<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p><p>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.</p></abstract>

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.

Using Particle Swarm Optimization to Accurately Identify Syntactic Phrases in Free Text

Dept. of Machine Translation, Institute for Language and Speech Processing / Athena Research Centre

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into...

Using Particle Swarm Optimization to Accurately Identify Syntactic Phrases in Free Text

Published Online: Nov 01, 2017

Page range: 63 - 77

Received: Jan 17, 2017

Accepted: Mar 29, 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0004

Keywords
parsing of natural language, machine translation, syntactically-derived phrasing, particle swarm optimization (PSO), parameter optimization, Adaptive PSO (AdPSO)

© 2018 George Tambouratzis, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Using Particle Swarm Optimization to Accurately Identify Syntactic Phrases in Free Text

Published Online: Nov 01, 2017

Page range: 63 - 77

Received: Jan 17, 2017

Accepted: Mar 29, 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0004

Keywordsparsing of natural language, machine translation, syntactically-derived phrasing, particle swarm optimization (PSO), parameter optimization, Adaptive PSO (AdPSO)

© 2018 George Tambouratzis, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
parsing of natural language, machine translation, syntactically-derived phrasing, particle swarm optimization (PSO), parameter optimization, Adaptive PSO (AdPSO)