis has been suggested as a way to analyze them [1].
Innate and challenging properties of this corpus include, for example, the lin-
guistic variety, which comes from works spanning 450 years of German language,
most of it without standardized orthography. Additionally, problems arise due
to the digitization process such as OCR errors (especially with texts printed in
Fraktur) and loss of information due to missing layout information. For example,
there can be the unclean appearance of individual characters due to old print-
ing techniques, letters from the inverse page can be visible because of thin book
page paper (bleed-through), or the book pages might be damaged due to storage
conditions (e.g. humidity caused page warping) or by catastrophic events (e.g.
fire). On top of that, the correct classification of travel reports is a problem to be
solved. This is also due to incomplete or incorrect meta data from the existing
catalog, which was in parts created in the 19th century.
3 Methodology
In a first step, we make use of the word2vec algorithm [5, 4] and visualizations
through t-SNE [2].
To develop our methodology, we worked on a small subset of the corpus. This
subset consists of 100 books, published between 1800-1875 and selected through
basic criteria - the title had to contain the German word for travel, ’Reise’, in one
of four different spellings (to cover language variations: ’reis*’, ’reyß*’, ’reiß*’,
’reys*’). The result was a mid-sized corpus of just over 10 million words.
We kept the preprocessing of the source material to a minimum, to preserve
as much of the original structure and wording of the text as possible. Those
aspects were deemed important for further language analysis.
We then applied word2vec4 to extract semantic relationships contained in
the test corpus, and plotted word clusters using tSNE [2].
3
Letterpress printing was widely available from the early 16th century, and after 1875,
copyright issues might arise - hence this time frame.
4
The gensim[6] implementation was used.
Through Space and Time: Historical Travelogues 3
4 Preliminary results
The first results and observations are promising, as we were quickly able to get
an intuition of the main topics of the source material. Plotting the 500 most
frequent words immediately drew attention to thematic clusters, such as set-
tlements (different types of buildings from huts to fortifications to cathedrals),
nobility (rulers, tribes etc.), food (types of meat, methods of preparation), sea-
sons and also abstract topics like emotions. By plotting the semantically closest
words to ’danger’ (see figure 1), we are able to draw a picture of dangers asso-
ciated with traveling, as far as our subset is concerned. This includes seafaring,
bandits and bad roads; illness; but also feelings like fear, anger and sadness.
Interestingly, many adjectives were also included - hinting that this topic is es-
pecially connected to vivid descriptions and emotions. Other topics centered on
food or cities offer insights into contemporary descriptions.
Fig. 1. Focus on the ’danger’ cluster.
5 Conclusion and further work
We have learned that text-exploration techniques like word embeddings can be
applied to a basically unknown text corpus in a domain that is challenging
4 J. Rörden et al.
through linguistic variety and OCR errors. In the long run, we plan to update
the library catalogue with additional and corrected meta data. For the next
steps, three historians picked a variety of representative travelogues, published
between 1700-1875. They consist of just over 2.5 million words, and we will use
them as the starting point in creating a classifier to identify additional, non-
obvious travelogues in the corpus.
We also plan to introduce additional approaches that can be used for in-
formation extraction, such as topic models and tf-idf. Furthermore, the ground
truth will be manually annotated using the software platform Recogito5 to mark
certain semantic structures. The goal of this approach is the identification of the
semantic concepts that describe notions of Otherness and its evolution through
time.
Acknowledgements
This work is supported by the Austrian FWF as project I 3795 and the German
DFG as project 398697847.
References
1. Harbsmeier, M.: Reisebeschreibungen als mentalitätsgeschichtliche quellen: Über-
legungen zu einer historisch-anthropologischen untersuchung frühneuzeitlicher
deutscher reisebeschreibungen. Reiseberichte als Quellen europäischer Kul-
turgeschichte, hg. v. A. Mączak, HJ Teuteberg pp. 1–31 (1982)
2. Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. Journal of machine learning
research 9(Nov), 2579–2605 (2008)
3. Maczak, A., Teuteberg, H.J.: Reiseberichte als Quellen europäischer
Kulturgeschichte: Aufgaben und Möglichkeiten der historischen Reise-
forschung:[Vorträge gehalten anlässlich des 9. Wolfenbütteler Symposions vom 22.
bis 25. Juni 1981 in der Herzog August Bibliothek]. Herzog August Bibliothek
(1982)
4. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word represen-
tations in vector space. arXiv preprint arXiv:1301.3781 (2013)
5. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed repre-
sentations of words and phrases and their compositionality. In: Advances in neural
information processing systems. pp. 3111–3119 (2013)
6. Řehůřek, R., Sojka, P.: Software Framework for Topic Modelling with Large Cor-
pora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP
Frameworks. pp. 45–50. ELRA, Valletta, Malta (May 2010), http://is.muni.cz/
publication/884893/en
5
https://recogito.pelagios.org/