A Framework for Text Processing and Supporting Access to Collections of Digitized Historical Newspapers

Allen, Robert B.; Japzon, Andrea; Achananuparp, Palakorn; Lee, Ki Jung

doi:10.1007/978-3-540-73354-6_26

Cited by 8 publications

(3 citation statements)

References 22 publications

(13 reference statements)

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“…The pipeline was deployed in the GATE, a popular and mature open source JAVA-based suite of tools with over 20 years of continuous development from the Natural Language Processing (NLP) research group at the University of Sheffield (Cunningham et al, 2013). The GATE platform has been employed for the construction of NER pipelines in the broader digital humanities domain, including processing of 18th century court proceedings (Bontcheva et al, 2002), historical newspapers (Allen et al, 2007), and archaeological greyliterature report (Vlachidis and Tudhope, 2016).…”

Section: The Ner Methodsmentioning

confidence: 99%

Named-entity recognition for early modern textual documents: a review of capabilities and challenges with strategies for the future

Humbel¹,

Nyhan²,

Vlachidis³

et al. 2021

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PurposeBy mapping-out the capabilities, challenges and limitations of named-entity recognition (NER), this article aims to synthesise the state of the art of NER in the context of the early modern research field and to inform discussions about the kind of resources, methods and directions that may be pursued to enrich the application of the technique going forward.Design/methodology/approachThrough an extensive literature review, this article maps out the current capabilities, challenges and limitations of NER and establishes the state of the art of the technique in the context of the early modern, digitally augmented research field. It also presents a new case study of NER research undertaken by Enlightenment Architectures: Sir Hans Sloane's Catalogues of his Collections (2016–2021), a Leverhulme funded research project and collaboration between the British Museum and University College London, with contributing expertise from the British Library and the Natural History Museum.FindingsCurrently, it is not possible to benchmark the capabilities of NER as applied to documents of the early modern period. The authors also draw attention to the situated nature of authority files, and current conceptualisations of NER, leading them to the conclusion that more robust reporting and critical analysis of NER approaches and findings is required.Research limitations/implicationsThis article examines NER as applied to early modern textual sources, which are mostly studied by Humanists. As addressed in this article, detailed reporting of NER processes and outcomes is not necessarily valued by the disciplines of the Humanities, with the result that it can be difficult to locate relevant data and metrics in project outputs. The authors have tried to mitigate this by contacting projects discussed in this paper directly, to further verify the details they report here.Practical implicationsThe authors suggest that a forum is needed where tools are evaluated according to community standards. Within the wider NER community, the MUC and ConLL corpora are used for such experimental set-ups and are accompanied by a conference series, and may be seen as a useful model for this. The ultimate nature of such a forum must be discussed with the whole research community of the early modern domain.Social implicationsNER is an algorithmic intervention that transforms data according to certain rules-, patterns- or training data and ultimately affects how the authors interpret the results. The creation, use and promotion of algorithmic technologies like NER is not a neutral process, and neither is their output A more critical understanding of the role and impact of NER on early modern documents and research and focalization of some of the data- and human-centric aspects of NER routines that are currently overlooked are called for in this paper.Originality/valueThis article presents a state of the art snapshot of NER, its applications and potential, in the context of early modern research. It also seeks to inform discussions about the kinds of resources, methods and directions that may be pursued to enrich the application of NER going forward. It draws attention to the situated nature of authority files, and current conceptualisations of NER, and concludes that more robust reporting of NER approaches and findings are urgently required. The Appendix sets out a comprehensive summary of digital tools and resources surveyed in this article.

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Section: The Ner Methodsmentioning

confidence: 99%

Named-entity recognition for early modern textual documents: a review of capabilities and challenges with strategies for the future

Humbel¹,

Nyhan²,

Vlachidis³

et al. 2021

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“…Of course, there should probably be an ongoing community review panel to ensure that appropriate material is saved, that local records are well managed, and that there is coordination and consistency across the records. Allen, et al (2007) describe the utility of such a model for processing digitized collections of historical newspapers.…”

Section: Comprehensive Community Information Repository and Semantic mentioning

confidence: 99%

Preserving digital local news

Allen

Johnson

2008

The Electronic Library

Self Cite

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“…However, it does not seem feasible to do that by entering specific facts; there are just too many. Generative models such as cyclic models for the seasons may be better (see [2]). These could be simple such as listing the months of the baseball season, the years in which there are presidential elections, the years during which the Wright Brothers were working, or the locations of major buildings in the city.…”

Section: The Big Picturementioning

confidence: 99%

Automated Processing of Digitized Historical Newspapers: Identification of Segments and Genres

Allen

Waldstein

Zhu

2008

Digital Libraries: Universal and Ubiquitous Access to Information

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Many historical newspapers are being digitized. We aim to support access to them via text analysis of the OCRd content. However, the OCR includes many errors; so extracting meaningful content from it is difficult. A pipeline of processing steps is proposed. Here, we describe the first two steps: segmentation and genre identification. The segmentation procedure based on headings was quite successful. Genre identification worked well for easily defined genre categories such as weather reports. We also propose additional techniques which may improve the accuracy still farther.

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A Framework for Text Processing and Supporting Access to Collections of Digitized Historical Newspapers

Cited by 8 publications

References 22 publications

Named-entity recognition for early modern textual documents: a review of capabilities and challenges with strategies for the future

Named-entity recognition for early modern textual documents: a review of capabilities and challenges with strategies for the future

Preserving digital local news

Automated Processing of Digitized Historical Newspapers: Identification of Segments and Genres

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