Linganisha mbinu

	Uchanganuzi wa Kisemantiki	Uchanganuzi wa Hisia
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1996 (modern neural revival c. 2018)	—
Mwanzilishi	Zelle & Mooney (1996) — foundational supervised approach	—
Aina	NLP structured-prediction task	NLP text-classification task
Chanzo asilia	Zelle, J.M. & Mooney, R.J. (1996). Learning to Parse Database Queries Using Inductive Logic Programming. AAAI.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Majina mbadala	Anlamsal Ayrıştırma (Semantic Parsing), NL-to-SQL, text-to-SQL, natural language understanding	opinion mining, polarity detection, duygu analizi
Zinazohusiana	5	3
Muhtasari	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.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.