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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".... -
Journal article
A Dataset for Toponym Resolution in Nineteenth-Century English Newspapers
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... -
Conference paper (published)
Living Machines: A study of atypical animacy
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,...Coll Ardanuy, Mariona ; Nanni, Federico ; Beelen, Kaspar ; Hosseini, Kasra ; Ahnert, Ruth …
nineteenth-century English, living machines, BERT, and animacy
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Research report
Data Study Group Final Report: Smart monitoring for conservation areas
WWF (World Wide Fund for Nature) monitors over 250,000 protected areas (e.g. national parks and nature reserves) and thousands of other sites and critical habitats. These sites are the foundation of global natural assets and are central to the preservation of biodiversity and human well-being. Unfortunately, they face increasing pressures... -
Conference paper (unpublished)
Defoe: A Spark-Based Toolbox for Analysing Digital Historical Textual Data
This work presents defoe, a new scalable and portable digital eScience toolbox that enables historical research. It allows for running text mining queries across large datasets, such as historical newspapers and books in parallel via Apache Spark. It handles queries against collections that comprise several XML schemas and physical representations.... -
Conference paper (published)
DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching
We present DeezyMatch, a free, open-source software library written in Python for fuzzy string matching and candidate ranking. Its pair classifier supports various deep neural network architectures for training new classifiers and for fine-tuning a pretrained model, which paves the way for transfer learning in fuzzy string matching. This approach...Hosseini, Kasra ; Nanni, Federico ; Coll Ardanuy, Mariona
Natural Language Processing, string matching, toponym matching, machine learning, and digital humanities
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Dataset
Living Machines atypical animacy dataset
Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library (available via https://doi.org/10.21250/db14, British Library Labs, 2014). This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness... -
Conference paper (unpublished)
Assessing the Impact of OCR Quality on Downstream NLP Tasks
A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR). Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis. However, the process of creating text through OCR introduces varying degrees of error to the text. The impact of...