Online Structured Learning for Semantic Parsing with Synchronous and λ-Synchronous Context Free Grammars

被引：1

作者：

Nguyen, Le-Minh ^{[1
]}

Shimazu, Akira ^{[1
]}

Phan, Xuan Hieu ^{[2
]}

Nguyen, Phuong Thai ^{[3
]}

机构：

[1] JAIST, GSIS, 1-1 Asahidai, Nomi Ishikawa 9231292, Japan

[2] GIS Tohoku Univ, Tohoku 980 8579, Japan

[3] Coll Tech, VNUH, Pantnagar 263145, Uttar Pradesh, India

来源：

20TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL 2, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICTAI.2008.96

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We formulate semantic parsing as a parsing problem on a synchronous context free grammar (SCFG) which is automatically built on the corpus of natural language sentences and the representation of semantic outputs. We then present an online learning framework for estimating the synchronous SCFG grammar In addition, our online learning methods for semantic parsing problems are also extended to deal with the case, in which the semantic representation could be represented under lambda-calculus. Experimental results in the domain of semantic parsing show advantages in comparison with previous works.

引用

页码：135 / +

页数：2

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