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New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services.
J. MacQueen et al. , “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Math. Statist. Probability. , 1967, pp. 281–297
1967
Earlier work this paper cites.
E. H. Ruspini, “A new approach to clustering,” Inf. Technol. Control. , pp. 22–32, 1969
1969
Earlier work this paper cites.
K. C. Gowda and G. Krishna, “Agglomerative clustering using the concept of mutual nearest neighbourhood,” Pattern Recognit. , pp. 105–112, 1978
1978
Earlier work this paper cites.
A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Comput. Surv. , pp. 264–323, 1999
1999
Earlier work this paper cites.
K. Wagstaff, C. Cardie, S. Rogers, and S. Schrödl, “Constrained k-means clustering with background knowledge,” in Proc. Int. Conf. Mach. Learn. , 2001, pp. 577–584
2001
Earlier work this paper cites.
S. Basu, A. Banerjee, and R. J. Mooney, “Active semi-supervision for pairwise constrained clustering,” in Proc. SIAM Int. Conf. Data Mining . SIAM, 2004, pp. 333–344
2004
Earlier work this paper cites.
M. Bilenko, S. Basu, and R. J. Mooney, “Integrating constraints and metric learning in semi-supervised clustering,” in Proc. Int. Conf. Mach. Learn. , 2004, pp. 81–88
2004
Earlier work this paper cites.
D. Arthur and S. Vassilvitskii, “k-means++: the advantages of careful seeding,” in Proc. SIAM Int. Conf. Data Mining , 2007, pp. 1027–1035
2007
Earlier work this paper cites.
S. Basu, I. Davidson, and K. Wagstaff, Constrained Clustering: Advances in Algorithms, Theory, and Applications . USA:CRC Press, 2008
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” J. Mach. Learn. Res. , pp. 3371–3408, 2010
2010
Earlier work this paper cites.
H. W. Kuhn, “The hungarian method for the assignment problem,” Nav. Res. Logistics Quart. , pp. 83–97, 2010
2010
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” J. Mach. Learn. Res. , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
M. Cuturi, “Sinkhorn distances: Lightspeed computation of optimal transport,” Proc. Advances Neural Inf. Process. Syst. , pp. 2292–2300, 2013
2013
Earlier work this paper cites.
J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” in Proc. Conf. Empir. Methods Natural Lang. Process. , 2014, pp. 1532–1543
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , pp. 436–444, 2015
2015
Earlier work this paper cites.
J. Xu, P. Wang, G. Tian, B. Xu, J. Zhao, F. Wang, and H. Hao, “Short text clustering via convolutional neural networks,” in Proc. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. , 2015
2015
Earlier work this paper cites.
J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in Proc. Int. Conf. Mach. Learn. , 2016, pp. 478–487
2016
Earlier work this paper cites.
B. Yang, X. Fu, N. D. Sidiropoulos, and M. Hong, “Towards k-means-friendly spaces: Simultaneous deep learning and clustering,” in Proc. Int. Conf. Mach. Learn. , 2017, pp. 3861–3870
2017
Earlier work this paper cites.
J. Chang, L. Wang, G. Meng, S. Xiang, and C. Pan, “Deep adaptive image clustering,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 5879–5887
2017
Earlier work this paper cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 132–149
2018
Earlier work this paper cites.
Y.-C. Hsu, Z. Lv, and Z. Kira, “Learning to cluster in order to transfer across domains and tasks,” in Proc. Int. Conf. Learn. Representations , 2018
2018
Cited alongside, same era.
J. Schuurmans and F. Frasincar, “Intent classification for dialogue utterances,” IEEE Trans. Intell. Transp. Syst. , pp. 82–88, 2019
2019
Cited alongside, same era.
K. Han, A. Vedaldi, and A. Zisserman, “Learning to discover novel visual categories via deep transfer clustering,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 8400–8408
2019
Cited alongside, same era.
Y.-C. Hsu, Z. Lv, J. Schlosser, P. Odom, and Z. Kira, “Multi-class classification without multi-class labels,” in Proc. Int. Conf. Learn. Representations , 2019
2019
Cited alongside, same era.
E. Yu, J. Sun, J. Li, X. Chang, X.-H. Han, and A. G. Hauptmann, “Adaptive semi-supervised feature selection for cross-modal retrieval,” IEEE Trans. Multim. , vol. 21, no. 5, pp. 1276–1288, 2019
L. Qin, T. Xie, W. Che, and T. Liu, “A survey on spoken language understanding: Recent advances and new frontiers,” in Proc. 30th Int. Joint Conf. Artif. Intell. , 2021, pp. 4577–4584
2021
Later among the works it cites.
K. Han, S.-A. Rebuffi, S. Ehrhardt, A. Vedaldi, and A. Zisserman, “Autonovel: Automatically discovering and learning novel visual categories,” IEEE Trans. Pattern Anal. Mach. Intell. , pp. 6767–6781, 2021
2021
Later among the works it cites.
E. Fini, E. Sangineto, S. Lathuilière, Z. Zhong, M. Nabi, and E. Ricci, “A unified objective for novel class discovery,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 9284–9292
2021
Later among the works it cites.
H. Zhang, H. Xu, T.-E. Lin, and R. Lyu, “Discovering new intents with deep aligned clustering,” in Proc. AAAI Conf. Artif. Intell. , 2021, pp. 14 365–14 373
2021
Later among the works it cites.
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2019
Cited alongside, same era.
S. Larson, A. Mahendran, J. J. Peper, C. Clarke, A. Lee, P. Hill, J. K. Kummerfeld, K. Leach, M. A. Laurenzano, L. Tang, and J. Mars, “An evaluation dataset for intent classification and out-of-scope prediction,” in Proc. Conf. Empir. Methods Natural Lang. Process. , 2019, pp. 1311–1316
2019
Cited alongside, same era.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proc. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. , 2019, pp. 4171–4186
2019
Cited alongside, same era.
N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proc. Conf. Empir. Methods Natural Lang. Process. , 2019, pp. 3982–3992
2019
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proc. Int. Conf. Learn. Representations , 2019
2019
Cited alongside, same era.
T.-E. Lin, H. Xu, and H. Zhang, “Discovering new intents via constrained deep adaptive clustering with cluster refinement,” in Proc. 34th AAAI Conf. Artif. Intell. , 2020, pp. 8360–8367
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proc. Int. Conf. Mach. Learn. , 2020, pp. 1597–1607
2020
Cited alongside, same era.
K. Han, S.-A. Rebuffi, S. Ehrhardt, A. Vedaldi, and A. Zisserman, “Automatically discovering and learning new visual categories with ranking statistics,” in Proc. Int. Conf. Learn. Representations , 2020
2020
Cited alongside, same era.
H. Zhang, H. Xu, and T.-E. Lin, “Deep open intent classification with adaptive decision boundary,” in Proc. 35th AAAI Conf. Artif. Intell. , 2021, pp. 14 374–14 382
2021
Later among the works it cites.
D. Zhang, F. Nan, X. Wei, S.-W. Li, H. Zhu, K. R. McKeown, R. Nallapati, A. O. Arnold, and B. Xiang, “Supporting clustering with contrastive learning,” in Proc. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. , 2021, pp. 5419–5430
2021
Later among the works it cites.
T. Gao, X. Yao, and D. Chen, “Simcse: Simple contrastive learning of sentence embeddings,” in Proc. Conf. Empir. Methods Natural Lang. Process. , 2021, pp. 6894–6910
2021
Later among the works it cites.
Y. Li, P. Hu, Z. Liu, D. Peng, J. T. Zhou, and X. Peng, “Contrastive clustering,” in Proc. AAAI Conf. Artif. Intell. , 2021, pp. 8547–8555
2021
Later among the works it cites.
Y. Li, C. Gao, X. Du, H. Wei, H. Luo, D. Jin, and Y. Li, “Automatically discovering user consumption intents in meituan,” in Proc. 28th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining , 2022, pp. 3259–3269
2022
Later among the works it cites.
H. Li, X. Wang, Z. Zhang, J. Ma, P. Cui, and W. Zhu, “Intention-aware sequential recommendation with structured intent transition,” IEEE Trans. Knowl. Data Eng. , pp. 5403–5414, 2022
2022
Later among the works it cites.
S. Vaze, K. Han, A. Vedaldi, and A. Zisserman, “Generalized category discovery,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 7492–7501
2022
Later among the works it cites.
R. Kumar, M. Patidar, V. Varshney, L. Vig, and G. Shroff, “Intent detection and discovery from user logs via deep semi-supervised contrastive clustering,” in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. , 2022, pp. 1836–1853
2022
Later among the works it cites.
F. Wei, Z. Chen, Z. Hao, F. Yang, H. Wei, B. Han, and S. Guo, “Semi-supervised clustering with contrastive learning for discovering new intents,” arXiv: 2201.07604 , 2022
2022
Later among the works it cites.
Y. Zhang, H. Zhang, L.-M. Zhan, X.-M. Wu, and A. Lam, “New intent discovery with pre-training and contrastive learning,” in Proc. 60th Assoc. Comput. Linguistics , 2022, pp. 256–269
2022
Later among the works it cites.
Y. Ren, J. Pu, Z. Yang, J. Xu, G. Li, X. Pu, P. S. Yu, and L. He, “Deep clustering: A comprehensive survey,” arXiv: 2210.04142 , 2022
2022
Later among the works it cites.
D. Yuan, X. Chang, Z. Li, and Z. He, “Learning adaptive spatial-temporal context-aware correlation filters for uav tracking,” ACM Trans. Multimedia Comput. Commun. Appl. , vol. 18, no. 3, 2022
2022
Later among the works it cites.
H. Zhang, H. Xu, X. Wang, Q. Zhou, S. Zhao, and J. Teng, “Mintrec: A new dataset for multimodal intent recognition,” in Proc. of the 30th ACM Int. Conf. on Multimedia , 2022, pp. 1688–1697
2022
Later among the works it cites.
Y. Li, M. Yang, D. Peng, T. Li, J. Huang, and X. Peng, “Twin contrastive learning for online clustering,” Int. J. Comput. Vis. , vol. 130, no. 9, pp. 2205–2221, 2022
2022
Later among the works it cites.
H. Zhang, H. Xu, S. Zhao, and Q. Zhou, “Learning discriminative representations and decision boundaries for open intent detection,” IEEE/ACM Trans. Audio, Speech, and Lang. Process. , vol. 31, pp. 1611–1623, 2023
2023
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