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.

本文章为自然语言处理领域的语义相似度任务提出了一种自动设计语义相似度视角的方法，该方法使用了语法演化来选择和聚合最大化人类判断相关性的候选测量指标，此外，该方法在多个基准数据集上进行了评估，并表明它能够显著提高相似性评估准确性，并在某些情况下优于现有方法。