Ensemble-Based Short Text Similarity: An Easy Approach for Multilingual Datasets Using Transformers and WordNet in Real-World Scenarios

When integrating data from different sources, there are problems of synonymy, different languages, and concepts of different granularity. This paper proposes a simple yet effective approach to evaluate the semantic similarity of short texts, especially keywords. The method is capable of matching keywords from different sources and languages by exploiting transformers and WordNet-based methods. Key features of the approach include its unsupervised pipeline, mitigation of the lack of context in keywords, scalability for large archives, support for multiple languages and real-world scenarios adaptation capabilities. The work aims to provide a versatile tool for different cultural heritage archives without requiring complex customization. The paper aims to explore different approaches to identifying similarities in 1- or n-gram tags, evaluate and compare different pre-trained language models, and define integrated methods to overcome limitations. Tests to validate the approach have been conducted using the QueryLab portal, a search engine for cultural heritage archives, to evaluate the proposed pipeline.