Large-scale Semantic Parsing without Question-Answer Pairs
Large-scale Semantic Parsing without Question-Answer Pairs
复制标题
DOI：
10.1162/tacl_a_00190
复制
发表时间：
2014-10
影响因子：
10.9
通讯作者：
Siva Reddy;Mirella Lapata;Mark Steedman
中科院分区：
文献类型：
--
作者：
Siva Reddy;Mirella Lapata;Mark Steedman
本文介绍了一种新的语义分析方法，可以在不需要人工标注和问答对的情况下用自然语言查询Freebase。我们的主要见解是通过语义图来表示自然语言，其拓扑结构与Freebase有许多共同之处。鉴于这种表示，我们将语义解析概念化为图匹配问题。我们的模型使用CCG将句子转换为语义图，然后将其作为一种弱监督形式的表示法引导到Freebase。在Free917和WebQuestions基准数据集的一个子集上进行的评估实验表明，我们的语义解析器比目前的技术水平有所提高。
In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.