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| Koreferenzauflösung× | Benannte Entitätenerkennung (NER)× | |
|---|---|---|
| Fachgebiet | Text Mining | Text Mining |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1978 | — |
| Urheber≠ | Hobbs (1978); Lee et al. (2017, neural end-to-end) | — |
| Typ≠ | NLP information-extraction task | NLP sequence-labelling task |
| Wegweisende Quelle≠ | Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Aliasnamen | coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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. | 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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