Papers Unsupervised Text Classification
“Unsupervised Text Classification” 태그가 달린 논문 14편 · 필터 해제
Dual Refinement Cycle Learning: Unsupervised Text Classification of Mamba and Community Detection on Text Attributed Graph
Pretrained language models offer strong text understanding capabilities but remain difficult to deploy in real-world text-attributed networks due to their heavy dependence on labeled data. Meanwhile, community detection …
Unsupervised Text ClassificationRepresentation LearningCommunity DetectionOne Size Does Not Fit All: Exploring Variable Thresholds for Distance-Based Multi-Label Text Classification
Distance-based unsupervised text classification is a method within text classification that leverages the semantic similarity between a label and a text to determine label relevance. This method provides numerous benefit…
Unsupervised Text ClassificationMulti-Label Text ClassificationMulti-Label ClassificationInformation RetrievalShuffle & Divide: Contrastive Learning for Long Text
We propose a self-supervised learning method for long text documents based on contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple text augmentation algorithm that sets up a pretext task requir…
Contrastive LearningDocument EmbeddingSelf-Supervised LearningText Augmentation+3Text classification in shipping industry using unsupervised models and Transformer based supervised models
Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the perfor…
ClassificationSelf-Learningtext-classificationText Classification+2Evaluating 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. Similarity-based approaches attempt to classify instances based on …
Classificationtext-classificationText ClassificationUnsupervised Text Classification+1Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics
In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised approach requires only a small number of key…
Document ClassificationRetrievalUnsupervised Text ClassificationWorld KnowledgeLex2Sent: 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 w…
ClassificationDecoderGPUSentiment Analysis+5DocSCAN: Unsupervised Text Classification via Learning from Neighbors
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-…
ClassificationClusteringGeneral ClassificationLanguage Modeling+6Exclusive Topic Modeling
We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) deliver well-structured topics with exclu…
text-classificationText ClassificationUnsupervised Text ClassificationConcentrated Document Topic Model
We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution. In particular, an exponential entropy penalty is …
modeltext-classificationText ClassificationUnsupervised Text ClassificationLearning Interpretable and Discrete Representations with Adversarial Training for Unsupervised Text Classification
Learning continuous representations from unlabeled textual data has been increasingly studied for benefiting semi-supervised learning. Although it is relatively easier to interpret discrete representations, due to the di…
General Classificationtext-classificationText ClassificationUnsupervised Text ClassificationDiversity-Based Generalization for Unsupervised Text Classification under Domain Shift
Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification …
ClassificationDiversityDomain AdaptationGeneral Classification+4Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings
Text classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach ad…
ClassificationGeneral ClassificationText Categorizationtext-classification+3Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification
The abundance of text data being produced in the modern age makes it increasingly important to intuitively group, categorize, or classify text data by theme for efficient retrieval and search. Yet, the high dimensionalit…
ClusteringDocument ClassificationGeneral ClassificationRetrieval+4