The problem of automatically measuring the degree of semantic similarity between textual expressions is a challenge that consists of calculating the degree of likeness between two text fragments that have none or few features in common according to human judgment. In recent times, several machine learning methods have been able to establish a new state-of-the-art regarding the accuracy, but none or little attention has been paid to their interpretability, i.e. the extent to which an end-user could be able to understand the cause of the output from these approaches. Although such solutions based on symbolic regression already exist in the field of clustering, we propose here a new approach which is being able to reach high levels of interpretability without sacrificing accuracy in the context of semantic textual similarity. After a complete empirical evaluation using several benchmark datasets, it is shown that our approach yields promising results in a wide range of scenarios.

自动测量文本表达之间的语义相似度的问题是一个挑战，包括根据人类的判断来计算两个没有或只有很少共同特征的文本片段之间的相似度。近年来，几种机器学习方法已经能够建立关于准确性的最新技术，但是对于它们的可解释性（即最终用户可以达到的程度），人们几乎没有关注了解这些方法产生输出的原因。尽管在聚类领域中已经存在基于符号回归的解决方案，但是我们在这里提出了一种新方法，该方法能够在不影响语义文本相似性的情况下达到较高的可解释性而又不牺牲准确性。