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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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6 MONTHS

of crowd-worker labels replaced in a fraction of time using Snorkel

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.

Use Cases

NER Customized for Your Workflow

FINANCIAL SERVICES

Account Identification
Banks can extract entities like client ID, IBAN number, and transaction details to automate account identification.

TELECOM & CYBER

Malicious Attack Prevention
Cybersecurity teams can automatically identify and track CVEs in real-time.

HEALTHCARE

Patient Identification
Hospitals can find entities in patient records to identify health patterns and improve diagnoses.

INSURANCE

Claims Processing
Insurance firms can recognize entities like insured person, loss amount, and policyholder to process claims faster.

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.

Result

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

6 MONTHS
of crowd-worker labels replaced

+18.5

precision percentage points

+28.5

coverage percentage points

How Snorkel Flow Works

Build Precise AI Apps for NER

Snorkel Flow allows you to recognize entities with ease and accuracy across an extensive collection of texts and documents. Collect and clean data with built-in pre-processors and taggers. Programmatically label documents with custom labeling functions, build end-to-end pipelines using your custom-trained models or tailored heuristics to perform entity tagging, linking, or classification. Deploy your NER application or seamlessly integrate it into downstream NLP tasks.

An End-to-end ML Platform

Designed for Collaboration

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Data Scientist Friendly

  • Integrated Jupyter notebooks
  • Instant analysis tools
  • Ready-to-use models
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Domain Expert Friendly

  • Intuitive, no-code UI
  • Rich dashboards and visualizations
  • Full-featured, push-button error analysis
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Developer Friendly

  • Platform access via Python SDK
  • Online or batch API deployment
  • Containerized software for cloud or on-premises deployments

Research

Based on Years of Novel Research
Learn more about groundbreaking techniques for machine learning and weak supervision developed by the Snorkel AI team at Stanford AI Lab and beyond.

RESEARCH

Training Complex Models with Multi-Task Weak Supervision

A. Ratner, et al, AAAI 2019

RESEARCH

Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision…

M. Chen, et al, 2020

RESEARCH

The Role of Massively Multi-Task and Weak Supervision in Software 2.0

A. Ratner, et al, CIDR 2019

RESEARCH

SwellShark: A Generative Model for Biomedical NER without Labeled Data

J. Fries, et al, 2017

RESEARCH

Software 2.0 and Snorkel: Going Beyond Hand-Labeled Data

C. Ré, KDD 2018 (invited)

RESEARCH

Snorkel: Rapid Training Data Creation With Weak Supervision

A. Ratner, et al. VLDB 2018

BLOG

Snorkel AI:
Putting Data First in ML Development

Alex Ratner

RESEARCH

Slice-based Learning: A Programming Model for Residual Learning…

V. Chen, et al, NeurIPS 2019

RESEARCH

Multi-Resolution Weak Supervision for Sequential Data

P. Varma, et al, NeurIPS 2019

RESEARCH

Medical Device Surveillance With Electronic Health Records

A. Callahan, et al. NPJ Digital Medicine 2019

RESEARCH

Learning Dependency Structures for Weak Supervision Models

P. Varma, et al, ICML 2019

RESEARCH

Fast & Three-rious: Speed Up Weak Supervision with Triplet Methods

D. Fu, et al, ICML 2020

RESEARCH

A Machine-compiled Database of Genome-wide Association Studies

V. Kuleshov, et al. Nature 2019

Accelerate your AI application development today

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