Σύγκριση μεθόδων

	Ανίχνευση Φυλετικής Μεροληψίας στην Επεξεργασία Φυσικής Γλώσσας	Αναφορά συν-αναφοράς
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017–2018 (seminal benchmarks)	1978
Δημιουργός	Caliskan et al. (2017); Zhao et al. (2018)	Hobbs (1978); Lee et al. (2017, neural end-to-end)
Τύπος	NLP bias auditing pipeline	NLP information-extraction task
Θεμελιώδης πηγή	Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.	Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.
Εναλλακτικές ονομασίες	Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias	coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution)
Συναφείς	5	4
Σύνοψη	Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora and language models. Grounded in benchmarks established by Caliskan et al. (2017) with the Word Embedding Association Test (WEAT) and Zhao et al. (2018) with the WinoBias dataset, these methods produce quantitative evidence of gender bias rather than qualitative impressions. They are widely applied in ethical AI research, media analysis, and fairness auditing of machine-learning systems.	Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding.