Weak Supervision Basics

Why this matters

As an NLP Engineer, you often need labeled text to train models, but expert labels are expensive and slow. Weak supervision lets you combine multiple noisy signals—like heuristics, keywords, distant supervision, and model predictions—to create training labels faster and at scale. This is critical for bootstrapping prototypes, handling new domains, and keeping datasets fresh.

• Real tasks: bootstrap a classifier for routing support tickets; pre-label data for entity extraction; quickly adapt sentiment models to a new product line.
• Outcome: ship earlier, iterate cheaper, and reserve expert time for auditing and hard cases.

Concept explained simply

Mental model: imagine a committee of imperfect voters (labeling functions). Each voter may vote, abstain, or be wrong. A referee (label model) learns which voters are trustworthy and produces a single, probabilistic label per example.

Core building blocks

• Labeling Function (LF): a small program or rule returning one label, another label, or ABSTAIN.
• Sources for LFs:
  • Heuristics: keywords, regexes, lexicons, simple NLP cues (negation, emojis).
  • Distant supervision: align to external resources like gazetteers or known dictionaries.
  • Programmatic weak labels: predictions from existing models or zero-shot prompts.
  • Metadata: sender domain, language, URL patterns, channel.
• Key metrics: coverage (how many examples an LF labels), overlap (how often LFs label the same item), conflict (how often they disagree), estimated LF accuracy.
• Label model: estimates LF accuracies from agreements/disagreements and outputs probabilistic labels.

Worked examples

Designing effective labeling functions

1. Write narrow, precise rules; prefer abstaining to avoid noisy coverage.
2. Target complementary signals (keywords, structure, metadata, context).
3. Add at least one high-precision LF per class; add broader LFs for coverage.
4. Measure coverage, overlap, and conflict; iterate to reduce unhelpful conflicts.
5. Run a label model to get probabilistic labels; optionally threshold for training.

• Checklist before training:
  • At least 5–10 LFs with mixed precision/coverage profiles.
  • Each LF can ABSTAIN explicitly.
  • No single LF dominates the entire dataset.
  • A small gold set (even 50–100 items) for validation is highly useful.

Quality and diagnostics

Suppose 3 LFs labeled 100 texts:

• Coverage: LF1=60%, LF2=35%, LF3=25%.
• Overlap: 20% of items got labels from 2 or more LFs.
• Conflict: Of overlapped items, 30% had disagreeing labels.

Interpretation:

• High conflict can be good if it reflects informative differences (e.g., negation-aware vs simple keywords). The label model learns which LF is usually right.
• If a single LF has huge coverage and low precision, it may drown out others. Prefer to tighten it or add abstain conditions.

Exercises you can do now

These mirror the tasks in the Exercises section below. Try them here first.

[1] "I was double-charged this month and support hasn't replied."
[2] "Loving the new update!"
[3] "The app crashes after login and shows error 502."
[4] "Quick question about integrating with Zapier."
[5] "This is unacceptable. I've asked for a refund twice."

Idx  LF1  LF2  LF3
1    C    C    A
2    O    A    A
3    A    C    O
4    C    A    C
5    A    A    A
6    O    O    C

• Before you submit:
  • Each LF explicitly handles abstain.
  • Your rules are not duplicates; they target different signals.
  • You documented expected precision vs coverage for each LF in one sentence.

Common mistakes and self-check

• Too few LFs or all very similar → add diverse sources (metadata, patterns, distant supervision).
• Never abstaining → noisy coverage. Add conservative conditions and ABSTAIN for ambiguous cases.
• Letting one broad LF dominate → tighten its conditions or lower its weight via the label model.
• No small gold set → hard to sanity-check. Label 50–100 items to verify directionally correct results.

Practical projects

• Project A: Weakly label a 3-class intent dataset (BILLING, TECH, OTHER). Build 8–12 LFs, train a small classifier on the label-model outputs, and evaluate on a 100-item gold set.
• Project B: Named entity typing for ORG vs PERSON on news headlines using gazetteers + patterns. Compare majority vote vs label model performance.

Who this is for and prerequisites

• Who this is for: NLP engineers, data scientists, ML practitioners working with limited labeled text.
• Prerequisites: basic Python or rule-writing mindset, understanding of classification, awareness of precision/recall trade-offs.

Learning path

1. Start with 5–10 simple LFs covering each class.
2. Measure coverage/overlap/conflict; add targeted LFs to fix blind spots.
3. Run a label model for probabilistic labels; iterate.
4. Create a small gold set to validate and calibrate thresholds.
5. Train a downstream classifier on weak labels; compare to gold.

Next steps

• Extend to multi-label or hierarchical tasks.
• Add calibration: align probabilities to the gold set.
• Set up periodic re-labeling as data drifts.

Mini challenge

Design three LFs for toxic comment detection: one high-precision, one high-coverage, one context-aware (negation or quotes). Explain when each should ABSTAIN and how you would test conflicts.

Take the quick test

Anyone can take the test for free. Only logged-in users will have their progress saved.