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Sparse Vector Handling

Learn Sparse Vector Handling for free with explanations, exercises, and a quick test (for NLP Engineer).

Published: January 5, 2026 | Updated: January 5, 2026

Why this matters

Who this is for

Engineers and analysts building classical NLP pipelines (classification, retrieval, clustering) who need fast, memory-safe feature handling.

Prerequisites

Comfort with vectors, dot products, and norms.
Basic text preprocessing (tokenization, stopwords).
Familiarity with Python or another language offering sparse matrices is helpful, but concepts transfer to any stack.

Learning path

Bag-of-Words and n-gram features.
TF-IDF weighting and normalization.
Sparse formats (CSR/CSC/COO) and conversions.
Cosine similarity and top-k retrieval.
Feature hashing and streaming updates.
Feature selection on sparse matrices.

Practical projects

Email topic classifier with TF-IDF + linear model, trained on CSR.
FAQ retriever using TF-IDF cosine, reporting top-3 contributing terms.
Streaming log tagger using hashing trick with fixed memory.

Next steps

Practice with larger corpora; confirm memory scales with non-zeros.
Try both vocabulary-based TF-IDF and hashing; compare quality/speed.
Add chi-square feature selection to shrink the feature space while keeping performance.

Quick test info

The quick test below is available to everyone. Only logged-in users have their progress saved.

Practice Exercises

2 exercises to complete

Instructions

Create a TF-IDF CSR matrix for the 5 documents below, L2-normalize rows, then compute cosine similarity between doc 0 and all others. Report the ranking (highest to lowest). Also, inspect the closest pair and list the top-3 terms that contribute most to their similarity by examining overlapping indices and their weighted values.

docs = [
  "nlp makes sparse vectors practical",
  "sparse vectors enable fast similarity",
  "dense vectors are large in memory",
  "similarity search uses sparse tf idf",
  "practical nlp prefers efficient sparse features"
]

Expected Output

Ranking should place doc 0 closest to a sparse-focused document (likely doc 1 or 4), with a plausible order like [0, 1, 4, 3, 2]. Top contributing terms include 'sparse', 'vectors', and possibly bigrams containing these.