NDLI: Snorkel: Fast Training Set Generation for Information Extraction

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Snorkel: Fast Training Set Generation for Information Extraction

Content Provider	ACM Digital Library
Author	Bach, Stephen H. Ratner, Alexander J. Ré, Chris Ehrenberg, Henry R.
Abstract	State-of-the art machine learning methods such as deep learning rely on large sets of hand-labeled training data. Collecting training data is prohibitively slow and expensive, especially when technical domain expertise is required; even the largest technology companies struggle with this challenge. We address this critical bottleneck with Snorkel, a new system for quickly creating, managing, and modeling training sets. Snorkel enables users to generate large volumes of training data by writing labeling functions, which are simple functions that express heuristics and other weak supervision strategies. These user-authored labeling functions may have low accuracies and may overlap and conflict, but Snorkel automatically learns their accuracies and synthesizes their output labels. Experiments and theory show that surprisingly, by modeling the labeling process in this way, we can train high-accuracy machine learning models even using potentially lower-accuracy inputs. Snorkel is currently used in production at top technology and consulting companies, and used by researchers to extract information from electronic health records, after-action combat reports, and the scientific literature. In this demonstration, we focus on the challenging task of information extraction, a common application of Snorkel in practice. Using the task of extracting corporate employment relationships from news articles, we will demonstrate and build intuition for a radically different way of developing machine learning systems which allows us to effectively bypass the bottleneck of hand-labeling training data.
Starting Page	1683
Ending Page	1686
Page Count	4
File Format	PDF
ISBN	9781450341974
DOI	10.1145/3035918.3056442
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Weak supervision Structured information extraction
Content Type	Text
Resource Type	Article

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