Σύγκριση μεθόδων

	Σημασιολογική ανάλυση	Αναγνώριση Ονομαστικών Οντοτήτων (NER)
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1996 (modern neural revival c. 2018)	—
Δημιουργός	Zelle & Mooney (1996) — foundational supervised approach	—
Τύπος	NLP structured-prediction task	NLP sequence-labelling task
Θεμελιώδης πηγή	Zelle, J.M. & Mooney, R.J. (1996). Learning to Parse Database Queries Using Inductive Logic Programming. AAAI.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Εναλλακτικές ονομασίες	Anlamsal Ayrıştırma (Semantic Parsing), NL-to-SQL, text-to-SQL, natural language understanding	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Συναφείς	5	3
Σύνοψη	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.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.