A lightweight semantic chunking model based on tagging

Abstract

In this paper, a framework for the development of a fast, accurate, and highly portable semantic chunker is introduced. The framework is based on a non-overlapping, shallow tree-structured language. The derivation of the tree is considered as a sequence of tagging actions in a predefined linguistic context, and a novel semantic chunker is accordingly developed. It groups the phrase chunks into the arguments of a given predicate in a bottom-up fashion. This is quite different from current approaches to semantic parsing or chunking that depend on full statistical syntactic parsers that require tree bank style annotation. We compare it with a recently proposed word-byword semantic chunker and present results that show that the phrase-by-phrase approach performs better than its word-by-word counterpart.