Controlling Gender Equality with Shallow NLP Techniques

By M. Carl, Sandrine Garnier, Johann Haller, A. Altmayer, Bärbel Miemietz. 2004

7 citations recorded by the source corpus.

This paper introduces the "Gendercheck Editor", a tool to check German texts for gender discriminatory formulations. It relays on shallow rule-based techniques as used in the Controlled Language Authoring Technology (CLAT). The paper outlines major sources of gender imbalances in German texts. It gives a background on the underlying CLAT technology and describes the marking and annotation strategy to automatically detect and visualize the questionable pieces of text. The paper provides a detailed evaluation of the editor.

Subfields: Artificial Intelligence, Gender Studies, Language and Linguistics

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Citations in the corpus

A Survey on Gender Bias in Natural Language Processing (2021, background): “For instance, Chaloner and Maldonado [2019] study differences in bias in a number of word embeddings trained on corpora from four domains showing the lowest bias in word embeddings trained on a biomedical corpus and the highest bias when trained on news data (higher than social media and Wikipedia-b”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, unknown): “Tadić and Fulgosi (2003) N Y N N − − − − Debowski (2003) N Y N N − − − − Navarretta (2004) Y Y Y N N Y Y − Carl et al. (2004) Y Y Y N N Y Y − Mota, Carvalho, and Ranchhod (2004) N Y N Y − − − − Eisner and Karakos (2005) N Y N Y − − − −”