"algo-nlp-similarity"

Text Similarity

Overview

Text similarity measures how close two texts are in meaning or surface form. Lexical methods (Jaccard, cosine on TF-IDF) compare word overlap. Semantic methods (sentence embeddings) capture meaning even with different words. Choice depends on whether you need exact matching or meaning matching.

When to Use

Trigger conditions:

• Finding similar or duplicate documents in a collection
• Matching queries to FAQ answers or knowledge base entries
• Detecting plagiarism or content reuse

When NOT to use:

• For topic-level grouping (use topic modeling / LDA)
• For entity extraction from text (use NER)

Algorithm

IRON LAW: Lexical Similarity ≠ Semantic Similarity
"The car is fast" and "The automobile is speedy" have LOW lexical
similarity (different words) but HIGH semantic similarity (same meaning).
"Bank of the river" and "Bank account" have HIGH lexical similarity
but LOW semantic similarity. Choose the method that matches your
definition of "similar."

Phase 1: Input Validation

Determine: similarity type needed (lexical or semantic), text preprocessing requirements, scale (pairwise vs all-pairs vs query-to-corpus). Gate: Texts preprocessed, method selected.

Phase 2: Core Algorithm

Lexical methods:

• Jaccard: |A∩B| / |A∪B| on word sets
• Cosine on TF-IDF vectors: cos(θ) = (A·B) / (|A|×|B|)

Semantic methods:

• Sentence embeddings: encode texts with sentence-transformers (all-MiniLM-L6-v2)
• Cosine similarity on embedding vectors
• For large-scale: use FAISS or Annoy for approximate nearest neighbor search

Phase 3: Verification

Spot-check: highly similar pairs should be genuinely similar. Low-similarity pairs should be genuinely different. Check threshold calibration. Gate: Similarity scores align with human judgment on sample pairs.

Phase 4: Output

Return similarity scores or nearest neighbors.

Output Format

{
  "similarities": [{"text_a": "doc1", "text_b": "doc5", "score": 0.92, "method": "semantic_cosine"}],
  "metadata": {"method": "sentence-transformers", "model": "all-MiniLM-L6-v2", "pairs_computed": 500}
}