Unsupervised Text Classification
5 papers with code • 4 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Text Classification
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Most implemented papers
Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches
Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches.
Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift
jitinkrishnan/Diversity-Based-Generalization
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At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data.
DocSCAN: Unsupervised Text Classification via Learning from Neighbors
dominiksinsaarland/DocSCAN
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KONVENS (WS) 2022
We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN).
Lex2Sent: A bagging approach to unsupervised sentiment analysis
Unsupervised text classification, with its most common form being sentiment analysis, used to be performed by counting words in a text that were stored in a lexicon, which assigns each word to one class or as a neutral word.
Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics
When successively retrieving documents on different predefined topics from publicly available and commonly used datasets, we achieved an average area under the receiver operating characteristic curve value of 0. 95 on one dataset and 0. 92 on another.
