Narrow Results

Multilingual text compression exploits the existence of the same text in several languages to compress the second and subsequent copies by reference to the first. We explore the details of this framework and present experimental results for parallel English and French texts.

A crucial step in plagiarism detection is text alignment. This task consists in finding similar text fragments between two given documents. We introduce an optimization methodology based on genetic algorithms to improve the performance of a plagiarism detection model by optimizing its input parameters. The implementation of the genetic algorithm is based on nonbinary representation of individuals, elitism selection, uniform crossover, and high mutation rate. The obtained parameter settings allow the plagiarism detection model to achieve better results than the state-of-the-art approaches.