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Intro

0:00

Outline

0:19

Dark Data Extraction (DDE)

2:26

Extraction from the Scientific Literature

4:54

The Need for Lightweight Extraction

8:20

Example: Chemical-Disease Relation Extraction from Text

9:26

The Advent of Representation Learning Deep learning is achieving state-of-the-art results

15:44

Relation Extraction with Machine Learning

16:24

Training Data Creation: $$$, Slow, Static

16:53

Jupyter Interface

19:48

Labeling Functions

22:31

Data Programming Pipeline in Snorkel

23:31

Does Modeling the Noise Help?

27:21

Conclusion

28:26
Introduction to Snorkel by Stephen Bach
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2017Sep 12
Snorkel (https://hazyresearch.github.io/snorkel/) is a tool that automatically extracts information from unstructured data sources, such as the scientific literature and clinical notes, without using large, labeled training datasets, which are often lacking in biomedicine. In this workshop, participants learned about the Snorkel workflow through brief lectures and hands-on activities. This included: Writing labeling functions using pattern-matching and comparisons against existing dictionaries (e.g., Unified Medical Language System) Fitting and assessing a model to the labeling functions to generate the training data Hearing about examples of problems that can and cannot be addressed with Snorkel

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Mobilize Center

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