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| Domænetilpasset navngiven enhedsgenkendelse× | Navngiven enhedsgenkendelse (NER)× | |
|---|---|---|
| Fagområde≠ | Dyb læring | Tekstmining |
| Familie≠ | Machine learning | Process / pipeline |
| Oprindelsesår≠ | 2006–2020 | — |
| Ophavsperson≠ | Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020) | — |
| Type≠ | Sequence labeling with domain adaptation | NLP sequence-labelling task |
| Oprindelig kilde≠ | Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Aliasser≠ | DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | Domain-adaptive Named Entity Recognition (DA-NER) applies named entity recognition to a target domain by transferring or adapting a model trained on a source domain, using techniques such as domain-specific pre-training, adversarial alignment, or feature augmentation. It addresses the performance collapse that standard NER models suffer when deployed outside their training domain. | 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. |
| ScholarGateDatasæt ↗ |
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