1. bookVolume 8 (2018): Issue 1 (January 2018)
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30 Dec 2014
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access type Open Access

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

Published Online: 01 Nov 2017
Page range: 63 - 77
Received: 17 Jan 2017
Accepted: 29 Mar 2017
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

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.

Keywords

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