Sentence BERT Semantic Similarity

Sentence-BERT semantic similarity uses a transformer network fine-tuned with a siamese architecture to encode sentences as dense vectors, so that cosine similarity between vectors reflects similarity in meaning.

Sentence-BERT 의미 유사도는 샴(siamese) 구조로 미세조정된 트랜스포머로 문장을 밀집 벡터로 변환하여, 벡터 간 코사인 유사도가 의미의 유사성을 반영하도록 하는 방법이다.

How do I measure how similar in meaning two pieces of text are?

두 텍스트가 의미상 얼마나 비슷한지 어떻게 측정하나요?

Sentence-BERT encodes each sentence or short text as an embedding vector, and the cosine similarity between vectors measures how close the texts are in meaning. It fits semantic search, duplicate detection and grouping open-ended responses, even when texts share few words.

When to use Sentence BERT Semantic Similarity

• A column of sentences or short texts such as survey answers or reviews
• Finding paraphrases, near-duplicates or the most similar responses
• Semantic search or clustering of texts by meaning

When to use something else

• Matching on exact words or keywords only; use TF-IDF with cosine similarity
• Long documents beyond the model's input length; split into passages or use document-level models

Assumptions and requirements

• A pretrained model suited to the language and domain
• Texts within the model's maximum input length
• Basic text cleaning
• Similarity scores are relative, not calibrated probabilities

What the analysis reports

• Sentence embeddings
• Pairwise cosine similarity matrix
• Most similar text pairs
• Clusters or 2D projections of embeddings

How to report it

Semantic similarity between [text set A] and [text set B] was computed with Sentence-BERT embeddings, yielding a mean cosine similarity of [value] (SD [value]).

Related methods

• TF-IDF Cosine Similarity
• FastText Vector Embedding
• BERTopic

Run Sentence BERT Semantic Similarity on Quorant