Ricerca
Risultati della ricerca
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Dataset
Datasets for toponym recognition and disambiguation for nineteenth-century English newspapers
We present two datasets, one for the task of toponym recognition and one for the task of toponym disambiguation. The datasets are derived from the "Dataset for Toponym Resolution in Nineteenth-Century English Newspapers" (DOI: https://doi.org/10.23636/r7d4-kw08). The toponym recognition dataset consists of two JSON files (ner_fine_train.json and ner_fine_dev.json), whereas the toponym...Coll Ardanuy, Mariona ; Nanni, Federico
toponym disambiguation, nineteenth-century newspapers, named entity recognition, entity linking, toponym resolution, toponym recognition, and dataset
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Dataset
DeezyMatch training set for OCR
Optical character recognition (OCR) is the process of automatically transcribing text from images. The presence of OCR-induced errors in digitised text is a common problem in the digital humanities. OCR errors are usually due to the misrecognition of characters, such as "h" recognised as "b", or "c" recognised as "o".... -
Conference paper (unpublished)
(Re)investing in a national repository infrastructure for cultural heritage
Since 2018, the British Library (BL) has invested considerable resource in establishing the necessary infrastructure for a national repository service for cultural heritage organisations, using Samvera Hyku. This has entailed working closely with all known Hyku suppliers and developers, as well as collaborating with the University of Virginia on an...Basford, Jenny ; Holt, Ilkay ; Jevon, Graham ; Ramsey, Nora
open access, OR2023, and repository
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Dataset
Diachronic word embeddings from 19th-century newspapers digitised by the British Library (1800-1919)
Word vectors related to the paper "Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers" by Nilo Pedrazzini and Barbara McGillivray (2022). The embeddings were trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and specific parameters. The embeddings are divided into...Pedrazzini, Nilo ; McGillivray, Barbara
historical semantics, word-vectors, late-modern-english, newspapers, diachronic-embeddings, and word2vec