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：语义相似度度量方法广泛应用于自然语言处理中，以促进各类计算机相关任务。然而，没有任何单一的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保证性能。本研究提出了一种自动设计语义相似度集成的方法。事实上，我们的方法首次采用语法演化，从候选池中自动选择并聚合度量，构建一个与人类判断相关性最大化的集成。该方法在多个基准数据集上进行了评估，并与最先进的集成方法进行了比较，结果表明：它能够显著提升相似度评估的准确性，并在某些情况下优于现有方法。因此，我们的研究展示了利用语法演化自动比较文本的潜力，并证明了将集成方法用于语义相似度任务的优势。说明本方法的源代码可从 https://github.com/jorge-martinez-gil/sesige 下载。