  In this paper, we study semantic role labelling (SRL), a subtask of semantic
parsing of natural language sentences and its application for the Vietnamese
language. We present our effort in building Vietnamese PropBank, the first
Vietnamese SRL corpus and a software system for labelling semantic roles of
Vietnamese texts. In particular, we present a novel constituent extraction
algorithm in the argument candidate identification step which is more suitable
and more accurate than the common node-mapping method. In the machine learning
part, our system integrates distributed word features produced by two recent
unsupervised learning models in two learned statistical classifiers and makes
use of integer linear programming inference procedure to improve the accuracy.
The system is evaluated in a series of experiments and achieves a good result,
an $F_1$ score of 74.77%. Our system, including corpus and software, is
available as an open source project for free research and we believe that it is
a good baseline for the development of future Vietnamese SRL systems.
