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.
Request demo
Image
Technology developed and deployed with the world’s leading organizations
Image
Image
Image
Image
Image
Image
Image
Image
Overview —

Targeted Applications to Tackle Any Entity

Train custom, high-accuracy NER models on your data without hand-labeling.
Image
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.
Image
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.
Image
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.
Image
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 —
Image
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
0K
Contracts processed in minutes
Read more
An End-to-end ML Platform —

Designed for Collaboration

Image

For Data Scientists

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

For Domain Experts

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

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.
Read more