We introduce a simple concept tracking approach to support conceptual history research. Building on the existing practices of conceptual historians, we use dictionaries to identify “anchors”, which represent primary dimensions of meaning of a concept. Then, we create a plot showing how a key concept has evolved over time in a historical corpus in relation to these dimensions. We demonstrate the approach by plotting the change of several key concepts in the COHA corpus.

This paper makes the case for studying concreteness in language as a bridge that will allow language technology to support the understanding and improvement of ethnic inclusivity in job advertisements. We propose an annotation scheme that guides the assignment of sentences in job ads to classes that reflect concrete actions, i.e., what the employer needs people to do, and abstract dispositions, i.e., who the employer expects people to be. Using an annotated dataset of Dutch-language job ads, we demonstrate that machine learning technology is effectively able to distinguish these classes.