DocSCAN: Unsupervised Text Classification via Learning from Neighbors

Understand

We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN).

• For each document, we obtain semantically informative vectors from a large pre-trained language model.
• Similar documents have proximate vectors, so neighbors in the representation space tend to share topic labels.
• Our learnable clustering approach uses pairs of neighboring datapoints as a weak learning signal.