Sentence-BERT, or sBERT, is a supervised, embedding-based machine learning approach used for specialized semantic tasks like topic modeling and detailed keyword classification. It belongs to the BERT family of transformer models but is optimized for sentence and short-form text comparison.
The model works by semantically understanding the meaning of each keyword and comparing it against predefined topic labels, matching the keyword to the topic with a high degree of precision. This capability makes sBERT superior to basic methods like string fuzzy matching because it relies on contextual semantic understanding rather than merely matching terms based on letter structure or distance.
sBERT is particularly effective in scenarios where high semantic precision is required, such as detailed classification of keywords into niche topics or in contextual keyword analysis. It is one of the ML approaches recommended for automatically labeling ranked content domains into search intent groups during advanced SERP analysis.