mlsanityData to 7 Oct 2026

Text Understanding from Scratch

arXiv 1502.01710 ↗First posted 5 Feb 2015cs.LG cs.CLcited by 25

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Abstract (arXiv)

This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 2; 2023: 4; 2024: 3; 2025: 3; 2026: 2.

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  1. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  2. 2021Supporting Clustering with Contrastive Learningcited by 90
  3. 2022On the Relation between Sensitivity and Accuracy in In-context Learningcited by 71
  4. 2021Pairwise Supervised Contrastive Learning of Sentence Representationscited by 31
  5. 2021Mitigating Memorization of Noisy Labels via Regularization between Representationscited by 26
  6. 2021Virtual Augmentation Supported Contrastive Learning of Sentence Representationscited by 21
  7. 2021NewsEmbed: Modeling News through Pre-trained Document Representationscited by 6
  8. 2022Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embeddingcited by 6
  9. 2023Clustering-Aware Negative Sampling for Unsupervised Sentence Representationcited by 5
  10. 2023Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labelscited by 5
  11. 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5
  12. 2021SocialInteractionGAN: Multi-person Interaction Sequence Generationcited by 4
13 more
  1. 2021Short Text Clustering with Transformerscited by 3
  2. 2025Large Language Models for Healthcare Text Classification: A Systematic Reviewcited by 3
  3. 2021How to Train Your Agent to Read and Writecited by 2
  4. 2025Adversarial Text Generation with Dynamic Contextual Perturbationcited by 2
  5. 2021The Role of the Input in Natural Language Video Descriptioncited by 1
  6. 2024Contrastive Learning Subspace for Text Clusteringcited by 1
  7. 2021Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignmentcited by 0
  8. 2023CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clusteringcited by 0
  9. 2023Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Contentcited by 0
  10. 2024Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Modelscited by 0
  11. 2025VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecturecited by 0
  12. 2026PruneFuse: Efficient Data Selection via Weight Pruning and Network Fusioncited by 0
  13. 2026Random Label Prediction Heads for Studying Memorization in Deep Neural Networkscited by 0