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Analyzing poetry with automatic tools has great potential for improving verse-related research. Over the last few decades, this field has expanded notably and a large number of tools aiming at analyzing various aspects of poetry have been developed. However, the concrete connection between these tools and traditional scholars investigating poetry and metrics is often missing. The purpose of this article is to bridge this gap by providing a comprehensive survey of the automatic poetry analysis tools available for European languages. The tools are described and classified according to the language for which they are primarily developed, and to their functionalities and purpose. Particular attention is given to those that have open-source code or provide an online version with the same functionality. Combining more traditional research with these tools has clear advantages: it provides the opportunity to address theoretical questions with the support of large amounts of data; also, it allows for the development of new and diversified approaches. [ABSTRACT FROM AUTHOR]

Abstract Understanding the nature–culture entanglement by combining the methods of natural sciences and humanities is little approached in neither of the fields. With a specific combination of methods from both digital humanities and ecology, we aimed at identifying several of people's life circumstances that relate to their individual sensitivity towards biodiversity. The circumstances with a strong correlation could be considered and targeted by decision‐makers, for example by developing specific education programmes for making people more eco‐conscious or adjusting relevant regulations. We applied machine learning techniques onto a database including information about the frequency of biodiversity mentioned in creative literature (BiL) from 1705 to 1969 as response variable related to metadata about the corresponding works and their authors as predictors, including localisation, age, gender and literature genre. The algorithm determined the response's dependency on each predictor, which can be interpreted as the intensity of this particular sensitivity parameter for biodiversity, and which we also related to time. We recognised that gender, age, region and settlement size are predictors significantly correlated to BiL. Statistically, these predictors can be viewed as starting points of the eventual individual level of awareness for biodiversity. For example, authors from villages exhibit a higher BiL than those from cities, which we interpret as a signal for the dependence of awareness for biodiversity on spatial distance from nature, which in turn can be addressed in urban development. Our conclusion is that applying a machine learning technique on literary data yields meaningful results, thereby showing potential for further similar investigations and the combination of methods from natural sciences and humanities to achieve so far unattainable insights. With our study, these insights could contribute to ecologically based decision‐making processes. Read the free Plain Language Summary for this article on the Journal blog.

Understanding the nature–culture entanglement by combining the methods of natural sciences and humanities is little approached in neither of the fields. With a specific combination of methods from both digital humanities and ecology, we aimed at identifying several of people's life circumstances that relate to their individual sensitivity towards biodiversity. The circumstances with a strong correlation could be considered and targeted by decision‐makers, for example by developing specific education programmes for making people more eco‐conscious or adjusting relevant regulations.We applied machine learning techniques onto a database including information about the frequency of biodiversity mentioned in creative literature (BiL) from 1705 to 1969 as response variable related to metadata about the corresponding works and their authors as predictors, including localisation, age, gender and literature genre. The algorithm determined the response's dependency on each predictor, which can be interpreted as the intensity of this particular sensitivity parameter for biodiversity, and which we also related to time.We recognised that gender, age, region and settlement size are predictors significantly correlated to BiL. Statistically, these predictors can be viewed as starting points of the eventual individual level of awareness for biodiversity. For example, authors from villages exhibit a higher BiL than those from cities, which we interpret as a signal for the dependence of awareness for biodiversity on spatial distance from nature, which in turn can be addressed in urban development.Our conclusion is that applying a machine learning technique on literary data yields meaningful results, thereby showing potential for further similar investigations and the combination of methods from natural sciences and humanities to achieve so far unattainable insights. With our study, these insights could contribute to ecologically based decision‐making processes. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

What do characters in theater plays know about character relations, and how does the distribution of knowledge evolve over the course of a play? We present a dataset of 30 German plays annotated with information about the distribution of knowledge about character relations (such as "A learns from B that C is the parent of D"). All plays were manually annotated by two independent annotators as part of the Q:TRACK research project, which aims to systematically model character knowledge. The dataset is available on GitHub and Zenodo and can be reused, for example, for systematic studies of knowledge in plays or for analyzing disagreements between annotators.