This paper presents a textual entailment (TE) model that considers entailment as an optimization 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 proposed 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.

本文提出了一种文本蕴含（TE）模型，该模型将蕴含视为优化问题。提出的TE模型采用遗传算法来推导最佳相似度函数和相关的吞吐判断阈值。通过文本相似性度量和权重的线性组合来制定相似性函数。考虑了两种文本相似性度量：余弦和最长的公共子字符串。为每个文本对计算这些文本相似性度量。权重表示考虑的文本相似性度量对于生成蕴含度判断的重要性。通过遗传算法获得权重和相关的判断阈值。使用ArbTED数据集进行了几次实验，以评估所提出模型的性能。比较结果证明了该模型的优越性。平均而言，该模型的准确性提高了16％。此外，平均召回率和精确度值分别为72.7％和72.3％。