Compară metode

	Scorarea Coerenței Textului	Clasificarea textului
Domeniu	Mineritul textelor	Mineritul textelor
Familie	Process / pipeline	Process / pipeline
Anul apariției	2008	—
Autorul original	Barzilay & Lapata	—
Tip	NLP text-level scoring task	Supervised NLP classification task
Sursa seminală	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.
Denumiri alternative	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	text categorization, document classification, topic classification, metin sınıflandırma
Înrudite	4	4
Rezumat	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.