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
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Overview —

Targeted Applications to Tackle Any Entity


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



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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.
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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.
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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.
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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 —

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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




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For Data Scientists


  • Ready-to-use model zoo
  • Auto-generated analysis tools
  • Integrated Python notebooks
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For Domain Experts


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