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2019
Journal article
Design Choices for Productive, Secure, Data-Intensive Research at Scale in the Cloud
We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user...Arenas, Diego ; Atkins, Jon ; Austin, Claire ; Beavan, David ; Cabrejas Egea, Alvaro …
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2020
Abstract
Using smart annotations to map the geography of newspapers
Geographic information is a key component in the description of collection objects, and yet its format is often unsuited for use with methods of geographic analysis. Catalogue entries are often inconsistent, in plain text, and without geographic coordinates (much less coordinates linked to authority records). Georesolution of the relevant fields...Ryan, Yann ; Coll Ardanuy, Mariona ; van Strien, Daniel ; Hosseini, Kasra ; Beelen, Kaspar …
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2020
Book chapter
Text Meets Space: Geographic Content Extraction, Resolution and Information Retrieval
In this half-day tutorial, we will review the basic concepts of, methods for, and applications of geographic information retrieval, also showing some possible applications in fields such as the digital humanities. The tutorial is organized in four parts. First we introduce some basic ideas about geography, and demonstrate why text...Leidner, Jochen ; Martins, Bruno ; McDonough, Katherine ; Purves, Ross
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2020
Book chapter
How we got here
Wilson, Daniel C.S.
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2020
Conference paper (unpublished)
A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
Recognizing toponyms and resolving them to their real-world referents is required for providing advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a toponym... -
2021
Research report
Data Study Group Final Report: The National Archives, UK: Discovering Topics and Trends in the UK Government Web Archive
The challenge we address in this report is to make steps towards improving search and discovery of resources within this vast archive for future archive users, and how the UKGWA collection could begin to be unlocked for research and experimentation by approaching it as data (i.e. as a dataset at...Beavan, David ; Nanni, Federico
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2021
Conference paper (published)
When Time Makes Sense: A Historically-Aware Approach to Targeted Sense Disambiguation
As languages evolve historically, making computational approaches sensitive to time can improve performance on specific tasks. In this work, we assess whether applying historical language models and time-aware methods help with determining the correct sense of polysemous words. We outline the task of time-sensitive Targeted Sense Disambiguation (TSD), which aims...Beelen, Kaspar ; Nanni, Federico ; Coll Ardanuy, Mariona ; Hosseini, Kasra ; Tolfo, Giorgia …
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2021
Journal article
Maps of a Nation? The Digitized Ordnance Survey for New Historical Research
Although the Ordnance Survey has itself been the subject of historical research, scholars have not systematically used its maps as primary sources of information. This is partly for disciplinary reasons and partly for the technical reason that high-quality maps have not until recently been available digitally, geo-referenced, and in color....Hosseini, Kasra ; McDonough, Katherine ; van Strien, Daniel ; Vane, Olivia ; Wilson, Daniel C.S.
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2022
Learning object
Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 2)
This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model. It covers some challenges one may face due to the training data used and the importance...Strien, Daniel van ; Beelen, Kaspar ; Wevers, Melvin ; Smits, Thomas ; McDonough, Katherine
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2022
Learning object
Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)
This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification.Strien, Daniel van ; Beelen, Kaspar ; Wevers, Melvin ; Smits, Thomas ; McDonough, Katherine
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2022
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
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2023
Software
Living-with-machines/MapReader: End of LwM
This release marks the end of the current funding for MapReader during the Living with Machines (LwM) project. @kasra-hosseini @andrewphilipsmith @rwood-97 @kmcdono2 @dcsw2 @kallewesterling @kasparvonbeelenHosseini, Kasra ; Wood, Rosie ; Smith, Andy ; McDonough, Katie ; Wilson, Daniel C. S. …
computer vision and maps
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2023
Other
Models for MapReader ACM SIGSPATIAL 2023 Geohumanities Workshop paper
Collection of fine-tuned models created during research published in Kasra Hosseini, Daniel C. S. Wilson, Kaspar Beelen, and Katherine McDonough. 2022. MapReader: a computer vision pipeline for the semantic exploration of maps at scale. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on Geospatial Humanities (GeoHumanities '22). Association for...Hosseini, Kasra ; Beelen, Kaspar ; McDonough, Katherine ; Wilson, Daniel C. S.
computational humanities, computer vision, maps, models, and image classification
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2023
Dataset
Diachronic and diatopic word embeddings from newspapers digitised by the British Library (1830-1889): North and South England
Diachronic word embeddings (decade-level) trained with Word2Vec (via Gensim) on different geographic subcorpora of the Heritage Made Digital British and the Living with Machines historical newspaper collections: - North England (north.zip) - South England (south.zip) At the moment, for each subcorpus, Word2Vec models are available for each decade in the...Pedrazzini, Nilo ; McGillivray, Barbara
historical semantics, diachronic embeddings, late modern English, word embeddings, word vectors, word2vec, and diatopic embeddings
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2023
Dataset
Decade-level Word2Vec models from automatically transcribed 19th-century newspapers digitised by the British Library (1800-1919)
Word embeddings trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and specific parameters. The embeddings are divided into periods of ten years each. Unlike those in this repository, these were not aligned and OCR errors skimmed from the vocabulary. See related GitHub repository for the full documentation:...Pedrazzini, Nilo
historical semantics, British newspapers, word embeddings, word vectors, word2vec, and Late Modern English