| Abstract | We present a new approach to learning a semantic parser (a system that maps natural language sentences into logical form). |
| Experimental Evaluation | Second, a semantic parser was learned from the training set augmented with their syntactic parses. |
| Experimental Evaluation | Finally, the learned semantic parser was used to generate the MRs for the test sentences using their syntactic parses. |
| Experimental Evaluation | We measured the performance of semantic parsing using precision (percentage of returned MRs that were correct), recall (percentage of test examples with correct MRs returned), and F -measure (harmonic mean of precision and recall). |
| Introduction | Semantic parsing is the task of mapping a natural language (NL) sentence into a completely formal meaning representation (MR) or logical form. |
| Introduction | A number of systems for automatically learning semantic parsers have been proposed (Ge and Mooney, 2005; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Lu et al., 2008). |
| Introduction | Previous methods for learning semantic parsers do not utilize an existing syntactic parser that provides disambiguated parse trees.1 However, accurate syntactic parsers are available for many |
| Learning a Disambiguation Model | Here, unique word alignments are not required, and alternative interpretations compete for the best semantic parse . |
| Semantic Parsing Framework | The process of generating the semantic parse for an NL sentence is as follows. |