Detection of Citations and Textual Reuse on Ancient Greek Texts and its Applications in the Classical Studies: eAQUA Project

"Users of this or any edition are warned that
the textual variants presented by citations from
Plato in later literature have not yet been as fully
investigated as is desirable". This shortcoming,
characterized by Kenneth Dover (Dover, 1980)
is still existent and is unlikely to be corrected
quickly by traditional research techniques.
Textual reuse plays an important role in
Classical Studies research. Similar to modern
publications, classical authors used the texts of
others as sources for their own work. In ancient
texts, however, a less stronger form of word by
word citation can be observed. Additionally, the
complexity of ancient resources disallows fully
manual research.
From a bird's eye view there are different
points of view to the problem of textual reuse
implying different research interests (Büchler
and Geßner, 2009):
-
A
Computer Science
perspective focuses on
algorithms (
technical view
): Which algorithm
is better than others? The scope of this
research is wide ranging and also relates
to plagiarism detection in modern texts like
theses at universities (Potthast et al., 2009).
-
A
Historian
is interested in more complex
correlations (
macro view
). For this kind of
work a dedicated user interface is necessary to
figure out relations between e.g. chapters of a
book and their citation usage on a timeline.
-
The research interests of a
Classical
Philologist
focus on the textual differences
between the original text and its variants
in citations (
micro view
). These varying
requirements necessitate designing different
user interfaces for these three kinds of
researchers.
Within the eAQUA project we are investigating
the reception of Plato as a case study of textual
reuse in ancient Greek texts. Our research is
carried out in two steps. On the
technical level
,
we firstly extract word by word citations. This
is achieved by combining syntactical ngram
overlappings (Hose, 2009 and Büchler, 2008)
and significant terms for several of Plato's
works. In the second step the constraints on
syntactic word order are relaxed. This is done
by combining text mining and information
retrieval techniques. A graph based approach
is then introduced that can deal with free
word order citations. The key concept is not
syntactically based, but focuses on the semantic
level to extract the relevant
core information
of a used citation. Then the information is
represented as a formal graph that is similar to
the
Lexical Chaining
approach (Waltinger et al.
2008) that is often used for text summarisation
(Yu et al. 2007). On the one hand syntactical
and semantic approaches are only used to select
reuse candidates with a small set of uncommon
matching words within a citation. On the
other hand, a complete pairwise comparison
of all of the nearly 5.5 million sentences in
the TLG corpus would require approximately
1000 years due to the squared complexity of
O(n
2
) that was used for example to compare
the Dead Sea Scrolls with the Hebrew Bible
(Hose, 2009). For this reason, an intelligent
pre-clustering of relevant reuse candidates is
needed. Such a divide and conquer strategy
reduces the complexity dramatically. Whilst the

2
second step only increases the degree of free
word order, in the third step the algorithm is
expanded by similarly used words like
go
and
walk
. Those candidates are computed by similar
cooccurrence profiles. The three levels briefly
described above are only one dimension of reuse
exploration. Other relevant dimensions that will
be discussed are the
degree of preprocessing
as
well as the
visualisation
of textual reuse in terms
of citations.
In the field of preprocessing the main focus
lies on
tokenisation
(more active tokenisation
is needed with ancient texts than on modern
languages),
normalisation
(reducing all words
internally to a lower-case representation
without diacritics) and
lemmatisation
(reducing
all words internally to a word's base form).
This dimension can speed up the algorithm and
also improves the results for strongly inflected
languages like Ancient Greek.
Leaving the technical point of view of computer
scientists, the research of Classicists includes
both an application of a
macro view
for
Historians as well as one for the
micro
view
of Classical Philologists. The visualisation
dimension of textual reuse is important since
text mining approaches typically generate a
huge amount of data that can't be explored
manually. This is shown in Fig. 1. Whilst the
light grey area marks Neoplatonism (about 5.
AC) the grey ranges highlight Middle Platonism
(about 2. AC). Taking Plato's
Timaeus
, one
can clearly identify that both phases of Plato's
reception (see Fig. 1 – top) are based on different
"chapters" of
Timaeus
(bottom).
Fig. 1. Macro view: Screen of an interactive visualisation
for citation usage. Citation distribution by Stephanus
pages of Plato's Timaeus. The highest peak of the first
picture is strongly correlated with the citation usage of
the pages 27 to 42 of the second picture: Neo Platonism.
As Fig. 1 is of stronger interest for Historians,
there is also a requirement for a visualisation
for researchers from the field of Classical Greek
Philology. As shown in Fig. 2, a visualisation
highlighting the differences in citation usage is
necessary. This is especially important if longer
citations are investigated.
Fig. 2. Micro view: Highlighted differences of citations
(green, orange) in relation to original text of Plato (blue).
Top: The orange word highlights the same word but
including a language evolution of about 10 centuries.
Bottom: An included word (orange) in the citation is shown.
Additionally, it will be demonstrated how to
detect different editions of the same original
text. Such completely unsupervised approaches
are important to investigate the scientific
landscape of text digitisation. Furthermore, the
relation to modern plagiarism detection will
be given as well as the importance of building
modern representative corpora since especially
web corpora typically contain several duplicates
of the same text.