Making verbs count: the research project ‘Gender and Work’ and its methodology

Fiebranz, Rosemarie; Lindberg, Erik; Lindström, Jonas; Ågren, Maria

doi:10.1080/03585522.2011.617576

Cited by 37 publications

(13 citation statements)

References 5 publications

(5 reference statements)

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“…Each system was tested as recommended above, with accuracy reported separately on seen and unseen items, and for different training data sizes. To evaluate the downstream effects of normalization, we applied the models to a collection of unseen documents and then tagged them with the Stan-6 English: Markus (1999); German: Scheible et al (2011); Hungarian: Simon (2014); Icelandic: Rögnvaldsson et al (2012); Swedish: Fiebranz et al (2011). For details of their dates and contents, see Pettersson et al (2014).…”

Section: Methodsmentioning

confidence: 99%

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Evaluating Historical Text Normalization Systems: How Well Do They Generalize?

Robertson

Goldwater²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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We highlight several issues in the evaluation of historical text normalization systems that make it hard to tell how well these systems would actually work in practice-i.e., for new datasets or languages; in comparison to more naïve systems; or as a preprocessing step for downstream NLP tools. We illustrate these issues and exemplify our proposed evaluation practices by comparing two neural models against a naïve baseline system. We show that the neural models generalize well to unseen words in tests on five languages; nevertheless, they provide no clear benefit over the naïve baseline for downstream POS tagging of an English historical collection. We conclude that future work should include more rigorous evaluation, including both intrinsic and extrinsic measures where possible.

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Section: Methodsmentioning

confidence: 99%

“… English: Markus (1999); German:Scheible et al (2011); Hungarian:Simon (2014); Icelandic:Rögnvaldsson et al (2012); Swedish:Fiebranz et al (2011). For details of their dates and contents, seePettersson et al (2014).…”

mentioning

confidence: 99%

Evaluating Historical Text Normalization Systems: How Well Do They Generalize?

Robertson

Goldwater²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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“…Fifteen occupational clusters, such as agricultural professions, health and nautical professions, are used for estimating the proportional distribution of those with the database content, showing for instance that members of the clergy diminished after about 1885 or that agricultural professions first declined in importance but then become to rise around the turn of the century, after which they rapidly sank again. However, even closer to our goals is the research by Pettersson & Nivre (2011); Fiebranz et al (2011) and Pettersson et al (2012;2014), who in cooperation with historians, study what men and women did for a living in the early modern Swedish society ("The Gender and Work project", GaW) between 1550-1800. In the context of GaW's verb-orientated studies, historians are building a database with relevant information, by identifying working activities often described in the form of verb phrases, such as chop wood or sell fish.…”

Section: Introductionmentioning

confidence: 94%

Gender-Based Vocation Identification in Swedish 19th Century Prose Fiction using Linguistic Patterns, NER and CRF Learning

Kokkinakis

Ighe

Malm

2015

Proceedings of the Fourth Workshop on Computational Linguistics for Literature

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This paper investigates how literature could be used as a means to expand our understanding of history. By applying macroanalytic techniques we are aiming to investigate how women enter literature and particularly which functions do they assume, their working patterns and if we can spot differences in how often male and female characters are mentioned with various types of occupational titles (vocation) in Swedish literary texts. Modern historiography, and especially feminist and women's history has emphasized a relative invisibility of women's work and women workers. The reasons to this are manifold, and the extent, the margin of error in terms of women's work activities is of course hard to assess. Therefore, vocation identification can be used as an indicator for such exploration and we present a hybrid system for automatic annotation of vocational signals in 19th century Swedish prose fiction. Beside vocations, the system also assigns gender (male, female or unknown) to the vocation words, a prerequisite for the goals of the study and future in-depth explorations of the corpora.

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“…Finally, for Swedish experiments, they compiled balanced subsets of the Gender and Work corpus (GaW) of court records and church documents from the time period 1527-1812 (Fiebranz et al 2011).…”

Section: German Hungarian Icelandic and Swedish Historical Corpusmentioning

confidence: 99%

Weighted finite-state transducers for normalization of historical texts

2019

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This paper presents a study about methods for normalization of historical texts. The aim of these methods is learning relations between historical and contemporary word forms. We have compiled training and test corpora for different languages and scenarios, and we have tried to read the results related to the features of the corpora and languages. Our proposed method, based on weighted finite-state transducers, is compared to previously published ones. Our method learns to map phonological changes using a noisy channel model; it is a simple solution that can use a limited amount of supervision in order to achieve adequate performance. The compiled corpora are ready to be used for other researchers in order to compare results. Concerning the amount of supervision for the task, we investigate how the size of training corpus affects the results and identify some interesting factors to anticipate the difficulty of the task.

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Making verbs count: the research project ‘Gender and Work’ and its methodology

Cited by 37 publications

References 5 publications

Evaluating Historical Text Normalization Systems: How Well Do They Generalize?

Evaluating Historical Text Normalization Systems: How Well Do They Generalize?

Gender-Based Vocation Identification in Swedish 19th Century Prose Fiction using Linguistic Patterns, NER and CRF Learning

Weighted finite-state transducers for normalization of historical texts

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