=Paper=
{{Paper
|id=Vol-3724/short4
|storemode=property
|title=Non-Canonical Acts and their Topical Distribution
|pdfUrl=https://ceur-ws.org/Vol-3724/short4.pdf
|volume=Vol-3724
|authors=Christian Vrangbæk,Eva Vrangbæk,Márton Kardos,Kristoffer Nielbo,Jacob Mortensen
}}
==Non-Canonical Acts and their Topical Distribution==
https://ceur-ws.org/Vol-3724/short4.pdf
Non-Canonical Acts and their Topical Distribution
Christian Vrangbæk1,∗,† , Eva Vrangbæk1,† , Márton Kardos1 , Kristoffer Nielbo1 and
Jacob Mortensen1
1
Aarhus University, Ringgaden 1, 8000 Aarhus C, Denmark
Abstract
This paper investigates how we can use topic modelling to characterize and place four apocryphal, i.e.
non-canonical, “Acts stories” in a corpus of ancient Greek texts. In the research field of New Testament
Apocrypha, there remains uncertainty concerning the classification of apocryphal text The analysis
serves the purpose of creating a structured ontology to be used in classifying New Testament Apocrypha.
We attempt to show that topic modelling can be a viable tool in classifying and characterizing these
texts. The results show that a) our four target texts of non-canonical “Acts stories” are ambiguous
and multifaceted in their topical distribution compared to other texts in the corpus, and b) that topic
modelling is a viable tool in this analysis.
Keywords
Apocrypha, New Testament Studies, topic modelling, classification
1. Introduction
In the field of New Testament Studies, classification of the heterogenous group of non-canonical
texts, i.e. apocrypha, is disputed. The main problem is that the taxonomy of modern scholars
largely reproduces the ancient classifications which were shaped during the 4th and 5th-century
debate of canon which tends to lead to a binary classification between either canonical or non-
canonical, and, moreover, to utilize conceptualized labels such as “Gnosticism” and “Encratite”
which does not do justice to the complexity of the topical variety in these texts [1, 2, 3]. To
contribute to this debate, we want to create an ontology, i.e., a structured framework, out of
(apocryphal) textual data with the overarching goal of establishing a computationally driven
classification system for New Testament Apocrypha within the context of the semantic web.
We will investigate the topical distribution of four non-canonical acts. Non-canonical acts are
stories from roughly 2nd-3rd century CE about early Christian apostles’ legendary deeds and
speeches [4, 2, 5]. In this paper, we investigate the stories called Acta Joannis, Acta Thomae,
Acta Barnabae and Acta Philippi [6]. These texts have been chosen as tests to create the basis
of including more texts. We want to contribute to define categories, textual characteristics, and
SemDH 2024: First International Workshop of Semantic Digital Humanities, May 26–27, 2024, ESWC 2024, Hersonissos,
Greece
∗
Corresponding author.
†
C. Vrangbæk is first and main author, E. Vrangbæk is second author with equal contribution. M. Kardos and K.
Nielbo are authors of code and visualization for topic modelling, J. Mortensen hosts the text database.
Envelope-Open mailto:chv@cas.au.dk (C. Vrangbæk)
Orcid 0009-0005-2011-3082 (C. Vrangbæk); 0000-0002-4941-3434 (E. Vrangbæk); 0000-0001-9652-4498 (M. Kardos);
0000-0002-5116-5070 (K. Nielbo); 0000-0002-7153-3707 (J. Mortensen)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
relationships between texts by building a structured ontology. While the creation of this ontology
extends beyond the scope of this paper, our present study of integrating topic modelling in
research on New Testament Apocrypha serves a crucial step towards this endeavor. By utilizing
ontological relationships and semantic annotations, in this context as topical distributions,
this study explores the potential for integrating the classical theological concepts present in
New Testament Apocrypha with modern computational methods, thereby bridging the gap
between traditional textual analysis and advanced semantic technologies within the context of
the semantic web. Our working question is how and in what way can this model contribute to
the discussion of classifying non-canonical acts in a corpus of ancient Greek texts?
2. Data and Methods
Our starting point is that digital methods are not quick and magic tools to solve complex
questions [7], rather we find that constant and critical exchange between traditional and new
methods in qualitative collaboration is the way forward [8, 9, 10]. Due to the circumstance
that this study originates from a research discipline where computational methods are not
embedded, we find it important to provide a detailed description — tending to tedious — of the
methodological process, since we believe that this can help to bridge the disciplinary divides
with a low-practical how-to-style
2.1. Text Corpus and Preprocessing
The first step is to provide our text corpus. The corpus serves in our experiments as the literary
context for the target texts, for which reason a historical relation between corpus and target
texts are needed. Our target texts, the four apocryphal acts, are written in the Ancient Greek
language, for which reason we have gathered a database consisting of 2153 Ancient Greek
Texts. These texts are retrieved from the Perseus Corpus, First1KGreek, Pseudepigrapha.org and
Deutsche Bibelgesellschaft [11, 12]. The selection of this corpus is chosen based on its relevance
for our target texts. The corpus largely sets the parameters for our topic modelling at the later
stages of the experiments. The Ancient Greek texts are in a solid machine-readable state. For the
corpus text to be prepared for calculations we perform a set of preprocessing steps, so that the
text is cleaned and lemmatized. We clean out for any Latin characters, digits, extra whitespaces
and stop words. The textual cleaning follows the logic of standard natural language processing
utilities. The models for cleaning and parsing the text were built with a transformer-based
pipeline called OdyCy [13]. This pipeline is a single-transformer pipeline that uses the following
workflow: The transformer is built on Ancient Greek Bert [14]. The parser, morphologizer and
lemmatizer follows the infrastructure of SpaCy [15]. Before running topic modelling, we had
already preprocessed the input text, so we set max_df and min_df to 1.0, since we did not want
to ignore any terms in this experiment [16]. When the textual preprocessing is done, we move
on to vectorization. Vectorisation is a highly qualitative choice, almost a language philosophical
step, in which we must decide how to represent our textual corpus as numerical vectors [17].
In this case, we employ the method of term frequency-inverted document frequency (TF-IDF)
which represents the logarithmic scale between a term’s frequency and the inverted frequency
of total documents in the corpus [18, 17, 16].
2.2. Topic Modelling: Non-Negative Matrix Factorization
Topic modelling is a much-utilized tool when engaging in natural language processing
classification tasks, mostly for the purpose of assigning a category based on the most probable
topic to a text in a corpus [19, 20, 21]. This is also partly our aim, although we do see
possibilities of detecting more complex layers in the topical distribution besides a text being
only a part of one single category, rather, topic modelling gives due credit to the complex
topical distribution and its significance for the position of our target texts in the corpus. Topic
modelling is a way of structuring our textual data to be included in a future knowledge graph
and similar semantic web technologies. Topic modelling assumes that a corpus of texts consists
of topics and that these topics are comprised by the words of the corpus. We utilize the kind of
topic modelling called Non-negative Matrix Factorization (NMF) [22, 23]. NMF is an approach
that decomposes a high-dimensional term-document matrix, i.e., a matrix consisting of the
corpus documents in columns, the words in rows, and their occurrence-values in the cell entries.
The occurrence-values are, as mentioned above, chosen to be TF-IDF. Based on optimization,
the method of NMF calculates topical patterns by associating words with topics and topics
with documents. The NMF model, so to say, produces latent topical patterns by grouping
similar and co-occurring words in the corpus [24]. These topics are then backtracked to fit
to the documents. The number of topics to choose is important for the analytical task. If
we choose too many topics, there were no interpretable coherence, so based on our knowl-
edge of the corpus and trial-and-error process the most robust output in topics was 10 topics [25].
The 10 topics and a selection of their top words are:
1. city, war, ruler, Hellene, land [πόλις, πὸλεμος, βασιλεύς, ἕλλην, χώρα]
2. god, soul, heaven, word, Christ [θεός, ψυχή, λόγος, χριστός]
3. god, lord, people, human being [θεός, κυριός, λαός, ἄνθρωπος]
4. body, matter, air, earth, water, fire [σῶμα, ὕλη, ἀήρ, γή, ὕδωρ, πῦρ]
5. Zeus, child, desire, Cypris, Apollo [ζέυς, παῖς, ἔρως, κύπρις, ἀπόλλων]
6. part, character, being, necessity, cause [μόριον, τρόπος, οὐσία, άνάγκη, αίτία]
7. law, justice, city, witness, possession [νόμος, δίκη, πὸλις, μὰρτυς, χρῆμα]
8. reason, city, law, fortune, favor [λόγος, πόλις, νόμος, τύχη, χάρις]
9. circle, angle, center, sign, appearance [κύκλος, γωνία, κέντρον, σημεῖον, ἐπιφάνεια]
10. reason, nature, virtue, human being [λόγος, φύσις, ἀρετή, ἄνθρωπος]
How the topics in a topic modelling are to be interpreted in a qualitative way is disputed
in scholarship [21, 26], so we decided to be transparent about our process, which was as
follows: Our domain knowledge enables us to notice and interpret the words of the topic into a
meaningful collective description, like e.g. “Historical-political topic” for Topic 0 and “Element
philosophy” for Topic 9. To nuance this interpretation of topics, we investigated our corpus for
those texts with the cleanest topic distribution. An example of a clean topical distribution is the
Book of Jeremiah from the Old Testament, which has an almost 95% of Topic 2. Another example
is Aristotle’s Problemata, which is dominated by Topic 9. Finally, we have a metadata set that
groups our texts in predefined genres, like, for example, Jewish Philosophy, Tragedies and New
Figure 1: Pie chart visualization of topical distribution of the four target texts in the corpus of Ancient
Greek texts. From top left to bottom right the four Acts stories are marked in the order Acta Joannis,
Acta Thomae, Acta Philippi, Acta Barnabae.
Testament Gospels. These genre categories did not influence the topic model’s calculations
but can be used by us as a navigating tool to interpret the topics. For example, we can see
that Topic 7 and Topic 6 have overlapping words about justice and city, but we can see that
the texts that are dominated by Topic 6 are rhetorical texts like Demosthenes, whereas Topic
7 dominate many of Philo of Alexandria’s texts as well as Libanius’ Declamationes which are
more philosophical. These extra steps enable us to distill the words of the topic into a qualified
label or notion about how to describe the topic presented.
3. Analysis 1: Topical Distribution of Four Non-Canonical Acts
Stories
In our first analysis, we interpret the topical distribution of the Acta Joannis, Acta Thomae,
Acta Barnabae and Acta Philippi [27]. The results can be seen in Figure 1, which shows four
pie charts over the topical distribution of the four non-canonical acts.
Overall, all the four target texts are dominated by topic 2 and generally they share similar
groups of topics, but the distribution is not equal. If we wanted to group the target texts
together in the corpus on a coarse level, they would be set in the group of topic 2. However,
this would not be a discovery. Where topic modelling can lead us further is in the presence and
distribution of minor topics compared to each other. The topics of Acta Joannis are distributed
over mainly topics 2, 1, 9, 3, 0, 4 and 6. Topic 1 is, like topic 2, also a theological topic. The
presence of topic 9, 3 and 4 is revealing of the content of the story, since these topics represent
philosophically oriented words which tell about how the story engages in Hellenistic religion
and philosophy. Topic 0 is the historical-political topic which is understandable since the text
narrates sequences of events and speeches. We also see a small portion of Topic 6, which
concerns justice and law, which resonates with a few scenes in the narrative. From the topical
distribution, we can generally characterize Acta Joannis as being situated in a Jewish-Christian
theological context where Hellenistic anthropocentric philosophy and religion is also present.
Concerning Acta Thomae , the topical distribution is similar to that of Acta Joannis, with a
large topic 2 and 1, but Acta Thomae has a more equal distribution between its subtopics,
3, 9, 4 and 0. The Acta Thomae-story is set in legendary India, where Thomas is sent off as
a missionary. From manual reading, Acta Thomae and Acta Joannis are comparable in the
sense of the mix between narrative and preaching, where the preaching might drag in the more
philosophically weighted topics, and this relation is also visible in the topical distribution. When
we inspect the topics of Acta Philippi, then the distribution is markedly different from the two
previous narratives with Topic 0, the historical-political topic being the third most prominent.
Although Acta Philippi in its content has a much more adventurous and mythical tone with, for
example, the protagonists arriving to a city of snakes, the structural dynamic of this narrative is
dominated by sequence narration where we follow event after event [5, 2]. This makes the
Acta Philippi into a drier, journal-like text. The large presence of Topic 0 is also visible in the
topical distribution of Acta Barnabae. This circumstance is probably linked to the fact that Acta
Barnabae is situated in a church-political context of legitimizing the so-called autocephalous,
i.e., independent ecclesiastical unity in Creta in the 4th century [28].
4. Analysis 2: Position in Corpus
In the second part of the analysis, we want to address how the topical distribution analyzed in
the previous part affects texts’ position in the corpus. The position of each of the four target
texts are visualized in Figure 2
The clusters are formed based on texts with a dominant topic. Those texts which have
an almost unequivocal dominance of one topic, e.g., Aristotle’s Problemata and The Book of
Jeremiah are placed in the outskirts of each colored cluster dragging away from the center of
complexity. Conversely, those texts that are close to the center and also other topical groups
display diversity and complexity in their topical distribution. Some clusters appear like relatively
demarcated cone-shapes like e.g. Topic 4 of Hellenistic religion and philosophy and Topic 2 of
god, people, human, whereas topic 3 of Greek element-philosophy is more flat, which indicates
that this topic is dispersed more evenly in the corpus. The topical distribution on the corpus
level gives a navigational tool with which texts can be grouped with relative clarity. For example,
it is noteworthy that classic, and almost foundational texts, of Ancient Greek language, the
Iliad and Odyssey, are clearly situated in topic 4 of Greek religion and philosophy, and that
later texts that are trying to imitate these, like Tryphiodorus’ Sack of Troy (4th century CE)
and Nonnus’ Dionysiaca from the 5th century CE, almost a thousand years after Homer, have
an almost identical topical distribution. All of the Acts stories are placed in the dark green
Figure 2: Four scatterplot snapshots of the same corpus with different markings of a target text. The
plots’ positions are based on their topical distribution. From top left to bottom right the four Acts stories
are marked in the order Acta Joannis, Acta Thomae, Acta Philippi, Acta Barnabae. The figure is a frozen
image from one angle of a multidimensional space. The scatterplot is created based on the topical
distribution of the four texts
cluster of Topic 2, the topic of god, people, human. But it is noticeable that they all show
diversity in the distribution. Acta Joannis and Acta Thomae are placed more securely in the
Topic 2 cluster, whereas Acta Philippi and Acta Barnabae are drawn toward the center which
might be explained by their higher percentage of the historical-political topic Topic 0. When
texts are placed in this corpus, it becomes important to iterate the basic assumption of topic
modelling: that all topics are produced based on words in the corpus. This means that the
corpus words are constituent of the created topics which calls for a qualitative choice of corpus
texts. Our corpus consists, as mentioned, of texts that historically have shaped our four target
texts, either directly or indirectly due to the circumstance that the (mostly anonymous) authors
were educated people in the Greco-Roman world about whom it can be assumed that they
had basic knowledge of the texts in their historical and literary context. The method of topic
modelling, then, almost backtracks the literary world of the authors of our texts, of course,
with the important acknowledgement that we do not have all texts which made up the author’s
literary context.
5. Concluding Remarks
In this analysis assisted by topic modelling, we were able to characterize and place four non-
canonical acts based on their topical distribution. The topical distribution of the analyzed texts
will be used to map ontological relationships and enhance semantic annotations in order to
classify New Testament Apocrypha, among which the topical distribution is a major component.
The results of this topic modelling analysis will thus be able to be included in a future New
Testament Apocryphal Ontology. The navigational advantages of topic modelling allowed us
to inspect the target texts in a qualitatively selected corpus consisting of texts from a similar
historical and literary horizon. This ensured meaningful topics. Instead of characterizing
and classifying the texts based on abstractions and taxonomy from the 4th and 5th century
church-political discussions on canonization, we could characterize and classify, or at least
contribute to these tasks on the basis of raw content, both on a small, close scale in the topical
distribution from text to text, but also on a larger scale based on the entire corpus.
Acknowledgments
The research in this article is funded by the Carlsberg Foundation in the Semper Ardens:
Accelerate-project “Computing Antiquity: Computational Research in Ancient Text Corpora.”
We would also like to thank Deutsche Bibelgesellschaft, Pseudepigrapha.org, The Perseus-Project
and First1KGreek for providing texts.
References
[1] K. King, No longer marginalized. from orthodoxy and heresy discourse to category critique
and beyond, in: O. Lehtipuu, S. Petersen (Eds.), Ancient Christian Apocrypha, SBL Press,
Atlanta, 2023, pp. 13–32. doi:10.2307/j.ctv2rh2cqj .
[2] F. Bovon, Die kanonische apostelgeschichte und die apokryphen apostelakten, in: Die
Apostelgeschichte im Kontext antiker und frühchristlicher Historiographie, volume 162,
Walter de Gruyter, Berlin, New York, 2009. doi:10.1515/9783110216325 .
[3] D. Martin, New testament apocrypha: Introduction and critique of a modern category, in:
C. W. J. C. Edwards, C. Evans (Ed.), Ancient Christian Apocrypha, Zondervan Academic,
Grand Rapids, 2022, pp. 490–511.
[4] H.-J. Klauck, The Apocryphal Acts of The Apostles: An Introduction, Baylor Univ. Press,
2008.
[5] F. Bovon, Canonical and apocryphal acts of apostles, Journal of Early Christian Studies 11
(2003) 165–194.
[6] M. Bonnet, R. A. Lipsius, Acta Apostolorum Apocrypha. Vols. 1–3, Mendelssohn, Leipzig,
1903.
[7] I. Uglanova, E. Gius, The order of things. a study on topic modelling of literary texts,
in: Proceedings of the Workshop on Computational Humanities Research (CHR 2020),
Amsterdam, the Netherlands, 2020, pp. 57–76.
[8] P. Molitor, J. Ritter, Digital methods for intertextuality studies, IT - Information Technology
62 (2020) 49–51. doi:10.1515/itit- 2020- 0006 .
[9] D. Tenen, Blunt instrumentalism: On tools and methods, in: M. K. Gold, L. F. Klein (Eds.),
Debates in the Digital Humanities 2016, University of Minnesota Press, 2016.
[10] E. E. H. Vrangbæk, K. L. Nielbo, Composition and change in de ciuitate dei: A case study
of computationally assisted methods, in: Papers presented at the Eighteenth International
Conference on Patristic Studies held in Oxford 2019, Peeters, 2021, pp. 149–164.
[11] Code for corpus, 2023. Available at https://github.com/centre-for-humanities-computing/
computing-antiquity.
[12] Code for parser, 2023. Available at https://github.com/centre-for-humanities-computing/
odyCy.
[13] J. Kostkan, M. Kardos, J. P. B. Mortensen, K. L. Nielbo, Odycy – a general-purpose nlp
pipeline for ancient greek, in: Proceedings of the 7th Joint SIGHUM Workshop on Com-
putational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature,
Association for Computational Linguistics, Dubrovnik, Croatia, 2023, pp. 128–134. Ac-
cessed: Jun. 02, 2023. Available: https://aclanthology.org/2023.latechclfl-1.14.
[14] P. Singh, G. Rutten, E. Lefever, A pilot study for bert language modelling and morphological
analysis for ancient and medieval greek, in: N. R. S. Degaetano-Ortlieb, A. Kazantseva,
S. Szpakowicz (Eds.), Proceedings of the 5th Joint SIGHUM Workshop on Computational
Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Association
for Computational Linguistics, Punta Cana, Dominican Republic (online), 2021, pp. 128–137.
doi:10.18653/v1/2021.latechclfl- 1.15 .
[15] M. Honnibal, I. Montani, S. V. Landeghem, A. Boyd, spacy: Industrial-strength natural
language processing in python, 2020. IEEE, Available: https://doi.org/10.5281/zenodo.
1212303.
[16] L. Buitinck, et al., Api design for machine learning software: experiences from the scikit-
learn project, in: ECML PKDD Workshop: Languages for Data Mining and Machine
Learning, 2013, pp. 108–122.
[17] A. Wendland, M. Zenere, J. Niemann, Introduction to text classification: Impact of stem-
ming and comparing tf-idf and count vectorization as feature extraction technique, in:
R. M. M. Yilmaz, P. Clarke, M. Reiner (Eds.), Communications in Computer and Informa-
tion Science, volume 1442, Springer International Publishing, Cham, 2021, pp. 289–300.
doi:10.1007/978- 3- 030- 85521- 5_19 .
[18] D. D. Mehare, Introduction to tf-idf: To represent importance of keyword within whole
dataset, IJRASET 6 (2018) 2321–2323. doi:10.22214/ijraset.2018.3369 .
[19] T. Sherstinova, O. Mitrofanova, T. Skrebtsova, E. Zamiraylova, M. Kirina, Topic modelling
with nmf vs. expert topic annotation: The case study of russian fiction, in: L. Martínez-
Villasenor, O. Herrera-Alcántara, H. Ponce, F. A. Castro-Espinoza (Eds.), Advances in
Computational Intelligence, volume 12026 of Lecture Notes in Computer Science, Springer
International Publishing, Cham, 2020, pp. 134–151. doi:10.1007/978- 3- 030- 608 .
[20] P. Kherwa, P. Bansal, Topic modeling: A comprehensive review, EAI Endorsed Transactions
on Scalable Information Systems 7 (2019). Accessed: Mar. 12, 2024. Available: https:
//eudl.eu/doi/10.4108/eai.13-7-2018.159623.
[21] M. Gillings, A. Hardie, The interpretation of topic models for scholarly analysis: An
evaluation and critique of current practice, Digital Scholarship in the Humanities 38 (2023)
530–543. doi:10.1093/llc/fqac075 .
[22] D. D. Lee, H. S. Seung, Learning the parts of objects by non-negative matrix factorization,
Nature 401 (1999) 788–791. doi:10.1038/44565 .
[23] J. Albrecht, S. Ramachandran, C. Winkler, Blueprints for Text Analytics Using Python:
Machine Learning-Based Solutions for Common Real World (NLP) Applications, 1st ed.,
O’Reilly Media, Sebastopol, CA, 2021.
[24] N. Gillis, The why and how of nonnegative matrix factorization (2014). Available: http:
//arxiv.org/abs/1401.5226.
[25] S. Vajjala, B. Majumder, A. Gupta, H. Surana, Practical Natural Language Processing: A
Comprehensive Guide to Building Real-World NLP Systems, 1st ed., O’Reilly Media, Beijing
Boston Farnham Sebastopol Tokyo, 2020.
[26] J. Chang, S. Gerrish, C. Wang, J. L. Boyd-Graber, D. M. Blei, Reading tea leaves: How
humans interpret topic models, in: Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams,
A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (NIPS 2009),
2009, pp. 288–296.
[27] Code for topic modelling, 2023. Available at https://github.com/ankyloHryax/
classic-topic.
[28] F. Cairns, An early byzantine pseudepigraphon: the apocryphal acta barnabae, Byzan-
tinische Zeitschrift 112 (2019) 47–66. doi:10.1515/bz- 2019- 0004 .