Statistical Learning for Semantic Parsing: A Survey
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 q...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2019-12-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568921642762240 |
|---|---|
| author | Qile Zhu Xiyao Ma Xiaolin Li |
| author_facet | Qile Zhu Xiyao Ma Xiaolin Li |
| author_sort | Qile Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86d885687bd84d84adf334224ec5d516 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-86d885687bd84d84adf334224ec5d5162025-02-02T23:47:57ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-12-012421723910.26599/BDMA.2019.9020011Statistical Learning for Semantic Parsing: A SurveyQile Zhu0Xiyao Ma1Xiaolin Li2<institution content-type="dept">National Science Foundation Center for Big Learning</institution>, <institution>University of Florida</institution>, <city>Gainesville</city>, <state>FL</state> <postal-code>32608</postal-code>, <country>USA</country>.<institution content-type="dept">National Science Foundation Center for Big Learning</institution>, <institution>University of Florida</institution>, <city>Gainesville</city>, <state>FL</state> <postal-code>32608</postal-code>, <country>USA</country>.<institution content-type="dept">National Science Foundation Center for Big Learning</institution>, <institution>University of Florida</institution>, <city>Gainesville</city>, <state>FL</state> <postal-code>32608</postal-code>, <country>USA</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2019.9020011deep learningsemantic parsingknowledge base question answering (kbqa) |
| spellingShingle | Qile Zhu Xiyao Ma Xiaolin Li Statistical Learning for Semantic Parsing: A Survey Big Data Mining and Analytics deep learning semantic parsing knowledge base question answering (kbqa) |
| title | Statistical Learning for Semantic Parsing: A Survey |
| title_full | Statistical Learning for Semantic Parsing: A Survey |
| title_fullStr | Statistical Learning for Semantic Parsing: A Survey |
| title_full_unstemmed | Statistical Learning for Semantic Parsing: A Survey |
| title_short | Statistical Learning for Semantic Parsing: A Survey |
| title_sort | statistical learning for semantic parsing a survey |
| topic | deep learning semantic parsing knowledge base question answering (kbqa) |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020011 |
| work_keys_str_mv | AT qilezhu statisticallearningforsemanticparsingasurvey AT xiyaoma statisticallearningforsemanticparsingasurvey AT xiaolinli statisticallearningforsemanticparsingasurvey |