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A long-term goal of Artificial Intelligence (AI) is to provide machines with the capability of understanding natural language. Understanding natural language may be referred as the system must produce a correct response to the received input order. This response can be a robot move, an answer to a question, etc. One way to achieve this goal is semantic parsing. It parses utterances into semantic representations called logical form, a representation of many important linguistic phenomena that can be understood by machines. Semantic parsing is a fundamental problem in natural language understanding area. In recent years, researchers have made tremendous progress in this field. In this paper, we review recent algorithms for semantic parsing including both conventional machine learning approaches and deep learning approaches. We first give an overview of a semantic parsing system, then we summary a general way to do semantic parsing in statistical learning. With the rise of deep learning, we will pay more attention on the deep learning based semantic parsing, especially for the application of Knowledge Base Question Answering (KBQA). At last, we survey several benchmarks for KBQA.


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Statistical Learning for Semantic Parsing: A Survey

Show Author's information Qile ZhuXiyao MaXiaolin Li( )
National Science Foundation Center for Big Learning, University of Florida, Gainesville, FL 32608, USA.

Abstract

A long-term goal of Artificial Intelligence (AI) is to provide machines with the capability of understanding natural language. Understanding natural language may be referred as the system must produce a correct response to the received input order. This response can be a robot move, an answer to a question, etc. One way to achieve this goal is semantic parsing. It parses utterances into semantic representations called logical form, a representation of many important linguistic phenomena that can be understood by machines. Semantic parsing is a fundamental problem in natural language understanding area. In recent years, researchers have made tremendous progress in this field. In this paper, we review recent algorithms for semantic parsing including both conventional machine learning approaches and deep learning approaches. We first give an overview of a semantic parsing system, then we summary a general way to do semantic parsing in statistical learning. With the rise of deep learning, we will pay more attention on the deep learning based semantic parsing, especially for the application of Knowledge Base Question Answering (KBQA). At last, we survey several benchmarks for KBQA.

Keywords:

deep learning, semantic parsing, Knowledge Base Question Answering (KBQA)
Received: 17 September 2018 Accepted: 29 April 2019 Published: 05 August 2019 Issue date: December 2019
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Publication history

Received: 17 September 2018
Accepted: 29 April 2019
Published: 05 August 2019
Issue date: December 2019

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© The author(s) 2019

Acknowledgements

This work was partially supported by National Science Foundation (No. CNS-1842407), National Institutes of Health (No. R01GM110240), and Industry Members of NSF Center for Big Learning (http://nsfcbl.org/index. php/partners/).

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