This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addressing the challenge of vast datasets with limited labeled examples. Unlike traditional SSTC approaches that rely on a predefined ...
employing such models to enhance general text clustering has shown promising potential in boosting clustering effectiveness. However, current LLMs-driven approaches often act as black boxes in analyzing the processes of text clustering,
Clustering (where text strings are grouped by similarity) Recommendations (where items with related text strings are recommended) Anomaly detection (where outliers with little relatedness are identified) Diversity measurement (where similarity distributions are analyzed) Classification (where text strings are...
A classification finetuned model can only predict classes it has seen during training (for example, "spam" or "not spam", whereas an instruction-finetuned model can usually perform many tasks We can think of a classification-finetuned model as a very specialized model; in practice, it is ...
natural-language-processingtext-classificationclusteringpcatopic-modelingkmeansvector-graphicsnertsnescattertext UpdatedMar 21, 2022 HTML Modern computational linguistics for the Dead Sea Scrolls nlptext-classificationbertgnnqumrandead-sea-scrolls UpdatedMay 1, 2025 ...
“Instruction inputs” represent requests made by humans to the model. There are various types of instructions, such as classification, summarization, paraphrasing, etc. “Answer outputs” are the responses generated by the model following the instruction and aligning with human expectations. General ...
60 papers with code • 4 benchmarks • 6 datasets Scene Text Spotting is the combination of Scene Text Detection and Scene Text Recognition in an end-to-end manner. It is the ability to read natural text in the wild.Benchmarks Add a Result These leaderboards are used to track ...
By playing different roles, such as developers and testers, LLMs participate in discussions to reach a consensus on the existence and classification of vulnerabilities. IRIS (Li et al. 2024d) combines LLMs with static analysis to enable reasoning over the entire codebase. It automatically infers...
Text mining, also known as text data mining, is a technique that can mine high-quality information from countless texts. It’s based on Natural Language Processing (NLP) and combined with some of the typical data mining algorithms, such as classification, clustering, neural network, etc. Beside...
“Text Classification with Representation Models” demonstrates the flexibility of nongenerative models for classification. We will cover both task-specific models and embedding models. “Text Classification with Generative Models” is an introduction to generative language models as most of them can be ...