  Alterations in historical manuscripts such as letters represent a promising
field of research. On the one hand, they help understand the construction of
text. On the other hand, topics that are being considered sensitive at the time
of the manuscript gain coherence and contextuality when taking alterations into
account, especially in the case of deletions. The analysis of alterations in
manuscripts, though, is a traditionally very tedious work. In this paper, we
present a machine learning-based approach to help categorize alterations in
documents. In particular, we present a new probabilistic model (Alteration
Latent Dirichlet Allocation, alterLDA in the following) that categorizes
content-related alterations. The method proposed here is developed based on
experiments carried out on the digital scholarly edition Berlin Intellectuals,
for which alterLDA achieves high performance in the recognition of alterations
on labelled data. On unlabelled data, applying alterLDA leads to interesting
new insights into the alteration behavior of authors, editors and other
manuscript contributors, as well as insights into sensitive topics in the
correspondence of Berlin intellectuals around 1800. In addition to the findings
based on the digital scholarly edition Berlin Intellectuals, we present a
general framework for the analysis of text genesis that can be used in the
context of other digital resources representing document variants. To that end,
we present in detail the methodological steps that are to be followed in order
to achieve such results, giving thereby a prime example of an Machine Learning
application the Digital Humanities.
