Chapter 3. Labeling in Action
The previous chapter introduced the labeling functions of decorators. Those decorators convert the Python functions into weak classifiers, for the Snorkel framework. In this chapter, we will use those labeling functions to create labeling strategies and label one text dataset, and one image dataset.
As mentioned in the previous chapters, weak supervision and data programming are all about bringing together information from different sources and extracting information about various shapes of data. To label the text dataset, we will generate fake/real labels out of activities like:
Inspecting particular images embedded in article review websites, indicating through their color (red, green, yellow) the level of veracity of the article they are reviewing.
By summarizing online articles reviewing the news, and extracting their sentiment about the article.
By aggregating agreement ...