Named Entity Recognition

Build named entity recognition (NER) applications to recognize common or custom entities in a fraction of time without hand-labeling data using Snorkel Flow.

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Technology developed and deployed with the world’s leading organizations

Overview —

Targeted Applications to Tackle Any Entity

Train custom, high-accuracy NER models on your data without hand-labeling.

Faster, Lower-cost Development

Use programmatic labeling to develop high-quality AI applications in hours instead of spending weeks or months on expensive hand-labeling.

Rapidly Adaptable

Monitor for changes in the data, and rapidly adapt using built-in error analysis tools. Zoom in on errors to fine-tune training data & models with guided iteration.

High-accuracy Models

Leverage large amounts of labeled and unlabeled data, NLP primitives, and state-of-the-art model architectures to build high-accuracy models.

Flexible Integrations

Easily integrate labeling, training and analysis pipelines defined over diverse input types–text, PDF, HTML, and more–with downstream applications using APIs or a Python SDK.

Industry Use Cases —

NER Customized for Your Workflow

Build industry-specific AI applications combining state-of-the-art machine learning approaches with industry-specific best practices and last-mile connectors, all on an enterprise-scale platform.

FINANCIAL SERVICES

Contract Intelligence

Banks can classify contracts by terms and conditions to smoothly ensure regulatory complience.

TELECOM & CYBER

Customer Segmentation

Telecom organizations can classify customer usage documents to target promotional offers.

HEALTHCARE

Clinical Trial Matching

Biotech organizations can classify patient records to identify actionable clinical trial candidates.

INSURANCE

Risk Classification

Insurance underwriters can classify policy documents by behavioral or occupational variables to assess risk.

SOFTWARE

Search Engine Optimization

Software companies can recognize named entities in customer search queries and to optimize website content.

RETAIL

Product Recommendation

E-commerce sites can recognize entities in product descriptions (price, key words, etc.) to improve recommender systems.

Case Study —

Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales & marketing agents.

Problem

Rapidly changing sales goals make social media monitoring difficult to maintain.

Solution

Deployed Snorkel to replace months-long crowd-worker effort with cheap and fast template-based programmatic labeling.

Results

Better performance and major cost savings for sales & marketing and customer analytics.

6 MONTHS

of crowd-worker labels replaced

<0hrs

To develop the first custom ML model

+18.5%

precision percentage points

+0%

Accuracy for contract classification

+28.5%

coverage percentage points

Contracts processed in minutes

An End-to-end ML Platform —

Designed for Collaboration

For Data Scientists

Ready-to-use model zoo
Auto-generated analysis tools
Integrated Python notebooks

For Domain Experts

Rich data annotation suite
Intuitive, no-code labeling UI
Model error analysis reports

For Developers

Fully interoperable API and web UI
Write custom operators with Python SDK
Integrations to deploy models at scale

Resources —

Explore More About Snorkel

Learn more about groundbreaking techniques for programmatic labeling and weak supervision developed by Team Snorkel and the broader data science community.