This paper addresses the challenge of detecting anthropomorphic language in AI research. We introduce AnthroSet, a novel dataset of 600 manually annotated utterances covering various linguistic structures. Through the evaluation of two current approaches for anthropomorphism and atypical animacy detection, we highlight the limitations of a masked language model approach, arising from masking constraints as well as increasingly anthropomorphizing AI-related terminology. Our findings underscore the need for more targeted methods and a robust definition of anthropomorphism.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.