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.

翻译：将计算技术与人文科学相结合是一项持续的努力，旨在使文本、图像、音频、视频及其他资料实现数字化、可检索和可分析。近年来，深度神经网络在自动文本分析与自然语言处理领域占据主导地位，某些情况下甚至展现出超越人类的表现。深度神经网络是解决数字人文研究中诸多自然语言处理任务的最先进机器学习算法，包括拼写检查、语言检测、实体抽取、作者识别、问答系统等。这类监督学习算法从大量"正确"与"错误"示例中学习模式，并将其应用于新示例。然而，将深度神经网络用于数字人文研究的文本资源分析面临两大挑战：训练数据的（不可）获取性及领域适配需求。本文通过分析近期文献中的多个数字人文研究用例及其可能解决方案来探讨这些挑战，并为数字人文专家构建了关于何时及如何选择合适深度学习方法的实用决策模型。此外，本文旨在提升数字人文社群对深度学习模型应用价值的认知。