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：语义相似度度量广泛用于自然语言处理中，以推动各类计算机相关任务的发展。然而，没有任何单一语义相似度度量适用于所有任务，研究者常采用集成策略来确保性能。本研究提出一种自动设计语义相似度集成的方法。事实上，我们提出的方法首次利用语法进化，从候选度量池中自动选择并聚合度量，以构建能最大化与人类判断相关性的集成。该方法在多个基准数据集上进行了评估，并与当前最先进的集成方法进行了比较，结果显示其在某些情况下能显著提升相似度评估准确性并超越现有方法。因此，我们的研究证明了利用语法进化自动比较文本的潜力，并证实了在语义相似度任务中采用集成的优势。实现该方法的源代码可从 https://github.com/jorge-martinez-gil/sesige 下载。