Salīdzināt metodes
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| Argumentu ieguve× | Nosaukuma entītiju atpazīšana (NER)× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2016 | — |
| Autors≠ | Lippi & Torroni (state-of-the-art survey) | — |
| Tips≠ | NLP information-extraction task | NLP sequence-labelling task |
| Pirmavots≠ | Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Citi nosaukumi | argumentation mining, argument extraction, Argüman Madenciliği | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units. | 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. |
| ScholarGateDatu kopa ↗ |
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