Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Detectie van genderbias in NLP× | Named Entity Recognition (NER)× | |
|---|---|---|
| Vakgebied | Tekstmining | Tekstmining |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2017–2018 (seminal benchmarks) | — |
| Grondlegger≠ | Caliskan et al. (2017); Zhao et al. (2018) | — |
| Type≠ | NLP bias auditing pipeline | NLP sequence-labelling task |
| Oorspronkelijke bron≠ | Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Aliassen≠ | Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | 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. |
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