Semantic
Similarity
Tutorial
Semantic Similarity
Calculate semantic textual similarity using standard metrics like Cosine Similarity, Jaccard Index, and WordNet pathing.
Semantic Similarity
Semantic Similarity evaluates how closely related two pieces of text are in meaning, rather than relying on exact string or character duplication.
Common Similarity Metrics
1. Jaccard Similarity
A set/intersection overlap metric. Intersection(A,B) / Union(A,B).
2. Cosine Similarity
Measures the angle between two dense vectors. The gold standard for embeddings.
3. WordNet Path
Based on hierarchical taxonomy steps in a knowledge graph.
Cosine Similarity with Scikit-Learn
from sklearn.metrics.pairwise import cosine_similarity
# Example calculation between two vectors
similarity = cosine_similarity([vec_a], [vec_b])