Semantic Similarity — Measuring Meaning Between Texts
Semantic Similarity Analysis · Also known as: semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi
Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.
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When to use it
Use semantic similarity when you have pairs of texts and want to know how close they are in meaning rather than how many words they share — for plagiarism and duplicate detection, paragraph matching, or information retrieval. It needs an embedding model that supports the language of the texts and at least a small set of text pairs to compare; with no text data it cannot run.
Strengths & limitations
- Captures meaning rather than surface word overlap, so paraphrases with different wording still score as similar.
- Works across languages when a multilingual embedding model is used.
- Produces a single, comparable score per pair that is easy to threshold for tasks like duplicate detection or retrieval.
- Requires text pairs to compare; it does not apply when there is no text data.
- Quality depends entirely on the embedding model matching the language of the texts.
- A similarity score reflects the model's notion of meaning, which may not align with a domain-specific or legal definition of 'the same'.
Frequently asked
How is this different from comparing shared words?
Word-overlap measures only count matching tokens, so they miss paraphrases that mean the same thing with different words. Semantic similarity embeds each text into a meaning space and compares the vectors, so closeness in meaning is captured even when the wording differs.
Does it work in languages other than English?
Yes, provided the embedding model supports the target language. A multilingual sentence-embedding model is used when the corpus is not English; applying a model that does not cover the language gives unreliable scores.
How do I turn a similarity score into a decision?
Choose a threshold suited to the task — for example a high cutoff for flagging likely copies — and inspect borderline pairs before acting. The raw score is comparable across pairs but is not itself a verdict.
How is the similarity score computed?
Each text is encoded into a vector by an embedding model, and the two vectors are compared, most commonly with cosine similarity, yielding a single bounded number where higher values mean closer meaning.
Sources
How to cite this page
ScholarGate. (2026, June 1). Semantic Similarity Analysis. ScholarGate. https://scholargate.app/en/text-mining/semantic-similarity
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- BERT EmbeddingsText mining↔ compare
- Document ClusteringText mining↔ compare
- Sentiment AnalysisText mining↔ compare
- TF-IDFText mining↔ compare