We present a framework for detecting and categorizing noise in literary texts, demonstrated through its application to Danish and Norwegian literature from the late 19-th century. Noise, understood as “aberrant sonic behaviour,” is not only an auditory phenomenon but also a cultural construct tied to the processes of civilization and urbanization.We begin by utilizing topic modeling techniques to identify noise-related documents, followed by fine-tuning BERT-based language models trained on Danish and Norwegian texts to analyze a corpus of over 800 novels.We identify and track the prevalence of noise in these texts, offering insights into the literary perceptions of noise during the Scandinavian “Modern Breakthrough” period (1870-1899). Our contributions include the development of a comprehensive dataset annotated for noise-related segments and their categorization into human-made, non-human-made, and musical noises. This study illustrates the framework’s potential for enhancing the understanding of the relationship between noise and its literary representations, providing a deeper appreciation of the auditory elements in literary works, including as sources for cultural history.

We develop and evaluate the first pre-trained language models specifically tailored for historical Danish and Norwegian texts. Three models are trained on a corpus of 19th-century Danish and Norwegian literature: two directly on the corpus with no prior pre-training, and one with continued pre-training. To evaluate the models, we utilize an existing sentiment classification dataset, and additionally introduce a new annotated word sense disambiguation dataset focusing on the concept of fate. Our assessment reveals that the model employing continued pre-training outperforms the others in two downstream NLP tasks on historical texts. Specifically, we observe substantial improvement in sentiment classification and word sense disambiguation compared to models trained on contemporary texts. These results highlight the effectiveness of continued pre-training for enhancing performance across various NLP tasks in historical text analysis.