Zero-shot Text Classification vs. Similarity-based Text Classification

This post is based on our NLPIR 2022 paper "Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches". You can read more details there. Unsupervised text classification approaches aim to perform categorization without using annotated data during training and therefore offer the potential to reduce annotation costs . Generally, unsupervised text classification approaches aim to map text to labels based on their textual description, without using annotated training data. To accomplish this, there exist mainly two categories of approaches. The first category can be summarized under similarity-based approaches.