  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.
