Cornell SPF: Cornell Semantic Parsing Framework

Abstract

The Cornell Semantic Parsing Framework (SPF) is a learning and inference framework for mapping natural language to formal representation of its meaning.

References in corpus (2)

• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• A Joint Model of Language and Perception for Grounded Attribute Learning

Cited by in corpus (5)

• Data Recombination for Neural Semantic Parsing
• Inferring Logical Forms From Denotations
• Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
• Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog
• Text2Math: End-to-end Parsing Text into Math Expressions