Computer scienceData scienceNLPMain NLP tasks

Text Classification

Multiple classes

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Choose the facts regarding handling multiple classes in text classification.

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For multi-label problems, binary cross-entropy with sigmoid activation is a suitable technique

Using a single loss function mean squared error is sufficient for multi-class text classification

Analyzing the presence of each class in your data is not important when working with multiple classes

Weighting technique and data augmentation can be used to address class imbalance in text classification

Generating additional data for rare classes is recommended when dealing with imbalanced datasets

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