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Text classification practice

**Text classification** assigns labels to documents or messages: topic, intent, sentiment, priority, language. SLP3 opens volume I with logistic regression and text classification before climbing into neural nets and LLMs—because classif...

What it is

Text classification assigns labels to documents or messages: topic, intent, sentiment, priority, language. SLP3 opens volume I with logistic regression and text classification before climbing into neural nets and LLMs—because classification remains one of the highest-ROI NLP jobs.

<!-- IMAGE: inbox → labeled buckets billing / tech / cancel -->

MEDIUM PRIORITYDIAGRAM
◷ IN PRODUCTION

Visual Spec & Architecture Diagram

Text classification pipeline: raw text → clean/tokenize → features or embeddings → classifier → label + confidence bar. Confusion-matrix inset 2×2 with fake spam/ham numbers.

Educational Focus: Connects practice chapter to measurable outcomes.

Why it matters

Routing and moderation often need classifiers, not chat. Simpler systems are easier to evaluate, calibrate, secure, and run cheaply at scale.

How it works (plain)

  1. Define labels that humans can apply consistently.
  2. Split data (train/validation/test) without leakage.
  3. Ship a baseline (TF-IDF + linear) before Transformers.
  4. Try embeddings / fine-tuned encoders if needed.
  5. Tune thresholds for imbalance; review edge cases.

CS224N still starts with word vectors and neural foundations—the math you use to debug a classifier shows up again in larger models.

Everyday example

A support inbox with five intents. A linear model at 50 ms latency may beat an LLM on cost and predictability for a stable taxonomy.

Try it

Define five intent labels for a support inbox. Note overlaps (“refund” vs “cancel”). Write two examples that would confuse annotators—then fix the guidelines.

Myths

⚠️ Myth: LLMs obsolete all classifiers.
✓ Reality: Classifiers win on cost/latency/control for stable taxonomies.
⚠️ Myth: Higher accuracy always means ready to ship.
✓ Reality: Check per-class recall on the costly error types.
⚠️ Myth: Sentiment models are universal ethics tools.
✓ Reality: Misusing “sentiment” for toxicity or HR decisions is a category error.

Sources