Annotating terms referring to aspects of disability in historical texts is crucial for understanding how societies in different periods conceptualized and treated disability. Such annotations help modern readers grasp the evolving language, cultural attitudes, and social structures surrounding disability, shedding light on both marginalization and inclusion throughout history. This is important as evolving societal attitudes can influence the perpetuation of harmful language that reinforces stereotypes and discrimination. However, this task presents significant challenges. Terminology often reflects outdated, offensive, or ambiguous concepts that require sensitive interpretation. Meaning of terms may have shifted over time, making it difficult to align historical terms with contemporary understandings of disability. Additionally, contextual nuances and the lack of standardized language in historical records demand careful scholarly judgment to avoid anachronism or misrepresentation.

Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.

Scalar implicatures are language features that imply the negation of stronger statements, e.g., “She was married twice” typically implicates that she was not married thrice. In this paper we discuss the importance of scalar implicatures in the context of textual information extraction. We investigate how textual features can be used to predict whether a given text segment mentions all objects standing in a certain relationship with a certain subject. Preliminary results on Wikipedia indicate that this prediction is feasible, and yields informative assessments.