mlsanityData to 13 Oct 2026

DOC: Deep Open Classification of Text Documents

arXiv 1709.08716 ↗First posted 25 Sep 2017cs.CLcited by 109

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Abstract (arXiv)

Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.

Key facts. Cited by 109 papers in the map (data through 13 Oct 2026). First posted on arXiv 25 Sep 2017.

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Citing papers per year: 2021: 27; 2022: 24; 2023: 26; 2024: 15; 2025: 9; 2026: 8.

109 papers.

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  1. 2023Towards Open Vocabulary Learning: A Surveycited by 165
  2. 2021Adversarial Reciprocal Points Learning for Open Set Recognitioncited by 153
  3. 2021Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPcited by 151
  4. 2021Learning Placeholders for Open-Set Recognitioncited by 121
  5. 2021Contrastive Out-of-Distribution Detection for Pretrained Transformerscited by 70
  6. 2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
  7. 2023Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractorscited by 44
  8. 2021Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learningcited by 42
  9. 2021Out-of-Scope Intent Detection with Self-Supervision and Discriminative Trainingcited by 42
  10. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  11. 2023Progressive Open Space Expansion for Open-Set Model Attributioncited by 32
  12. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
38 more
  1. 2021A Survey on Open Set Recognitioncited by 25
  2. 2024MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversationscited by 25
  3. 2021TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognitioncited by 24
  4. 2022Difficulty-Aware Simulator for Open Set Recognitioncited by 24
  5. 2021Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detectioncited by 22
  6. 2021GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentationcited by 20
  7. 2021Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approachcited by 20
  8. 2022Learning Discriminative Representations and Decision Boundaries for Open Intent Detectioncited by 15
  9. 2024Predictive Churn with the Set of Good Modelscited by 15
  10. 2022Towards Textual Out-of-Domain Detection without In-Domain Labelscited by 14
  11. 2024Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detectioncited by 14
  12. 2024Open World Object Detection: A Surveycited by 13
  13. 2023SOAR: Scene-debiasing Open-set Action Recognitioncited by 12
  14. 2021Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classescited by 11
  15. 2022Estimating Soft Labels for Out-of-Domain Intent Detectioncited by 11
  16. 2022Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Textcited by 11
  17. 2021Collective Decision of One-vs-Rest Networks for Open Set Recognitioncited by 10
  18. 2023Glocal Energy-based Learning for Few-Shot Open-Set Recognitioncited by 10
  19. 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognitioncited by 10
  20. 2021Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalizationcited by 9
  21. 2022Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemblecited by 9
  22. 2021Self-Supervised Features Improve Open-World Learningcited by 8
  23. 2024IntentGPT: Few-shot Intent Discovery with Large Language Modelscited by 8
  24. 2023Long-Tailed Question Answering in an Open Worldcited by 7
  25. 2021Energy-based Unknown Intent Detection with Data Manipulationcited by 6
  26. 2023Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contextscited by 6
  27. 2021An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attackscited by 5
  28. 2022Shift-Robust Node Classification via Graph Adversarial Clusteringcited by 5
  29. 2022Learning to Classify Open Intent via Soft Labeling and Manifold Mixupcited by 5
  30. 2022UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learningcited by 5
  31. 2023G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknownscited by 5
  32. 2023Open-World Lifelong Graph Learningcited by 5
  33. 2025scAgent: Universal Single-Cell Annotation via a LLM Agentcited by 5
  34. 2021ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detectioncited by 4
  35. 2023Managing the unknown: a survey on Open Set Recognition and tangential areascited by 4
  36. 2024Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognitioncited by 4
  37. 2021Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarioscited by 3
  38. 2022Learning Better Intent Representations for Financial Open Intent Classificationcited by 3

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