Latest posts

  • 10-Ks information extraction case studies
    July 6, 2022Team Snorkel
    - Building NLP techniques to understand 10-Ks is time-consuming, costly, and challenging. In this post, Machine Learning Engineer, Aarti Bagul discusses three information extraction case studies on how banks around the world are building highly accurate NLP applications using Snorkel Flow's AI platform. From retail banking to hedge fund investing, NLP… ...
  • Introducing Cluster View: Instant data insight made actionable to speed AI development
    June 30, 2022Molly Friederich
    - Programmatic labeling moves a classic technique from interesting to high-impact So much of real-world AI development entails working with text data that’s messy — in fact, 80%+ of enterprise data is unstructured. And while state-of-the-art models get a lot of the glory, creating the training data that conveys what your model needs… ...
  • Data-centric approaches to multi-label classification
    June 29, 2022Kanyes Thaker
    - AI systems are well-suited to tasks involving recognizing and predicting data patterns. Supervised classification systems categorize unseen data into a finite set of discrete classes by learning from millions of hand-labeled labeled sample points. These classifiers are powerful business tools – they automate document sorting, customer sentiment analysis, sales performance,… ...
  • Data annotation guidelines and best practices
    June 28, 2022Anastassia Kornilova
    - What is data annotation? Data annotation refers to the process of categorizing and labeling data for training datasets. In order for a training dataset to be usable, it must be categorized appropriately and annotated for a specific use case. With Snorkel Flow, organizations can annotate high-quality labeled training data via… ...
  • 3 ways to use Snorkel’s Labeling Functions
    June 24, 2022Nic Acton
    - Labeling functions are fundamental building blocks of programmatic labeling that encode diverse sources of weak labeling signals to produce high-quality labeled data at scale. Let’s start with the core motivation for labeling functions: over time, every major commercial organization and government agency builds various valuable, often bespoke knowledge resources. These… ...
  • Clinical entity classification in electronic health records
    June 17, 2022Nazanin Makkinejad
    - Research recap: Ontology-driven weak supervision for clinical entity classification in electronic health records (EHRs)  In this post, I have summarized the research published in this academic paper, Ontology-driven weak supervision for clinical entity classification in electronic health records by Jason Fries et al. This paper was published in Nature Communications… ...
  • Building AI models for financial document processing best practices
    June 15, 2022Hoang Tran
    - Highlighting the best practices for building and deploying AI models for financial document processing applications AI has massive potential in the financial industry. Building AI models to automate information extraction, fraud detection, and compliance monitoring can provide efficient and faster responses and support repurposing domain experts' labor to more meaningful… ...
  • The benefits of programmatic labeling for trustworthy AI
    June 9, 2022Team Snorkel
    - The following post is based on a talk discussing the benefits of programmatic labeling for trustworthy AI, which was presented as part of the Trustworthy AI: A Practical Roadmap for Government event that took place this past April, with Snorkel AI Co-founder and Head of Technology, Braden Hancock. If you… ...
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