A Structured SVM Semantic Parser Augmented by Semantic Tagging with Conditional Random Field
Le-Minh Nguyen, Akira Shimazu, Xuan-Hieu Phan · 2016
This paper presents a novel method of semantic parsing that maps a natural language (NL) sentence to a logical form. We propose a semantic parsing method by conducting separately two steps as follows; 1) The first step is to predict semantic tags for a given input sentence. 2) The second step is to build a semantic representation structure for the sentence using the sequence of semantic tags. We formulate the problem of semantic tagging as a sequence learning using a conditional random field models (CRFs). We then represent a tree structure of a given sentence in which syntactic and semantic information are integrated in that tree. The learning problem is to map a given input sentence to a tree structure using a structure support vector model. Experimental results on the CLANG corpus show that the semantic tagging performance achieved a sufficiently high result. In addition, the precision and recall of mapping NL sentences to logical forms i.e. the meaning representation in CLANG show an improvement in comparison with the previous work. 1.