Principle:Snorkel team Snorkel Labeling Function Definition

Knowledge Sources	Data Programming: Creating Large Training Sets Quickly Training Complex Models with Multi-Task Weak Supervision Snorkel Intro Tutorial
Domains	Weak_Supervision, Data_Programming, NLP
Last Updated	2026-02-14 20:00 GMT

Overview

A mechanism for encoding domain heuristics as programmatic labeling functions that assign noisy labels to unlabeled data points.

Description

Labeling Function Definition is the foundational step in the data programming paradigm. Rather than hand-labeling individual data points, domain experts encode their knowledge as small, modular functions called labeling functions (LFs). Each LF takes a data point as input and either assigns it a label (an integer class) or abstains (returns -1), indicating it has no opinion on that data point.

This approach addresses the critical bottleneck of obtaining labeled training data for supervised learning. Instead of requiring expensive manual annotation of every data point, LFs allow experts to express high-level patterns, heuristics, knowledge bases, and distant supervision signals as reusable functions. Individual LFs may be noisy and have limited coverage, but when combined through a generative label model, they can produce high-quality probabilistic training labels.

LFs can range from simple keyword rules to complex NLP-based patterns using spaCy for entity recognition, part-of-speech tagging, and dependency parsing.

Usage

Use this principle when you have unlabeled data and domain expertise that can be expressed as heuristic rules, patterns, or distant supervision signals. It is appropriate when:

• Manual labeling is too expensive or slow for the dataset size
• Multiple noisy signals can be combined to improve label quality
• Domain experts can articulate labeling heuristics programmatically
• Labels need to be updated as requirements change (LFs can be rewritten)

Theoretical Basis

In the data programming framework, a labeling function ${\lambda }_j$ maps a data point $x_i$ to a label space:

${\lambda }_j:{𝒳}\to \{-1,0,1,\dots ,k-1\}$

where $-1$ denotes abstention and $\{0,\dots ,k-1\}$ are the class labels.

The key insight is that each LF is a noisy voter with unknown accuracy. By collecting votes from multiple LFs into a label matrix $L\in {\mathbb{\mathbb{Z}}}^{n\times m}$ (where $n$ is the number of data points and $m$ the number of LFs), a generative model can learn each LF's accuracy without access to ground truth labels.

Pseudo-code:

# Abstract labeling function definition
def labeling_function(data_point):
    if heuristic_matches(data_point):
        return predicted_class  # integer label
    else:
        return ABSTAIN  # -1

Related Pages

Implemented By

• Implementation:Snorkel_team_Snorkel_LabelingFunction_Init