Semantic Parsing
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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Zelle, J.M. & Mooney, R.J. (1996). Learning to Parse Database Queries Using Inductive Logic Programming. AAAI. · URL
- Yu, T. et al. (2018). Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing. EMNLP. · URL
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.