Related papers: Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches

Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches

URL: http://arxiv.org/abs/2211.16285v1
Date: Tue, 29 Nov 2022 15:14:47 GMT
Title: Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches
Authors: Tim Schopf, Daniel Braun, Florian Matthes
Abstract summary: Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. This paper conducts a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes.
Score: 0.6767885381740952
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE and SBERT-based baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.