String Fuzzy Matching is classified as a supervised, heuristic, string-based machine learning method used for assessing the approximate similarity between text strings. This approach is favored for tasks requiring speed, lightweight implementation, and approximation, making it suitable for analysis of smaller datasets.
In semantic research, fuzzy matching can be used to compare search terms and identify variations or refinements that users make in successive queries. It is one of the techniques available for grouping keywords into topics, especially when applied in a rule-based fashion.
Despite its utility for quick tasks, string fuzzy matching is considered less effective than embedding-based approaches like Sentence-BERT (sBERT). This is because fuzzy matching only considers the structure of words and the distance between letters, meaning it fundamentally lacks semantic understanding or the ability to handle nuanced contextual relationships between terms.