Semantic similarity measures are widely used in natural language processing to catalyze various computer-related tasks. However, no single semantic similarity measure is the most appropriate for all tasks, and researchers often use ensemble strategies to ensure performance. This research work proposes a method for automatically designing semantic similarity ensembles. In fact, our proposed method uses grammatical evolution, for the first time, to automatically select and aggregate measures from a pool of candidates to create an ensemble that maximizes correlation to human judgment. The method is evaluated on several benchmark datasets and compared to state-of-the-art ensembles, showing that it can significantly improve similarity assessment accuracy and outperform existing methods in some cases. As a result, our research demonstrates the potential of using grammatical evolution to automatically compare text and prove the benefits of using ensembles for semantic similarity tasks. The source code that illustrates our approach can be downloaded from https://github.com/jorge-martinez-gil/sesige.

翻译：语义相似性度量在自然语言处理领域被广泛应用于催化各类计算机相关任务。然而，没有任何单一的语义相似性度量能够适用于所有任务，研究者常采用集成策略以确保性能。本研究提出了一种自动设计语义相似性集成的方法。该方法首次运用语法进化技术，从候选度量池中自动选择并聚合度量，以构建与人类判断相关性最大化的集成系统。通过在多个基准数据集上的评估，并与前沿集成方法进行比较，结果表明该方法能显著提升相似性评估精度，并在某些情况下优于现有方法。因此，本研究证明了利用语法进化技术自动比较文本的潜力，并验证了在语义相似性任务中使用集成策略的优越性。阐述本方法的源代码可从 https://github.com/jorge-martinez-gil/sesige 下载。