The present article reviews the application of Particle Swarm Optimization (PSO) algorithms
to optimize a phrasing model, which splits any text into linguistically-motivated
phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser.
The phrasing model combines attractive and repulsive forces between neighbouring words
in a sentence to determine which segmentation points are required. The extrapolation of
phrases in the specific application is aimed towards the automatic translation of unconstrained
text from a source language to a target language via a phrase-based system, and
thus the phrasing needs to be accurate and consistent to the training data.
Experimental results indicate that PSO is effective in optimising the weights of the proposed
parser system, using two different variants, namely sPSO and AdPSO. These variants
result in statistically significant improvements over earlier phrasing results. An analysis
of the experimental results leads to a proposed modification in the PSO algorithm,
to prevent the swarm from stagnation, by improving the handling of the velocity component
of particles. This modification results in more effective training sequences where
the search for new solutions is extended in comparison to the basic PSO algorithm. As a
consequence, further improvements are achieved in the accuracy of the phrasing module.
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