Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Résolution de coréférences× | Étiquetage sémantique de rôles (SRL)× | |
|---|---|---|
| Domaine | Fouille de textes | Fouille de textes |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1978 | 2002 |
| Auteur d'origine≠ | Hobbs (1978); Lee et al. (2017, neural end-to-end) | Daniel Gildea & Daniel Jurafsky |
| Type≠ | NLP information-extraction task | NLP shallow semantic parsing task |
| Source fondatrice≠ | Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP. link ↗ | Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288. DOI ↗ |
| Alias | coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution) | SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL) |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | 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. | Semantic role labeling, introduced by Gildea and Jurafsky in 2002, is a natural-language-processing task that assigns semantic roles — who did what to whom, where, when, and how — to the components around a verb (predicate) in a sentence. It turns plain text into structured predicate-argument representations and is a foundational tool for event extraction. |
| ScholarGateJeu de données ↗ |
|
|