No description available.

No description available.

Der Beitrag beschreibt ein laufendes Projekt zur computergestützten Erschließung und Analyse einer großen Sammlung handschriftlicher Liedblätter mit Volksliedern aus dem deutschsprachigen Raum. Am Beispiel dieses praktischen Projekts werden Chancen und Herausforderungen diskutiert, die der Einsatz von Digital Humanities-Methoden für den Bereich der Musikwissenschaft mit sich bringt.

No description available.

No description available.

In this short paper we present work in progress that tries to leverage crowdsourced music metadata and crowdsourced affective word norms to create a comprehensive dataset of music emotions, which can be used for sentiment analyses in the music domain. We combine a mixture of different data sources to create a new dataset of 90,408 songs with their associated embeddings in Russell’s model of affect, with the dimensions valence, dominance and arousal. In addition, we provide a Spotify ID for the songs, which can be used to add more metadata to the dataset via the Spotify API.

While historical newspapers recently have gained a lot of attention in the digital humanities, transforming them into machine-readable data by means of OCR poses some major challenges. In order to address these challenges, we have developed an end-to-end OCR pipeline named Origami. This pipeline is part of a current project on the digitization and quantitative analysis of the German newspaper “Berliner Börsen-Zeitung” (BBZ), from 1872 to 1931. The Origami pipeline reuses existing open source OCR components and on top offers a new configurable architecture for layout detection, a simple table recognition, a two-stage X-Y cut for reading order detection, and a new robust implementation for document dewarping. In this paper we describe the different stages of the workflow and discuss how they meet the above-mentioned challenges posed by historical newspapers.

No description available.

Digital tools have played an important role in Digital Humanities (DH) since its beginnings. Accordingly, a lot of research has been dedicated to the documentation of tools as well as to the analysis of their impact from an epistemological perspective. In this paper we propose a binary and a multi-class classification approach to detect and classify tools. The approach builds on state-of-the-art neural language models. We test our model on two different corpora and report the results for different parameter configurations in two consecutive experiments. In the end, we demonstrate how the models can be used for actual tool detection and tool classification tasks in a large corpus of DH journals.

Cited-works-lists in Humanities dissertations are typically the result of five years of work. However, despite the long-standing tradition of reference mining, no research has systematically untapped the bibliographic data of existing electronic thesis collections. One of the main reasons for this is the difficulty of creating a tagged gold standard for the around 300 pages long theses. In this short paper, we propose a page-based random forest (RF) prediction approach which uses a new corpus of Literary Studies Dissertations from Germany. Moreover, we will explain the handcrafted but computationally informed feature-selection process. The evaluation demonstrates that this method achieves an F1 score of 0.88 on this new dataset. In addition, it has the advantage of being derived from an interpretable model, where feature relevance for prediction is clear, and incorporates a simplified annotation process.