Learning a Compositional Semantic Parser using an Existing Syntactic Parser
By Ruifang Ge, Raymond J. Mooney. 2009
58 citations recorded by the source corpus.
We present a new approach to learning a semantic parser (a system that maps natural language sentences into logical form). Unlike previous methods, it exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. The resulting system produces improved results on standard corpora on natural language interfaces for database querying and simulated robot control.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Luke S. Zettlemoyer
- Percy Liang
- Mohit Bansal
- Mirella Lapata
- Jianfeng Gao
- Jason Aaron Edward Weston
- Christopher D. Manning
- Noah A. Smith
Citations in the corpus
- A semantic matching energy function for learning with multi-relational data (2013, neutral): “L G ] 21 M ar 2 01 3 A Semantic Matching Energy Function for Learning with Multi-relational Data Xavier Glorot(1), Antoine Bordes(2), Jason Weston(3), Yoshua Bengio(1) (1) DIRO, Université de Montréal, Montréal, QC, Canada– {glorotxa,bengioy}@iro.umontreal.ca (2) CNRS - Heudiasyc, Université de Tech”
- Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing (2012, background): “The first one, which could be termed in-domain, aims at learning to build highly evolved and comprehensive MRs (Ge and Mooney, 2009; Zettlemoyer and Collins, 2009; Liang et al., 2011).”
- Transforming Dependency Structures to Logical Forms for Semantic Parsing (2016, background): “…going through an intermediate semantic representation, e.g., answer-sentence selection (Punyakanok et al., 2004; Heilman and Smith, 2010; Yao et al., 2013) and semantic parsing (Ge and Mooney, 2009; Poon, 2013; Parikh et al., 2015; Xu et al., 2015; Wang et al., 2015; Andreas and Klein, 2015).”
- Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (2018, methodology): “These methods are all based on manually-designed, heuristic generation process, which may suffer from syntactic parse errors (Ge and Mooney, 2009; Reddy et al., 2014, 2016), structure mismatch (Chen et al.”
- Unsupervised Semantic Parsing (2009, background): “The only exception that we are aware of is Ge and Mooney (2009).”
- Composition in Distributional Models of Semantics (2010, background): “, 2003) and natural language interfaces for robot control (Ge and Mooney, 2009).”