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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>In a document, the topic distribution of a sentence depends on both the topics of its neighbored sentences and its own content, and it is usually affected by the topics of the neighbored sentences with different weights. The neighbored sentences of a sentence include the preceding sentences and the subsequent sentences. Meanwhile, it is natural that a document can be treated as a sequence of sentences. Most existing works for Bayesian document modeling do not take these points into consideration. To fill this gap, we propose a bi-Directional Recurrent Attentional Topic Model (bi-RATM) for document embedding. The bi-RATM not only takes advantage of the sequential orders among sentences but also uses the attention mechanism to model the relations among successive sentences. To support to the bi-RATM, we propose a bi-Directional Recurrent Attentional Bayesian Process (bi-RABP) to handle the sequences. Based on the bi-RABP, bi-RATM fully utilizes the bi-directional sequential information of the sentences in a document. Online bi-RATM is proposed to handle large-scale corpus. Experiments on two corpora show that the proposed model outperforms state-of-the-art methods on document modeling and classification.<\/jats:p>","DOI":"10.1145\/3412371","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T04:10:30Z","timestamp":1601352630000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Bi-Directional Recurrent Attentional Topic Model"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6404-3438","authenticated-orcid":false,"given":"Shuangyin","family":"Li","sequence":"first","affiliation":[{"name":"South China Normal University, Guangdong, China"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Guangdong, China"}]},{"given":"Rong","family":"Pan","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangdong, China"}]}],"member":"320","published-online":{"date-parts":[[2020,9,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Xing","author":"Ahmed Amr","year":"2008","unstructured":"Amr Ahmed and Eric P . 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In Proceedings of the Advances in Neural Information Processing Systems . 185--192. Jordan L. Boyd-Graber and David M. Blei. 2009. Syntactic topic models. In Proceedings of the Advances in Neural Information Processing Systems. 185--192."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/176313.176316"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458202"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2009.00698.x"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2018.2877359"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2016.2603511"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2904687"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the International Conference on Machine Learning. 703--711","author":"Chen Zhiyuan","year":"2014","unstructured":"Zhiyuan Chen and Bing Liu . 2014 . 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In Proceedings of the Advances in Neural Information Processing Systems . 121--128. Jon D. Mcauliffe and David M. Blei. 2008. Supervised topic models. In Proceedings of the Advances in Neural Information Processing Systems. 121--128."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 3111--3119","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Ilya Sutskever , Kai Chen , Greg S. Corrado , and Jeff Dean . 2013 . Distributed representations of words and phrases and their compositionality . In Proceedings of the Advances in Neural Information Processing Systems. 3111--3119 . Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. 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In Proceedings of the Advances in Neural Information Processing Systems. 1081--1088."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00140"},{"key":"e_1_2_1_52_1","volume-title":"Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI\u201913)","author":"Nitish Srivastava","unstructured":"Srivastava Nitish , Ruslan Salakhutdinov , and Geoffrey E. Hinton . 2013. Modeling documents with a deep boltzmann machine . In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI\u201913) . AUAI Press, Arlington, Virginia, 616--624. Srivastava Nitish, Ruslan Salakhutdinov, and Geoffrey E. Hinton. 2013. Modeling documents with a deep boltzmann machine. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI\u201913). 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In Proceedings of the International Conference on Machine Learning Workshop. Vol. 79."},{"key":"e_1_2_1_62_1","volume-title":"Salakhutdinov","author":"Srivastava Nitish","year":"2012","unstructured":"Nitish Srivastava and Russ R . Salakhutdinov . 2012 . Multimodal learning with deep boltzmann machines. In Proceedings of the Advances in Neural Information Processing Systems . 2222--2230. Nitish Srivastava and Russ R. Salakhutdinov. 2012. Multimodal learning with deep boltzmann machines. In Proceedings of the Advances in Neural Information Processing Systems. 2222--2230."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.5555\/2390948.2391052"},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the 28th International Conference on Machine Learning. 1017--1024","author":"Sutskever Ilya","unstructured":"Ilya Sutskever , James Martens , and Geoffrey E. Hinton . 2011. Generating text with recurrent neural networks . In Proceedings of the 28th International Conference on Machine Learning. 1017--1024 . Ilya Sutskever, James Martens, and Geoffrey E. Hinton. 2011. Generating text with recurrent neural networks. In Proceedings of the 28th International Conference on Machine Learning. 1017--1024."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1167"},{"key":"e_1_2_1_66_1","volume-title":"Proceedings of the International Conference on Machine Learning. 190--198","author":"Tang Jian","year":"2014","unstructured":"Jian Tang , Zhaoshi Meng , Xuanlong Nguyen , Qiaozhu Mei , and Ming Zhang . 2014 . Understanding the limiting factors of topic modeling via posterior contraction analysis . In Proceedings of the International Conference on Machine Learning. 190--198 . Jian Tang, Zhaoshi Meng, Xuanlong Nguyen, Qiaozhu Mei, and Ming Zhang. 2014. Understanding the limiting factors of topic modeling via posterior contraction analysis. In Proceedings of the International Conference on Machine Learning. 190--198."},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000302"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000001"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553515"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_2_1_71_1","volume-title":"Cavalcante","author":"Wehrmann J\u00f4natas","year":"2018","unstructured":"J\u00f4natas Wehrmann , Gabriel S. Sim\u00f5es , Rodrigo C. Barros , and Victor F . Cavalcante . 2018 . Adult content detection in videos with convolutional and recurrent neural networks. Neurocomputing 272 (January 2018), 432--438. J\u00f4natas Wehrmann, Gabriel S. Sim\u00f5es, Rodrigo C. Barros, and Victor F. Cavalcante. 2018. Adult content detection in videos with convolutional and recurrent neural networks. Neurocomputing 272 (January 2018), 432--438."},{"key":"e_1_2_1_72_1","volume-title":"Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 178--185","author":"Wei Xing","unstructured":"Xing Wei and W. Bruce Croft . 2006. LDA-based document models for ad-hoc retrieval . In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 178--185 . Xing Wei and W. Bruce Croft. 2006. LDA-based document models for ad-hoc retrieval. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 178--185."},{"key":"e_1_2_1_73_1","volume-title":"Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries. Information Sciences 405 (September","author":"Xiao Guoqing","year":"2017","unstructured":"Guoqing Xiao , Kenli Li , and Keqin Li. 2017. Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries. Information Sciences 405 (September 2017 ), 207--226. Guoqing Xiao, Kenli Li, and Keqin Li. 2017. Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries. Information Sciences 405 (September 2017), 207--226."},{"key":"e_1_2_1_74_1","volume-title":"Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC\u201917)","author":"Xu Mingyang","unstructured":"Mingyang Xu , Ruixin Yang , Steve Harenberg , and Nagiza F. Samatova . 2017. A lifelong learning topic model structured using latent embeddings . In Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC\u201917) . IEEE, 260--261. Mingyang Xu, Ruixin Yang, Steve Harenberg, and Nagiza F. Samatova. 2017. A lifelong learning topic model structured using latent embeddings. In Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC\u201917). IEEE, 260--261."},{"key":"e_1_2_1_75_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2353--2359","author":"Yang Min","year":"2015","unstructured":"Min Yang , Tianyi Cui , and Wenting Tu . 2015 . Ordering-sensitive and semantic-aware topic modeling . In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2353--2359 . Min Yang, Tianyi Cui, and Wenting Tu. 2015. Ordering-sensitive and semantic-aware topic modeling. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2353--2359."},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of the 21st International Conference on World Wide Web. ACM, 879--888","author":"Zhai Ke","unstructured":"Ke Zhai , Jordan Boyd-Graber , Nima Asadi , and Mohamad L. Alkhouja . 2012. Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce . In Proceedings of the 21st International Conference on World Wide Web. ACM, 879--888 . Ke Zhai, Jordan Boyd-Graber, Nima Asadi, and Mohamad L. Alkhouja. 2012. Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce. In Proceedings of the 21st International Conference on World Wide Web. 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