Non-Literal Text Reuse in Historical Texts: An Approach to Identify Reuse Transformations and its Application to Bible Reuse

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1849–1859, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Non-Literal Text Reuse in Historical Texts: An Approach to Identify Reuse Transformations and its Application to Bible Reuse Maria Moritz1, Andreas Wiederhold2, Barbara Pavlek3, Yuri Bizzoni4, and Marco B¨uchler1 1Institute of Computer Science, University of G¨ottingen 2Institute for Educational Science, University of G¨ottingen 3Minds and Traditions Research Group, Max Planck Institute for the Science of Human History, Jena 4Department of Philosophy, Linguistics, Theory of Science, University of Gothenburg Abstract Text reuse refers to citing, copying or allud- ing text excerpts from a text resource to a new context. While detecting reuse in con- temporary languages is well supported—given extensive research, techniques, and corpora— automatically detecting historical text reuse is much more difficult. Corpora of historical lan- guages are less documented and often encom- pass various genres, linguistic varieties, and topics. In fact, historical text reuse detection is much less understood and empirical studies are necessary to enable and improve its automation. We present a linguistic analysis of text reuse in two ancient data sets. We contribute an auto- mated approach to analyze how an original text was transformed into its reuse, taking linguis- tic resources into account to understand how they help characterizing the transformation. It is complemented by a manual analysis of a subset of the reuse. Our results show the limi- tations of approaches focusing on literal reuse detection. Yet, linguistic resources can effec- tively support understanding the non-literal text reuse transformation process. Our results sup- port practitioners and researchers working on understanding and detecting historical reuse. 1 Introduction The computational detection of historical text reuse— including citations, quotations or allusions —can be applied in many respects. It can help tracing down historical content (a.k.a., lines of transmis- sion), which is essential to the field of textual crit- icism (B¨uchler et al., 2012). In the context of mas- sive digitization projects, it can identify relationships between text excerpts referring to the same source. Specifically, detecting copies of the same historical text that have diverged over time (manuscript studies, a.k.a., Stemma Codicum) is an important task. Although much work exists in the field of nat- ural language processing (NLP), many new chal- lenges arise when processing historical text. The most important challenges are the absence of support- ing tools and methods, including an agreement on a common orthography, standardization of variants, and a wide range of clean, digitized text (Piotrowski, 2012; Geyken and Gloning, 2014; Zitouni, 2014). Typical statistical approaches from the field of NLP are difficult to apply to historically transferred texts, since these often cover a large timespan and, thus, comprise many different writing styles, text variants or even reuse styles (B¨uchler, 2013). Our long-term goal is to conceive robust text reuse detection tech- niques for historical texts. To this end, we need to improve the quantitative empirical understanding of such reuse accompanied by qualitative empirical stud- ies. However, only few such works …

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