We investigate large language models (LLMs) for cross-lingual abstractive keyphrase generation from historical newspapers. The task consists of producing a small set of English keyphrases for articles written in German, French, and Luxembourgish, combining translation, abstraction, and normalization. We conduct a human-centered pilot study comparing model outputs using human selections, LLM-as-judge assessments, and inter-annotator agreement analysis, followed by a medium-scale application to multilingual data from the Impresso corpus. Results show that LLM-generated keyphrases can support semantic enrichment and exploratory analysis of historical collections, while highlighting the subjective and methodologically challenging nature of keyphrase evaluation.

This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.