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This article examines a long-standing question in the history of technology concerning the trope of the living machine. The authors do this by using a cutting-edge computational method, which they apply to large collections of digitized texts. In particular, they demonstrate the affordances of a neural language model for historical research. In a deliberate maneuver, the authors use a type of model, often portrayed as sentient today, to detect figures of speech in nineteenth-century texts that portrayed machines as self-acting, automatic, or alive. Their masked language model detects unusual or surprising turns of phrase, which could not be discovered using simple keyword search. The authors collect and close read such sentences to explore how figurative language produced a context that conceived humans and machines as interchangeable in complicated ways. They conclude that, used judiciously, language models have the potential to open up new avenues of historical research. [ABSTRACT FROM AUTHOR]

We present a new dataset for the task of toponym resolution in digitized historical newspapers in English. It consists of 343 annotated articles from newspapers based in four different locations in England (Manchester, Ashton-under-Lyne, Poole and Dorchester), published between 1780 and 1870. The articles have been manually annotated with mentions of places, which are linked—whenever possible—to their corresponding entry on Wikipedia. The dataset consists of 3,364 annotated toponyms, of which 2,784 have been provided with a link to Wikipedia. The dataset is published in the British Library shared research repository, and is especially of interest to researchers working on improving semantic access to historical newspaper content. [ABSTRACT FROM AUTHOR]

We present four types of neural language models trained on a large historical dataset of books in English, published between 1760 and 1900, and comprised of ?5.1 billion tokens. The language model architectures include word type embeddings (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the type embeddings, and four instances considering different time slices for BERT. Our models have already been used in various downstream tasks where they consistently improved performance. In this paper, we describe how the models have been created and outline their reuse potential. [ABSTRACT FROM AUTHOR]