Comparer des méthodes

	Analyse sémantique	Classification de texte
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	1996 (modern neural revival c. 2018)	—
Auteur d'origine	Zelle & Mooney (1996) — foundational supervised approach	—
Type	NLP structured-prediction task	Supervised NLP classification task
Source fondatrice	Zelle, J.M. & Mooney, R.J. (1996). Learning to Parse Database Queries Using Inductive Logic Programming. AAAI.	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	Anlamsal Ayrıştırma (Semantic Parsing), NL-to-SQL, text-to-SQL, natural language understanding	text categorization, document classification, topic classification, metin sınıflandırma
Apparentées	5	4
Résumé	Semantic parsing is a natural-language-processing task that converts free-text utterances into executable formal representations such as SQL queries, logical forms, or Abstract Meaning Representations (AMR). Established in its supervised learning form by Zelle and Mooney in 1996 and scaled to cross-domain settings by the Spider benchmark (Yu et al., 2018), it bridges the gap between human language and machine-executable structures.	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.