Salīdzināt metodes
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| Entītiju saistīšana× | Nosaukuma entītiju atpazīšana (NER)× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2008 | — |
| Autors≠ | Milne & Witten | — |
| Tips≠ | NLP knowledge-base grounding task | NLP sequence-labelling task |
| Pirmavots≠ | Milne, D. & Witten, I.H. (2008). Learning to Link with Wikipedia. CIKM (Proceedings of the 17th ACM Conference on Information and Knowledge Management). DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Citi nosaukumi≠ | named entity disambiguation, entity disambiguation, entity resolution to knowledge base, Varlık Bağlama (Entity Linking) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Entity linking is a natural-language-processing task that matches ambiguous entity mentions in text — people, places, organisations — to the correct record in a knowledge base such as Wikidata, DBpedia, or a domain dictionary. Surveyed and shaped by Milne and Witten (2008) and later neural approaches reviewed by Sevgili and colleagues (2022), it grounds free text into structured, unambiguous references used in knowledge-graph building and multi-source text analysis. | 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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