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A Survey on Neural Topic Models: Methods, Applications, and Challenges (2401.15351v2)

Published 27 Jan 2024 in cs.CL, cs.AI, and cs.IR

Abstract: Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new research field -- Neural Topic Models (NTMs). Different from conventional topic models, NTMs directly optimize parameters without requiring model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and bilingual documents. We also discuss a wide range of popular applications built on NTMs. Finally, we highlight the challenges confronted by NTMs to inspire future research. We accompany this survey with a repository for easier access to the mentioned paper resources: https://github.com/bobxwu/Paper-Neural-Topic-Models.

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References (154)
  1. David Alvarez-Melis and Tommi S Jaakkola. 2017. Tree-structured decoding with doubly-recurrent neural networks. In International Conference on Learning Representations.
  2. Dimo Angelov. 2020. Top2vec: Distributed representations of topics. arXiv preprint arXiv:2008.09470.
  3. Topic modeling techniques for text mining over a large-scale scientific and biomedical text corpus. International Journal of Ambient Computing and Intelligence (IJACI), 13(1):1–18.
  4. Neural relational topic models for scientific article analysis. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 27–36.
  5. Pre-training is a hot topic: Contextualized document embeddings improve topic coherence. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 759–766.
  6. Cross-lingual contextualized topic models with zero-shot learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1676–1683, Online. Association for Computational Linguistics.
  7. The acl anthology reference corpus: A reference dataset for bibliographic research in computational linguistics. In LREC.
  8. David Blei and John Lafferty. 2006a. Correlated topic models. Advances in neural information processing systems, 18:147.
  9. David M Blei. 2012. Probabilistic topic models. Communications of the ACM, 55(4):77–84.
  10. The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. Journal of the ACM (JACM), 57(2):1–30.
  11. Variational inference: A review for statisticians. Journal of the American statistical Association, 112(518):859–877.
  12. David M Blei and John D Lafferty. 2006b. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning, pages 113–120.
  13. Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan):993–1022.
  14. Gerlof Bouma. 2009. Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, pages 31–40.
  15. Applications of topic models, volume 11. now Publishers Incorporated.
  16. Wray L Buntine. 2009. Estimating likelihoods for topic models. ACML, 9:51–64.
  17. Sophie Burkhardt and Stefan Kramer. 2019. Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model. J. Mach. Learn. Res., 20(131):1–27.
  18. A novel neural topic model and its supervised extension. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29.
  19. Neural Models for Documents with Metadata. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 2031–2040.
  20. Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems, pages 288–296.
  21. Topicbert for energy efficient document classification. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1682–1690.
  22. Nonlinear structural equation model guided gaussian mixture hierarchical topic modeling. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10377–10390.
  23. Hierarchical neural topic modeling with manifold regularization. World Wide Web, 24:2139–2160.
  24. Tree-structured topic modeling with nonparametric neural variational inference. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2343–2353.
  25. Rob Churchill and Lisa Singh. 2022. The evolution of topic modeling. ACM Computing Surveys, 54(10s):1–35.
  26. Jeremy Costello and Marek Z Reformat. 2023. Reinforcement learning for topic models. arXiv preprint arXiv:2305.04843.
  27. Neural dynamic focused topic model. arXiv preprint arXiv:2301.10988.
  28. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  29. The dynamic embedded topic model. arXiv preprint arXiv:1907.05545.
  30. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8:439–453.
  31. TopicRNN: A recurrent neural network with long-range semantic dependency. In International Conference on Learning Representations.
  32. Thanh-Nam Doan and Tuan-Anh Hoang. 2021. Benchmarking neural topic models: An empirical study. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4363–4368, Online. Association for Computational Linguistics.
  33. Caitlin Doogan and Wray Buntine. 2021. Topic model or topic twaddle? re-evaluating semantic interpretability measures. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3824–3848.
  34. Bayesian progressive deep topic model with knowledge informed textual data coarsening process. In International Conference on Machine Learning, pages 8731–8746. PMLR.
  35. Sawtooth factorial topic embeddings guided gamma belief network. In International Conference on Machine Learning, pages 2903–2913. PMLR.
  36. Bayesian deep embedding topic meta-learner. In International Conference on Machine Learning, pages 5659–5670. PMLR.
  37. Saving earth one tweet at a time through the lens of artificial intelligence. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–9.
  38. Structured neural topic models for reviews. In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, volume 89 of Proceedings of Machine Learning Research, pages 3429–3439. PMLR.
  39. Hierarchical topic models and the nested chinese restaurant process. Advances in neural information processing systems, 16.
  40. Maarten Grootendorst. 2022. Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
  41. Neural topic model with reinforcement learning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3478–3483.
  42. Multi task mutual learning for joint sentiment classification and topic detection. IEEE Transactions on Knowledge and Data Engineering, 34(4):1915–1927.
  43. Texttovec: Deep contextualized neural autoregressive topic models of language with distributed compositional prior. In International Conference on Learning Representations.
  44. Document informed neural autoregressive topic models with distributional prior. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6505–6512.
  45. Neural topic modeling with continual lifelong learning. In International Conference on Machine Learning, pages 3907–3917. PMLR.
  46. Unified neural topic model via contrastive learning and term weighting. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1802–1817, Dubrovnik, Croatia. Association for Computational Linguistics.
  47. Kernel topic models. In Artificial Intelligence and Statistics, pages 511–519.
  48. Geoffrey E Hinton and Russ R Salakhutdinov. 2009. Replicated softmax: an undirected topic model. Advances in neural information processing systems, 22.
  49. Is automated topic model evaluation broken? the incoherence of coherence. In Advances in Neural Information Processing Systems.
  50. Improving neural topic models using knowledge distillation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
  51. Are neural topic models broken? In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5321–5344.
  52. Neural topic modeling with cycle-consistent adversarial training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9018–9030.
  53. Tree-structured neural topic model. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 800–806.
  54. Dirichlet variational autoencoder. Pattern Recognition, 107:107514.
  55. Simultaneous discovery of common and discriminative topics via joint nonnegative matrix factorization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 567–576.
  56. Diederik P Kingma and Max Welling. 2014. Auto-encoding variational bayes. In The International Conference on Learning Representations (ICLR).
  57. Discriminative topic modeling with logistic lda. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.
  58. Katsiaryna Krasnashchok and Salim Jouili. 2018. Improving Topic Quality by Promoting Named Entities in Topic Modeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, volume 2, pages 247–253.
  59. Hugo Larochelle and Stanislas Lauly. 2012. A neural autoregressive topic model. Advances in Neural Information Processing Systems, 25.
  60. Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 530–539.
  61. A systematic review of the use of topic models for short text social media analysis. Artificial Intelligence Review, pages 1–33.
  62. Daniel Lee and H Sebastian Seung. 2000. Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 13.
  63. Alleviating" posterior collapse”in deep topic models via policy gradient. Advances in Neural Information Processing Systems, 35:22562–22575.
  64. Global surveillance of covid-19 by mining news media using a multi-source dynamic embedded topic model. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 1–14.
  65. Copula guided neural topic modelling for short texts. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1773–1776.
  66. Sparsemax and relaxed wasserstein for topic sparsity. In Proceedings of the twelfth ACM international conference on web search and data mining, pages 141–149.
  67. Tomodapi: a topic modeling api to train, use and compare topic models. In Proceedings of second workshop for NLP open source software (NLP-OSS), pages 132–140.
  68. Neural variational correlated topic modeling. In The World Wide Web Conference, pages 1142–1152.
  69. Plda+ parallel latent dirichlet allocation with data placement and pipeline processing. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):1–18.
  70. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.
  71. Jon Mcauliffe and David Blei. 2007. Supervised topic models. Advances in neural information processing systems, 20:121–128.
  72. Andrew Kachites McCallum. 2002. Mallet: A machine learning for languagetoolkit. UMass.
  73. Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843.
  74. Discovering discrete latent topics with neural variational inference. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 2410–2419. JMLR. org.
  75. Neural variational inference for text processing. In International conference on machine learning, pages 1727–1736.
  76. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings.
  77. Polylingual topic models. In Proceedings of the 2009 conference on empirical methods in natural language processing, pages 880–889, Singapore. Association for Computational Linguistics.
  78. Optimizing semantic coherence in topic models. In Proceedings of the conference on empirical methods in natural language processing, pages 262–272. Association for Computational Linguistics.
  79. Dynamic structured neural topic model with self-attention mechanism. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5916–5930.
  80. Aaron Mueller and Mark Dredze. 2021. Fine-tuning encoders for improved monolingual and zero-shot polylingual neural topic modeling. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3054–3068.
  81. Topic modeling with Wasserstein autoencoders. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6345–6381, Florence, Italy. Association for Computational Linguistics.
  82. Distributed algorithms for topic models. Journal of Machine Learning Research, 10(8).
  83. Automatic evaluation of topic coherence. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 100–108. Association for Computational Linguistics.
  84. Thong Nguyen and Anh Tuan Luu. 2021. Contrastive learning for neural topic model. Advances in Neural Information Processing Systems, 34.
  85. Enriching and controlling global semantics for text summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9443–9456.
  86. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
  87. Dang Pham and Tuan MV Le. 2021. Neural topic models for hierarchical topic detection and visualization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part III 21, pages 35–51. Springer.
  88. Short text topic modeling techniques, applications, and performance: a survey. IEEE Transactions on Knowledge and Data Engineering, 34(3):1427–1445.
  89. Antm: An aligned neural topic model for exploring evolving topics. arXiv preprint arXiv:2302.01501.
  90. Radim Rehurek and Petr Sojka. 2011. Gensim – statistical semantics in python. Retrieved from genism. org.
  91. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992.
  92. Mehdi Rezaee and Francis Ferraro. 2020. A discrete variational recurrent topic model without the reparametrization trick. Advances in neural information processing systems, 33:13831–13843.
  93. Stochastic backpropagation and approximate inference in deep generative models. In Proceedings ofthe 31th International Conference on Machine Learning.
  94. Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining, pages 399–408. ACM.
  95. Detecting common discussion topics across culture from news reader comments. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 676–685.
  96. Short-text topic modeling via non-negative matrix factorization enriched with local word-context correlations. In Proceedings of the 2018 World Wide Web Conference, pages 1105–1114. International World Wide Web Conferences Steering Committee.
  97. Tired of topic models? clusters of pretrained word embeddings make for fast and good topics too! In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1728–1736, Online. Association for Computational Linguistics.
  98. Carson Sievert and Kenneth Shirley. 2014. Ldavis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces, pages 63–70.
  99. Classification aware neural topic model for covid-19 disinformation categorisation. PloS one, 16(2):e0247086.
  100. Akash Srivastava and Charles Sutton. 2017. Autoencoding variational inference for topic models. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.
  101. Re-visiting automated topic model evaluation with large language models. arXiv preprint arXiv:2305.12152.
  102. Mark Steyvers and Tom Griffiths. 2007. Probabilistic topic models. Handbook of latent semantic analysis, 427(7):424–440.
  103. A topic augmented text generation model: Joint learning of semantics and structural features. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5090–5099.
  104. Sharing clusters among related groups: Hierarchical dirichlet processes. Advances in neural information processing systems, 17.
  105. OCTIS: Comparing and optimizing topic models is simple! In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 263–270. Association for Computational Linguistics.
  106. Laure Thompson and David Mimno. 2020. Topic modeling with contextualized word representation clusters. arXiv preprint arXiv:2010.12626.
  107. Learning vae-lda models with rounded reparameterization trick. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1315–1325.
  108. Topic modeling on podcast short-text metadata. In 44th European Conference on Information Retrieval (ECIR).
  109. Representation learning with contrastive predictive coding. arXiv e-prints, pages arXiv–1807.
  110. Aaron Van den Oord and Oriol Vinyals. 2017. Neural discrete representation learning. In Advances in Neural Information Processing Systems, pages 6306–6315.
  111. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, 9(Nov):2579–2605.
  112. Attention is all you need. Advances in neural information processing systems, 30.
  113. Evaluation methods for topic models. In Proceedings of the 26th annual international conference on machine learning, pages 1105–1112.
  114. Continuous time dynamic topic models. In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 579–586.
  115. Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 448–456.
  116. Representing mixtures of word embeddings with mixtures of topic embeddings. In International Conference on Learning Representations.
  117. Topic model on microblog with dual-streams graph convolution networks. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
  118. Neural topic modeling with bidirectional adversarial training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 340–350, Online. Association for Computational Linguistics.
  119. Atm: Adversarial-neural topic model. Information Processing & Management, 56(6):102098.
  120. Neural topic model with attention for supervised learning. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 1147–1156. PMLR.
  121. Plda: Parallel latent dirichlet allocation for large-scale applications. In Algorithmic Aspects in Information and Management: 5th International Conference, AAIM 2009, San Francisco, CA, USA, June 15-17, 2009. Proceedings 5, pages 301–314. Springer.
  122. Layer-assisted neural topic modeling over document networks. In IJCAI, pages 3148–3154.
  123. Extracting topics with simultaneous word co-occurrence and semantic correlation graphs: Neural topic modeling for short texts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 18–27.
  124. Infoctm: A mutual information maximization perspective of cross-lingual topic modeling. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 13763–13771.
  125. Effective neural topic modeling with embedding clustering regularization. In International Conference on Machine Learning. PMLR.
  126. Xiaobao Wu and Chunping Li. 2019. Short Text Topic Modeling with Flexible Word Patterns. In International Joint Conference on Neural Networks.
  127. Discovering topics in long-tailed corpora with causal intervention. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 175–185, Online. Association for Computational Linguistics.
  128. Learning Multilingual Topics with Neural Variational Inference. In International Conference on Natural Language Processing and Chinese Computing.
  129. Short text topic modeling with topic distribution quantization and negative sampling decoder. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1772–1782, Online.
  130. Mitigating data sparsity for short text topic modeling by topic-semantic contrastive learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2748–2760, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  131. Towards the topmost: A topic modeling system toolkit. arXiv preprint arXiv:2309.06908.
  132. On the affinity, rationality, and diversity of hierarchical topic modeling. In Proceedings of the AAAI Conference on Artificial Intelligence.
  133. Graph neural collaborative topic model for citation recommendation. ACM Transactions on Information Systems (TOIS), 40(3):1–30.
  134. Hyperminer: Topic taxonomy mining with hyperbolic embedding. In Advances in Neural Information Processing Systems, volume 35, pages 31557–31570. Curran Associates, Inc.
  135. A biterm topic model for short texts. In Proceedings of the 22nd international conference on World Wide Web, pages 1445–1456. ACM.
  136. Graph attention topic modeling network. In Proceedings of The Web Conference 2020, pages 144–154.
  137. Topnet: Learning from neural topic model to generate long stories. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1997–2005.
  138. Jianhua Yin and Jianyong Wang. 2014. A dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 233–242. ACM.
  139. Multilingual anchoring: Interactive topic modeling and alignment across languages. Advances in neural information processing systems, 31.
  140. What you say and how you say it: Joint modeling of topics and discourse in microblog conversations. Transactions of the Association for Computational Linguistics, 7:267–281.
  141. Topic memory networks for short text classification. In Proceedings of the conference on empirical methods in natural language process.
  142. Delvin Ce Zhang and Hady Lauw. 2022. Dynamic topic models for temporal document networks. In International Conference on Machine Learning, pages 26281–26292. PMLR.
  143. WHAI: Weibull hybrid autoencoding inference for deep topic modeling. In International Conference on Learning Representations.
  144. Lifelong topic modeling with knowledge-enhanced adversarial network. World Wide Web, 25(1):219–238.
  145. Htkg: Deep keyphrase generation with neural hierarchical topic guidance. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1044–1054.
  146. Is neural topic modelling better than clustering? an empirical study on clustering with contextual embeddings for topics. In NAACL 2022-2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, page 3886.
  147. Topic modelling meets deep neural networks: A survey. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 4713–4720. International Joint Conferences on Artificial Intelligence Organization. Survey Track.
  148. Neural topic model via optimal transport. In International Conference on Learning Representations.
  149. Neural topic model via optimal transport. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net.
  150. A neural topic model with word vectors and entity vectors for short texts. Information Processing & Management, 58(2):102455.
  151. Neural topic modeling by incorporating document relationship graph. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3790–3796.
  152. Improving topic disentanglement via contrastive learning. Information Processing & Management, 60(2):103164.
  153. Graph neural topic model with commonsense knowledge. Information Processing & Management, 60(2):103215.
  154. Graphbtm: Graph enhanced autoencoded variational inference for biterm topic model. In Conference on Empirical Methods in Natural Language Processing (EMNLP 2018).
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Authors (3)
  1. Xiaobao Wu (43 papers)
  2. Thong Nguyen (38 papers)
  3. Anh Tuan Luu (69 papers)
Citations (16)
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