Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@Misc{martinezgil2023automatic,

• author = "Jorge Martinez-Gil",
• title = "Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution",
• howpublished = "arXiv",
• year = "2023",
• month = "30 " # aug,
• note = "v5",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Ensemble Learning, Grammatical Evolution, Semantic Similarity Measurement",
• eprint = "2307.00925",
• archiveprefix = "arXiv",
• primaryclass = "cs.CL",
• URL = "https://arxiv.org/abs/2307.00925",
• code_url = "https://github.com/jorge-martinez-gil/sesige",
• size = "30 pages",
• abstract = "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 maximises 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",
• notes = "'heavily based' on PonyGE2 \cite{DBLP:journals/corr/FentonMFFOH17}",

}

Genetic Programming entries for Jorge Martinez-Gil

Citations