The <i>Old Bailey Proceedings, 1674–1913</i>: Text Mining for Evidence of Court Behavior

Hitchcock, Tim; Turkel, William J.

doi:10.1017/s0738248016000304

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Codification process concluded in March 2012 (version 7.0) and since then the team released two minor upgrades, version 7.1 in April 2013 to correct tagging errors and broken links, and version 7.2 in March 2015 which equally corrects some punctual tagging errors product of mistranscribed data or blunders during automatized tagging tasks (Hitchcock et al, 2012). Researchers can retrieve data through the Old Bailey API (OBAPI) developed by Jamie McLaughlin, and users can perform queries with its demonstrator interface, which allows integrating Old Bailey data with other repositories, projects, and studies, besides to perform advanced task with machine learning methods (Cohen et al, 2011;Hitchcock and Turkel, 2016;Turkel, 2008;Watkins, 2015).…”

Section: Semantic Data Modellingmentioning

confidence: 99%

Jurisdictional Culture and Memory Digitization of the “Government of Justice.” Data Modeling and Digital Approach for the Legal History of Ibero-America

Gayol

Flórez

2018

Cult. Hist. Digit. J.

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Can a machine retrieve the cultural meaning from a corpus of sources? This article addresses the scope and restrictions that digitization, transcription, and data modeling represents for machine-mediated readings of legal, historical records, particularly those derived from the cultural context of Hispanic empire. It compares the dichotomy between the ambiguous language of ancient regime legal texts and the unambiguity required by machinereadable files. Besides, is problematize the corporal reading and the strategy of distant-reading and visualizations as a model for interpretation of vast bulk of textual data. We propose a strategy for segmentation and data modelling for the approach the textual logic of ancient regime legal records based on their hierarchization, interrelation with nonjudiciary sources (theological, historical, philosophical, etc.), its internal segmentation, the non-linear logic of the normative, and the authoritative requirements of compilations and relevant legal works. Its conclude that the advantages of automation are attached to the ability to manipulate files without distorting the original meaning of the texts, therefore, it proposes the necessity to develop standardized vocabularies that help to avoid anachronistic approaches regarding Modern Age legal sources.

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Section: Semantic Data Modellingmentioning

confidence: 99%

Jurisdictional Culture and Memory Digitization of the “Government of Justice.” Data Modeling and Digital Approach for the Legal History of Ibero-America

Gayol

Flórez

2018

Cult. Hist. Digit. J.

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“…Once digitized, manuscripts are typically collected in archives [2], [3], [4]. Some are also transcribed [3], allowing for text-based searching and processing [5]. Others are collections of published data used in research [4], [6], though they are most commonly simply photographed [2].…”

Section: Introductionmentioning

confidence: 99%

Neural Word Search in Historical Manuscript Collections

Wilkinson¹,

Lindstrøm²,

Brun³

2018

Preprint

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We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query. This is commonly referred to as "word spotting". To this end, we first propose an end-to-end trainable model based on deep neural networks that we dub Ctrl-F-Net. The model simultaneously generates region proposals and embeds them into a word embedding space, wherein a search is performed. We further introduce a simplified version called Ctrl-F-Mini. It is faster with similar performance, though it is limited to more easily segmented manuscripts. We evaluate both models on common benchmark datasets and surpass the previous state of the art. Finally, in collaboration with historians, we employ the Ctrl-F-Net to search within a large manuscript collection of over 100 thousand pages, written across two centuries. With only 11 training pages, we enable large scale data collection in manuscript-based historical research. This results in a speed up of data collection and the number of manuscripts processed by orders of magnitude. Given the time consuming manual work required to study old manuscripts in the humanities, quick and robust tools for word spotting has the potential to revolutionise domains like history, religion and language.

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“…Researchers working with old manuscripts can spend several months with a single book of a few hundred pages. There exist many digitized collections that are used for research purposes [6,7,1], and although there have been work automat-ically mining these for information [12,13,28,27], they are exclusively text-based methods. This means that the only way to search for information or do statistics in a collection is to painstakingly read and transcribe the manuscripts manually.…”

Section: Introductionmentioning

confidence: 99%

Neural Ctrl-F: Segmentation-Free Query-by-String Word Spotting in Handwritten Manuscript Collections

Wilkinson

Lindström

Brun

2017

2017 IEEE International Conference on Computer Vision (ICCV)

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In this paper, we approach the problem of segmentationfree query-by-string word spotting for handwritten documents. In other words, we use methods inspired from computer vision and machine learning to search for words in large collections of digitized manuscripts. In particular, we are interested in historical handwritten texts, which are often far more challenging than modern printed documents. This task is important, as it provides people with a way to quickly find what they are looking for in large collections that are tedious and difficult to read manually. To this end, we introduce an end-to-end trainable model based on deep neural networks that we call Ctrl-F-Net. Given a full manuscript page, the model simultaneously generates region proposals, and embeds these into a distributed word embedding space, where searches are performed. We evaluate the model on common benchmarks for handwritten word spotting, outperforming the previous state-of-theart segmentation-free approaches by a large margin, and in some cases even segmentation-based approaches. One interesting real-life application of our approach is to help historians to find and count specific words in court records that are related to women's sustenance activities and division of labor. We provide promising preliminary experiments that validate our method on this task.

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The Old Bailey Proceedings, 1674–1913: Text Mining for Evidence of Court Behavior

Cited by 11 publications

References 13 publications

Jurisdictional Culture and Memory Digitization of the “Government of Justice.” Data Modeling and Digital Approach for the Legal History of Ibero-America

Jurisdictional Culture and Memory Digitization of the “Government of Justice.” Data Modeling and Digital Approach for the Legal History of Ibero-America

Neural Word Search in Historical Manuscript Collections

Neural Ctrl-F: Segmentation-Free Query-by-String Word Spotting in Handwritten Manuscript Collections

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