Vertaile menetelmiä

	Text Coherence Scoring	Tekstinluokittelu
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2008	—
Kehittäjä	Barzilay & Lapata	—
Tyyppi	NLP text-level scoring task	Supervised NLP classification task
Alkuperäislähde	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.
Rinnakkaisnimet	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	text categorization, document classification, topic classification, metin sınıflandırma
Liittyvät	4	4
Tiivistelmä	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.