Linganisha mbinu

	Utambuzi wa upendeleo wa kijinsia katika NLP	Utambuzi wa Majina ya Entiti (NER)
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2017–2018 (seminal benchmarks)	—
Mwanzilishi	Caliskan et al. (2017); Zhao et al. (2018)	—
Aina	NLP bias auditing pipeline	NLP sequence-labelling task
Chanzo asilia	Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Majina mbadala	Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Zinazohusiana	5	3
Muhtasari	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.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.