Linear Digressions Labels and Where To Find Them
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Feb 4, 2015 Explore the challenges of gathering labeled data for machine learning examples including lie detection using brain images and automated image captioning. Discover the difficulties in obtaining accurate labeling through platforms like Amazon's Mechanical Turk. Learn about the significance of semantic analysis in natural language processing and the value of labeled data in data science. Discover innovative strategies for obtaining labeled data and the scientific value of human contribution in projects like Galaxy Zoo and Higgshunters.org.
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Lie Detection Data Collection
- Researchers collected labeled data for lie detection by scanning people in fMRI machines as they lied or told the truth.
- They incentivized participants with money to try to fool the researchers during the scans.
Use Crowdsourcing for Labeling
- When you lack labeled data, you can use services like Mechanical Turk to pay humans to label it for you.
- To ensure quality, have multiple people label the same data and include trusted workers for benchmarking.
Multiple Labels Reveal Core Ideas
- Multiple captions for the same image reveal core concepts through overlapping terms despite phrasing differences.
- This variation helps identify consensus labels and guides machine learning to understand the core meaning.