Huminfra handbook: Empowering digital and experimental humanities

Selle kollektsiooni püsiv URIhttps://hdl.handle.net/10062/117324

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A machine learning pipeline for digitalising historical printed materials – from data collection to a searchable database
(University of Tartu Library, 2025-11) Pablo, Dalia Ortiz; Badri, Sushruth; Aangenendt, Gijs; von Bychelberg, Mo ; Lindström, Matts; Bouma, Gerlof; Dannélls, Dana; Kokkinakis, Dimitrios; Volodina, Elena
Recent developments in the fields of machine learning and computer vision have created new opportunities for the digitalisation of printed historical materials. However, successful integration of machine learning models requires interdisciplinary collaboration between computer- and data scientists, researchers, librarians and/or archivists, and digitisation experts. This chapter describes a comprehensive pipeline designed to address the challenges of digitalising printed historical materials, from document-scanning best practices to incorporating state-of-the-art machine learning techniques. It aims to streamline the management and processing of historical data, making the digitalised materials accessible and searchable through the application of machine learning techniques. The content of this chapter encompasses scanning best practices, annotation approaches, model training, and deployment. This chapter presents a collection of useful tools for each stage of building a machine learning model, step-by-step instructions and example notebooks designed to be easily adapted to other cases.
Applied NLP for humanities research
(University of Tartu Library, 2025-11) Aangenendt, Gijs; Skeppstedt, Maria; Berglund, Karl; Bouma, Gerlof; Dannélls, Dana; Kokkinakis, Dimitrios; Volodina, Elena
Natural language processing (NLP) has become a field of interest for many researchers within the humanities. However, framing humanities research questions as NLP problems and identifying suitable methods can be a difficult task. Taking previous and ongoing projects from the Centre for Digital Humanities and Social Sciences at Uppsala University (CDHU) as a point of departure, this chapter presents concrete use cases of how humanities research questions can be approached using various NLP methods and tools, from ready-to use text analysis tools to programming libraries that require basic familiarity with Python. Two case studies from the field of history and literature will be introduced to illuminate how texts can be processed for humanities research purposes. With this chapter, we hope to give the reader the means to directly explore NLP methods for their research as well as encourage further learning.
The Word Rain visualisation technique applied to digital history: How to visualise, explore and compare texts using semantically structured word Clouds
(University of Tartu Library, 2025-11) Skeppstedt, Maria; Ahltorp, Magnus; Kucher, Kostiantyn; Aangenendt, Gijs; Lindström, Matts; Söderfeldt, Ylva; Bouma, Gerlof; Dannélls, Dana; Kokkinakis, Dimitrios; Volodina, Elena
The Word Rain text visualisation technique aims to retain the simplicity of the classic word cloud, while addressing some of its limitations. In particular, the Word Rain visualisation uses word embeddings to automatically give the visualised words a semantically meaningful position along the horizontal axis. In this handbook chapter, we showcase how this novel approach for word positioning makes the Word Rain technique suitable for exploring, analysing and comparing texts. More specifically, we show how the Word Rain Python module can be used to visualise longitudinal changes in periodicals published by the Swedish Diabetes Association, and how the Word Rain web service can be used to create visualisations that compare the patient organisation periodicals to journals published by the Swedish Medical Association.

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Sirvi Huminfra handbook: Empowering digital and experimental humanities Autor "Aangenendt, Gijs" järgi