Detail of publication

Abstract

The aim of this paper is to present an extension of the hidden vector state semantic parser. First, we describe the statistical semantic parsing and its decomposition into the semantic and the lexical model. Subsequently, we present the original hidden vector state parser. Then, we modify its lexical model so that it supports the use of the input sequence of feature vectors instead of the sequence of words. We compose the feature vector from the automatically generated linguistic features (lemma form and morphological tag of the original word). We also examine the effect of including the original word into the feature vector. Finally, we evaluate the modified semantic parser on the Czech Human-Human train timetable corpus. We found that the performance of the semantic parser improved significantly compared with the baseline hidden vector state parser.