The Rare Books Catalog and the Scholarly Database

Welsh, A

doi:10.1080/01639374.2016.1188433

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Metadata descriptions in the Archive's catalog (and most GLAM catalogs) are freely, publicly available online 2. GLAM metadata descriptions have yet to be analyzed at large scale using natural language processing (NLP) methods and, as records of cultural heritage, the descriptions have the potential to provide historical insights on changes in language and society (Welsh, 2016) 3. GLAM metadata standards are freely, publicly available, often online, meaning we can use historical changes in metadata standards used in the Archive to guide large-scale text analysis of changes in the language of the metadata descriptions over time 4.…”

Section: B9 Provenance Appendixmentioning

confidence: 99%

“…In archives, heritage items include photographs, handwritten documents, instruments, and tweets, among other materials. Heritage items and the language that describes them influence society's understanding of the past, the present, and the direction society is moving into the future (Benjamin, 2019;Welsh, 2016;Yale, 2015;Cook, 2011;Smith, 2006). Through research with GLAM documentation, variations in biased language could be better understood.…”

Section: Background and Introductionmentioning

confidence: 99%

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Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text

Havens¹,

Alex²,

Bach³

et al. 2022

Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

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Mitigating harms from gender biased language in Natural Language Processing (NLP) systems remains a challenge, and the situated nature of language means bias is inescapable in NLP data. Though efforts to mitigate gender bias in NLP are numerous, they often vaguely define gender and bias, only consider two genders, and do not incorporate uncertainty into models. To address these limitations, in this paper we present a taxonomy of gender biased language and apply it to create annotated datasets. We created the taxonomy and annotated data with the aim of making gender bias in language transparent. If biases are communicated clearly, varieties of biased language can be better identified and measured. Our taxonomy contains eleven types of gender biases inclusive of people whose gender expressions do not fit into the binary conceptions of woman and man, and whose gender differs from that they were assigned at birth, while also allowing annotators to document unknown gender information. The taxonomy and annotated data will, in future work, underpin analysis and more equitable language model development.

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Section: B9 Provenance Appendixmentioning

confidence: 99%