Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution

TL;DR

This paper introduces an automated method using grammatical evolution to design semantic similarity ensembles that adaptively combine multiple measures, significantly improving accuracy over existing ensemble methods in NLP tasks.

Contribution

It presents a novel automated approach employing grammatical evolution to optimize semantic similarity ensembles, outperforming traditional ensemble techniques.

Findings

Outperforms existing ensemble methods in accuracy

Effective in adapting similarity measures to datasets

Source code available for replication

Abstract

Semantic similarity measures are a key component in natural language processing tasks such as document analysis, requirement matching, and user input interpretation. However, the performance of individual measures varies considerably across datasets. To address this, ensemble approaches that combine multiple measures are often employed. This paper presents an automated strategy based on grammatical evolution for constructing semantic similarity ensembles. The method evolves aggregation functions that maximize correlation with human-labeled similarity scores. Experiments on standard benchmark datasets demonstrate that the proposed approach outperforms existing ensemble techniques in terms of accuracy. The results confirm the effectiveness of grammatical evolution in designing adaptive and accurate similarity models. The source code that illustrates our approach can be downloaded from https://github.com/jorge-martinez-gil/sesige.