Thailand

Gender bias in word representations has emerged as a prominent research area in recent years. While numerous studies have focused on measuring and addressing bias in English word embeddings, research on the Turkish language remains limited. This work aims to bridge this gap by conducting a comprehensive evaluation of gender bias in Turkish word embeddings, considering the dimensions of syntax, semantics, and morphology. We employ subword-based static word vectors trained on three distinct domains: web crawl, academical text, and medical text. Through the analysis of gender-associated words in each domain, we not only uncover gender bias but also gain insights into the unique characteristics of these domains. Additionally, we explore the influence of Turkish suffixes on word gender, providing a novel perspective on gender bias. Our findings reveal the pervasive nature of gender biases across various aspects of the Turkish language, including word frequency, semantics, parts-of-speech, and even the smallest linguistic unit - suffixes. Notably, we demonstrate that the majority of noun and verb lemmas, as well as adverbs and adjectives, exhibit masculine gendering in the general-purpose written language. This study is the first of its kind to offer a comprehensive examination of gender bias in the Turkish language.

ACL 2024

Gender Bias in Turkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains

turkish morphology

turkish word embeddings

turkish gender bias

turkish nlp

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Gender Bias in Turkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains

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Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in English Educational Resources by Extending Existing Gender Bias Taxonomies

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