Analysis and Optimization of fastText Linear Text Classifier

Understand

The paper [1] shows that simple linear classifier can compete with complex deep learning algorithms in text classification applications.

• Combining bag of words (BoW) and linear classification techniques, fastText [1] attains same or only slightly lower accuracy than deep learning algorithms [2-9] that are orders of magnitude slower.
• We proved formally that fastText can be transformed into a simpler equivalent classifier, which unlike fastText does not have any hidden layer.
• We also proved that the necessary and sufficient dimensionality of the word vector embedding space is exactly the number of document classes.