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.

Text analysis using deep neural networks in digital humanities and information science

Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study develops a character social network relationship map tool (CSNRMT) that can semi-automatically assist digital humanists through human-computer interaction to more efficiently and accurately explore the character social network relationships from Chinese ancient texts for useful research findings.,With a counterbalanced design, semi-structured in-depth interview, and lag sequential analysis, a total of 21 research subjects participated in an experiment to examine the system effectiveness and technology acceptance of adopting the ancient book digital humanities research platform with and without the CSNRMT to interpret the characters and character social network relationships.,The experimental results reveal that the experimental group with the CSNRMT support appears higher system effectiveness on the interpretation of characters and character social network relationships than the control group without the CSNRMT, but does not achieve a statistically significant difference. Encouragingly, the experimental group with the CSNRMT support presents remarkably higher technology acceptance than the control group without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could assist digital humanists in the interpretation of character social network relationships. The results of the interview present positive opinions on the integration of system interface, smoothness of operation, and external search function.,Currently, the system effectiveness of exploring the character social network relationships from texts for useful research findings by using the CSNRMT developed in this study will be significantly affected by the accuracy of recognizing character names and character social network relationships from Chinese ancient texts. The developed CSNRMT will be more practical when the offered information about character names and character social network relationships is more accurate and broad.,This study develops an ancient book digital humanities research platform with an emerging CSNRMT that provides an easy-to-use real-time interaction interface to semi-automatically support digital humanists to perform digital humanities research with the need of exploring character social network relationships.,At present, a real-time social network analysis tool to provide a friendly interaction interface and effectively assist digital humanists in the digital humanities research with character social networks analysis is still lacked. This study thus presents the CSNRMT that can semi-automatically identify character names from Chinese ancient texts and provide an easy-to-use real-time interaction interface for supporting digital humanities research so that digital humanists could more efficiently and accurately establish character social network relationships from the analyzed texts to explore complicated character social networks relationship and find out useful research findings.

A character social network relationship map tool to facilitate digital humanities research

Humanities researchers are faced with an overwhelming volume of digitised
primary source material, and "born digital" information, of relevance to their
research as a result of large-scale digitisation projects. The current digital tools
do not provide consistent support for analysing the content of digital archives
that are potentially large in scale, multilingual, and come in a range of data
formats. The current language-dependent, or project specific, approach to tool
development often puts the tools out of reach for many research disciplines in
the humanities. In addition, the tools can be incompatible with the way
researchers locate and compare the relevant sources. For instance, researchers
are interested in shared structural text patterns, known as \parallel passages"
that describe a specific cultural, social, or historical context relevant to their
research topic. Identifying these shared structural text patterns is challenging
due to their repeated yet highly variable nature, as a result of differences in
the domain, author, language, time period, and orthography.
The contribution of the thesis is a novel infrastructure that directly addresses
the need for generic, 
flexible, extendable, and sustainable digital tools
that are applicable to a wide range of digital archives and research in the
humanities. The infrastructure adopts a character-level n-gram Statistical
Language Model (SLM), stored in a space-optimised k-truncated suffix tree
data structure as its underlying data model. A character-level n-gram model
is a relatively new approach that is competitive with word-level n-gram models,
but has the added advantage that it is domain and language-independent,
requiring little or no preprocessing of the document text unlike word-level
models that require some form of language-dependent tokenisation and stemming.
Character-level n-grams capture word internal features that are ignored
by word-level n-gram models, which provides greater 
exibility in addressing
the information need of the user through tolerant search, and compensation
for erroneous query specification or spelling errors in the document text. Furthermore,
the SLM provides a unified approach to information retrieval and
text mining, where traditional approaches have tended to adopt separate data
models that are often ad-hoc or based on heuristic assumptions. In addition,
the performance of the character-level n-gram SLM was formally evaluated
through crowdsourcing, which demonstrates that the retrieval performance of
the SLM is close to that of the human level performance.
The proposed infrastructure, supports the development of the Samtla (Search
And Mining Tools for Language Archives), which provides humanities researchers
digital tools for search, browsing, and text mining of digital archives
in any domain or language, within a single system. Samtla supersedes many of
the existing tools for humanities researchers, by supporting the same or similar
functionality of the systems, but with a domain-independent and languageindependent
approach. The functionality includes a browsing tool constructed
from the metadata and named entities extracted from the document text, a
hybrid-recommendation system for recommending related queries and documents.
However, some tools are novel tools and developed in response to
the specific needs of the researchers, such as the document comparison tool
for visualising shared sequences between groups of related documents. Furthermore,
Samtla is the first practical example of a system with a SLM as
its primary data model that supports the real research needs of several case
studies covering different areas of research in the humanities.

The anatomy of a search and mining system for digital humanities : Search And Mining Tools for Language Archives (SAMTLA)

A new method for grouping manuscripts in clusters is presented with the calculation of distances between readings, then between witnesses. A classification algorithm (" Hierarchical Ascendant Clustering "), achieved through computer-aided processing, enables the construction of trees illustrating the textual taxonomy obtained. This method is applied to the Old Latin witnesses of the Gospel of John, and, in order to provide a study of a reasonable size, to a chapter as a whole (chapter 14). The result basically confirms the text-types identified by Bonatius Fischer, founder of the Vetus Latina Institute, while it invalidates the classification adopted by the current edition of the Vetus Latina of the Gospel of John.

A Classification of Manuscripts Based on A New Quantitative Method. The Old Latin Witnesses of John's Gospel as Text Case

Biographies constitute the main historical record of China. The China Biographical Database (CBDB) is an important project that tackles this vast biographical material with digital technologies. With both online and offline versions, CBDB is meant to be useful for statistical, social network, and spatial analysis, as well as serving as biographical reference. Through the wide range of data it collects through mining historical texts and reference sources, CBDB offers multiple ways to examine the lives of past groups and individuals in Chinese history. The use of CBDB data for prosopographical and other types of analysis has generated important work that interprets Chinese history in new ways, and has also fostered new forms of digital humanities collaborations. This article introduces the history of the CBDB project and its methods for populating its biographical data. It also presents the ways that historians and other scholars could utilize its data for research and teaching.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/066593EBDCEDBADD5FCCE968D64EA76E/S2059163220000213a.pdf/div-class-title-harvesting-big-biographical-data-for-chinese-history-the-china-biographical-database-cbdb-div.pdf

Harvesting Big Biographical Data for Chinese History: The China Biographical Database (CBDB)

With the rapid development of digital humanities, some digital humanities platforms have been successfully developed to support digital humanities research for humanists. However, most of them have still not provided a friendly digital reading environment and practicable social network analysis tool to support humanists on interpreting texts and exploring characters’ social network relationships. Moreover, the advancement of digitization technologies for the retrieval and use of Chinese ancient books is arising an unprecedented challenge and opportunity. For these reasons, this paper aims to present a Chinese ancient books digital humanities research platform (CABDHRP) to support historical China studies. In addition to providing digital archives, digital reading, basic search and advanced search functions for Chinese ancient books, this platform still provides two novel functions that can more effectively support digital humanities research, including an automatic text annotation system (ATAS) for interpreting texts and a character social network relationship map tool (CSNRMT) for exploring characters’ social network relationships.,This study adopted DSpace, an open-source institutional repository system, to serve as a digital archives system for archiving scanned images, metadata, and full texts to develop the CABDHRP for supporting digital humanities (DH) research. Moreover, the ATAS developed in the CABDHRP used the Node.js framework to implement the system’s front- and back-end services, as well as application programming interfaces (APIs) provided by different databases, such as China Biographical Database (CBDB) and TGAZ, used to retrieve the useful linked data (LD) sources for interpreting ancient texts. Also, Neo4j which is an open-source graph database management system was used to implement the CSNRMT of the CABDHRP. Finally, JavaScript and jQuery were applied to develop a monitoring program embedded in the CABDHRP to record the use processes from humanists based on xAPI (experience API). To understand the research participants’ perception when interpreting the historical texts and characters’ social network relationships with the support of ATAS and CSNRMT, semi-structured interviews with 21 research participants were conducted.,An ATAS embedded in the reading interface of CABDHRP can collect resources from different databases through LD for automatically annotating ancient texts to support digital humanities research. It allows the humanists to refer to resources from diverse databases when interpreting ancient texts, as well as provides a friendly text annotation reader for humanists to interpret ancient text through reading. Additionally, the CSNRMT provided by the CABDHRP can semi-automatically identify characters’ names based on Chinese word segmentation technology and humanists’ support to confirm and analyze characters’ social network relationships from Chinese ancient books based on visualizing characters’ social networks as a knowledge graph. The CABDHRP not only can stimulate humanists to explore new viewpoints in a humanistic research, but also can promote the public to emerge the learning interest and awareness of Chinese ancient books.,This study proposed a novel CABDHRP that provides the advanced features, including the automatic word segmentation of Chinese text, automatic Chinese text annotation, semi-automatic character social network analysis and user behavior analysis, that are different from other existed digital humanities platforms. Currently, there is no this kind of digital humanities platform developed for humanists to support digital humanities research.

Chung Chang

Papers

A Chinese ancient book digital humanities research platform to support digital humanities research