Learning Executable Semantic Parsers for Natural Language Understanding

by   Percy Liang, et al.
Stanford University

For building question answering systems and natural language interfaces, semantic parsing has emerged as an important and powerful paradigm. Semantic parsers map natural language into logical forms, the classic representation for many important linguistic phenomena. The modern twist is that we are interested in learning semantic parsers from data, which introduces a new layer of statistical and computational issues. This article lays out the components of a statistical semantic parser, highlighting the key challenges. We will see that semantic parsing is a rich fusion of the logical and the statistical world, and that this fusion will play an integral role in the future of natural language understanding systems.


page 1

page 2

page 3

page 4


Logical Parsing from Natural Language Based on a Neural Translation Model

Semantic parsing has emerged as a significant and powerful paradigm for ...

Building an Application Independent Natural Language Interface

Traditional approaches to building natural language (NL) interfaces typi...

A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

A growing interest in tasks involving language understanding by the NLP ...

Practical Semantic Parsing for Spoken Language Understanding

Executable semantic parsing is the task of converting natural language u...

Toward a Neural Semantic Parsing System for EHR Question Answering

Clinical semantic parsing (SP) is an important step toward identifying t...

A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World

The blocks world is a classic toy domain that has long been used to buil...

A Generate-Validate Approach to Answering Questions about Qualitative Relationships

Qualitative relationships describe how increasing or decreasing one prop...

Please sign up or login with your details

Forgot password? Click here to reset