Comparer des méthodes

	Évaluation de la Cohérence Textuelle	Classification de texte
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	2008	—
Auteur d'origine	Barzilay & Lapata	—
Type	NLP text-level scoring task	Supervised NLP classification task
Source fondatrice	Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alias	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	text categorization, document classification, topic classification, metin sınıflandırma
Apparentées	4	4
Résumé	Text coherence scoring computes a document-level coherence score with machine learning, rooted in the entity-based local coherence model introduced by Barzilay and Lapata (2008). It measures how well the sentences of a text hang together, using either an entity-grid model, a graph-based approach, or a transformer-based model.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.