Cart (Loading....) | Create Account
Close category search window
 

Using an Ant Colony Metaheuristic to Optimize Automatic Word Segmentation for Ancient Greek

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Tambouratzis, G. ; Inst. for Language & Speech Process., Athens, Greece

Given a text or collection of texts involving unconstrained language, a basic task in a multitude of applications is the identification of stems and endings for each word form, which is termed morphological analysis. In this paper, the use of an ant colony optimization (ACO) metaheuristic is proposed for a linguistic task that involves the automated morphological segmentation of Ancient Greek word forms into stem and ending. The task of morphological analysis is essential for implementing text-processing applications such as semantic analysis and information retrieval. The difficulty of the morphological analysis task differs depending on the language chosen, being hardest in the case of highly-inflectional languages, where each stem may be associated with a large number of different endings. In this paper, focus is placed on the morphological analysis of ancient Greek, which has been shown to be a particularly hard task. To perform this task, a system for the automated morphological processing has been proposed, which implements the morphological analysis of words by coupling an iterative pattern-recognition algorithm with a modest amount of linguistic knowledge, expressed via a set of interactions associated with weights. In an earlier version of the system, these weights were determined by combining the input from specialized scientists with a lengthy manual optimization process. In this paper, the ACO metaheuristic is applied to the task of defining near-optimal system weights using an automated process based on a set of training data. The experiments performed indicate that the segmentation quality achieved by ACO is equivalent to or in several cases substantially higher than that achieved using manually optimized weights.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:13 ,  Issue: 4 )

Date of Publication:

Aug. 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.