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| Adaptive Granular-Ball and Pure Cluster Splitting for Open Intent Classification |
| WANG Jingkai1, LI Yanhua1, LIU Jiafen1, WANG Xiangkun1, YANG Xin1 |
| 1. School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130 |
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Abstract Open intent classification is a critical task in building intelligent dialogue systems, and it is intended to detect unknown intents accurately while recognizing the known ones. However, existing methods are limited in modeling complex semantic structures and fail to represent the diversity within intent classes, resulting in inter-class confusion. To address this issue, a method for adaptive granular-ball and pure cluster splitting for open intent classification(AGPCS-OIC)is proposed. First, adaptive granular-ball clustering is applied to construct multi-center subclass structures reflecting the true data distribution, and thus intra-class heterogeneity is captured more effectively. Then, a structural sparsity-based pure cluster splitting strategy is introduced to further divide loosely bounded but high-purity granular-balls. The expressiveness of decision boundaries and the ability to reject the unknown intents are enhanced. Additionally, a granular-ball-aware contrastive learning mechanism is incorporated. Structural-level semantic pairs are built with granular-ball centers serving as anchors, to guide the model to improve intra-class compactness and inter-class separability in the feature space. Experiments show that AGPCS-OIC achieves strong performance on multiple open intent classification datasets.
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Received: 10 June 2025
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| Fund:National Natural Science Foundation of China(No.62476228), Science and Technology Support Program of Sichuan Province(No.2024ZYD0180,2024YFHZ0024) |
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Corresponding Authors:
LIU Jiafen, Ph.D., associate professor. Her research interests include data analysis and processing, information security.
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About author:: WANG Jingkai, Master student. His research interests include multi-granularity lear-ning, open world learning and few-shot lear-ning.
LI Yanhua, Ph.D. candidate. Her research interests include three-way decision, multi-granularity learning and continual lear-ning.
WANG Xiangkun, Master student. His research interests include continual learning, open world learning and few-shot learning.
YANG Xin, Ph.D., professor. His research interests include trustworthy federated learning and continual learning. |
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[1] ZHENG Y H, CHEN G Y, HUANG M L. Out-of-Domain Detection for Natural Language Understanding in Dialog Systems. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020, 28: 1198-1209.
[2] ABRO W A, QI G L, ALI Z, et al. Multi-turn Intent Determination and Slot Filling with Neural Networks and Regular Expressions. Knowledge-Based Systems, 2020, 208. DOI: 10.1016/j.knosys.2020.106428.
[3] FIRDAUS M, KUMAR A, EKBAL A, et al. A Multi-task Hierarchical Approach for Intent Detection and Slot Filling. Knowledge-Based Systems, 2019, 183. DOI: 10.1016/j.knosys.2019.07.017.
[4] YANG P, JI D, AI C M, et al. AISE: Attending to Intent and Slots Explicitly for Better Spoken Language Understanding. Knowledge-Based Systems, 2021, 211. DOI: 10.1016/j.knosys.2020.106537.
[5] SHU L, XU H, LIU B. DOC: Deep Open Classification of Text Documents // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2017: 2911-2916.
[6] BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record, 2000, 29(2): 93-104.
[7] JAIN L P, SCHEIRER W J, BOULT T E, et al. Multiclass Open Set Recognition Using Probability of Inclusion // Proc of the 13th European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 393-409.
[8] HENDRYCKS D, GIMPEL K. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks[C/OL]. [2025-05-15]. https://arxiv.org/pdf/1610.02136.
[9] CHEN G Y, PENG P X, WANG X Q, et al. Adversarial Reciprocal Points Learning for Open Set Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(11): 8065-8081.
[10] XU K Y, REN T Z, ZHANG S K, et al. Unsupervised Out-of-Domain Detection via Pre-trained Transformers // Proc of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg, USA: ACL, 2021: 1052-1061.
[11] ZHANG H L, XU H, LIN T E. Deep Open Intent Classification with Adaptive Decision Boundary. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(16): 14374-14382.
[12] ZHANG H L, XU H, ZHAO S J, et al. Learning Discriminative Representations and Decision Boundaries for Open Intent Detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 31: 1611-1623.
[13] LIU X K, LI J Q, MU J J,et al. Effective Open Intent Classification with K-Center Contrastive Learning and Adjustable Decision Boundary. Proceedings of the AAAI Conference on Artificial Inte-lligence, 2023, 37(11): 13291-13299.
[14] LI Y H, OUYANG X C, PAN C F, et al. Multi-granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary. Proceedings of the AAAI Conference on Artificial Intelligence, 2025, 39(23): 24512-24520.
[15] BAI H Y, MING Y F, KATZ-SAMUELS J, et al. HYPO: Hyperspherical Out-of-Distribution Generalization[C/OL].[2025-05-15]. https://arxiv.org/pdf/2402.07785.
[16] XIA S Y, LIU Y S, DING X, et al. Granular Ball Computing Classifiers for Efficient, Scalable and Robust Learning. Information Sciences, 2019, 483: 136-152.
[17] XIA S Y, ZHANG H, LI W H, et al. GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(3): 1231-1242.
[18] BENDALE A, BOULT T E. Towards Open Set Deep Networks // Proc of the IEEE Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2016: 1563-1572.
[19] CHEN L. Topological Structure in Visual Perception. Science, 1982, 218(4573): 699-700.
[20] XIA S Y, ZHENG S Y, WANG G Y, et al. Granular Ball Sampling for Noisy Label Classification or Imbalanced Classification. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(4): 2144-2155.
[21] CASANUEVA I, TEMČINAS T, GERZ D, et al. Efficient Intent Detection with Dual Sentence Encoders // Proc of the 2nd Workshop on Natural Language Processing for Conversational AI. Stroudsburg, USA: ACL, 2020: 38-45.
[22] LARSON S, MAHENDRAN A, PEPER J J, et al. An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction // Proc of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, USA: ACL, 2019: 1311-1316.
[23] LIU X K, ESHGHI A, SWIETOJANSKI P, et al. Benchmarking Natural Language Understanding Services for Building Conversational Agents // Proc of the 10th International Workshop on Spoken Dialogue Systems. Berlin, Germany: Springer, 2021: 165-183.
[24] COUCKE A, SAADE A, BALL A, et al. Snips Voice Platform: An Embedded Spoken Language Understanding System for Privateby-Design Voice Interfaces[C/OL].[2025-05-15]. https://arxiv.org/pdf/1805.10190.
[25] XU J M, WANG P, TIAN G H, et al. Short Text Clustering via Convolutional Neural Networks // Proc of the 1st Workshop on Vector Space Modeling for Natural Language Processing. Stroudsburg, USA: ACL, 2015: 62-69.
[26] ZHOU Y H, WANG P Y, LIU P J, et al. The Open-World Lottery Ticket Hypothesis for OOD Intent Classification // Proc of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation. Stroudsburg, USA: ACL, 2024: 15988-15999. |
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2025, Vol. 38 
