Novel textual entailment technique for the Arabic language using genetic algorithm

This paper presents a textual entailment (TE) model that considers entailment as an optimi-zation problem. The proposed TE model employs a genetic algorithm to derive an optimal similarity function and correlated entailment judgment threshold. The similarity function is formulated through a linear combination of text similarity measures and weights. Two text similarity measures are considered: cosine and the longest common substring. These text similarity measures are computed for each text pair. The weights represent the importance of the considered text similarity measures for generating an entailment judgment. The weights and correlated judgment thresholds are obtained by the genetic algorithm. Several experiments are conducted using the ArbTED dataset to evaluate the performance of the pro-posed model. Comparative results demonstrate the superiority of the proposed model. On average, the model achieved a 16% improvement in terms of accuracy. Furthermore, the average recall and precision values were 72.7% and 72.3%, respectively. (c) 2021 Elsevier Ltd. All rights reserved.