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Weak Supervision to Merge Labeling Functions

In machine learning-based weak supervision, we leverage multiple labeling functions to generate labeled data, incorporating advanced techniques to merge an arbitrary number of labeling functions. Each labeling function provides noisy or imperfect annotations, and by intelligently combining them, we can generate high-quality labeled datasets without relying solely on manual annotation.

Combining Labeling Functions using Weak Supervision

The process of combining labeling functions using machine learning involves the following steps:

  1. Creating Labeling Functions: Define a set of labeling functions that capture different patterns or heuristics in the data. These functions can be rule-based, pattern-based, or derived from external sources. Each labeling function independently assigns labels to instances.

  2. Generating Initial Labels: Apply the labeling functions to your unlabeled data to generate initial noisy labels. Each labeling function assigns labels based on its coverage and heuristic, resulting in multiple candidate labels for each instance.

  3. Training a Generative Model: Train a generative model, such as a deep neural network or a probabilistic graphical model, using the noisy labeled data. The model learns to capture the correlations and dependencies among the labeling functions and the true labels.

  4. Estimating Label Probabilities: Use the trained generative model to estimate the probabilities of each label being correct for each instance. This estimation takes into account the labeling function outputs and their correlations.

  5. Merging Labeling Functions: Apply sophisticated techniques to merge the labeling function outputs based on their probabilities and other factors. This can involve thresholding, weighted voting, or more complex aggregation strategies.

  6. Iterative Refinement: Iterate on the labeling functions, generative model, and merging techniques to improve the quality of the combined labels. Incorporate user feedback, perform error analysis, and adjust the model and merging strategies accordingly.

By combining labeling functions using machine learning techniques, weak supervision enables the generation of accurate labeled data while effectively leveraging the strengths of each labeling function and compensating for their limitations.