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• W3037610916 endingPage "113663" @default.
• W3037610916 startingPage "113663" @default.
• W3037610916 abstract "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." @default.
• W3037610916 created "2020-07-02" @default.
• W3037610916 creator A5014798665 @default.
• W3037610916 creator A5024343171 @default.
• W3037610916 date "2020-12-01" @default.
• W3037610916 modified "2023-10-18" @default.
• W3037610916 title "A novel method based on symbolic regression for interpretable semantic similarity measurement" @default.
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• W3037610916 doi "https://doi.org/10.1016/j.eswa.2020.113663" @default.
• W3037610916 hasPublicationYear "2020" @default.
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