We examine the relationship between the canonization of Danish novels and their textual innovation and influence, taking the Danish Modern Breakthrough era (1870–1900) as a case study. We evaluate whether canonical novels introduced a significant textual novelty in their time, and explore their influence on the overall literary trend of the period. By analyzing the positions of canonical versus non-canonical novels in semantic space, we seek to better understand the link between a novel’s canonical status and its literary impact. Additionally, we examine the overall diversification of Modern Breakthrough novels during this significant period of rising literary readership. We find that canonical novels stand out from both the historical novel genre and non-canonical novels of the period. Our findings on diversification within and across groups indicate that the novels now regarded as canonical served as literary trendsetters of their time.

While Sentiment Analysis has become increasingly central in computational approaches to literary texts, the literary domain still poses important challenges for the detection of textual sentiment due to its highly complex use of language and devices - from subtle humor to poetic imagery. Furthermore these challenges are only further amplified in low-resource language and domain settings. In this paper we investigate the application and efficacy of different Sentiment Analysis tools on Danish literary texts, using historical fairy tales and religious hymns as our datasets. The scarcity of linguistic resources for Danish and the historical context of the data further compounds the challenges for the tools. We compare human annotations to the continuous valence scores of both transformer- and dictionary-based Sentiment Analysis methods to assess their performance, seeking to understand how distinct methods handle the language of Danish prose and poetry.

This paper presents a general-purpose NLP pipeline that achieves state-of-the-art performance on the Ancient Greek Perseus UD Treebank for several tasks (POS Tagging, Morphological Analysis and Dependency Parsing), and close to state-of-the-art performance on the Proiel UD Treebank. Our aim is to provide a reproducible, open source language processing pipeline for Ancient Greek, capable of handling input texts of varying quality. We measure the performance of our model against other comparable tools and then evaluate lemmatization errors.