  Combining computational technologies and humanities is an ongoing effort
aimed at making resources such as texts, images, audio, video, and other
artifacts digitally available, searchable, and analyzable. In recent years,
deep neural networks (DNN) dominate the field of automatic text analysis and
natural language processing (NLP), in some cases presenting a super-human
performance. DNNs are the state-of-the-art machine learning algorithms solving
many NLP tasks that are relevant for Digital Humanities (DH) research, such as
spell checking, language detection, entity extraction, author detection,
question answering, and other tasks. These supervised algorithms learn patterns
from a large number of "right" and "wrong" examples and apply them to new
examples. However, using DNNs for analyzing the text resources in DH research
presents two main challenges: (un)availability of training data and a need for
domain adaptation. This paper explores these challenges by analyzing multiple
use-cases of DH studies in recent literature and their possible solutions and
lays out a practical decision model for DH experts for when and how to choose
the appropriate deep learning approaches for their research. Moreover, in this
paper, we aim to raise awareness of the benefits of utilizing deep learning
models in the DH community.
