{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:32:42Z","timestamp":1757626362378,"version":"3.44.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030865221"},{"type":"electronic","value":"9783030865238"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86523-8_46","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T06:05:16Z","timestamp":1631253916000},"page":"762-778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi-supervised Semantic Visualization for Networked Documents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5571-9766","authenticated-orcid":false,"given":"Delvin Ce","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8245-8677","authenticated-orcid":false,"given":"Hady W.","family":"Lauw","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"46_CR1","doi-asserted-by":"crossref","unstructured":"Bai, H., Chen, Z., Lyu, M.R., King, I., Xu, Z.: Neural relational topic models for scientific article analysis. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 27\u201336 (2018)","DOI":"10.1145\/3269206.3271696"},{"key":"46_CR2","unstructured":"Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, Heidelberg (2006). ISBN 978-0-387-31073-2"},{"key":"46_CR3","unstructured":"Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of GSCL, pp. 31\u201340 (2009)"},{"key":"46_CR4","unstructured":"Chang J., Blei D.: Relational topic models for document networks. In: Artificial Intelligence and Statistics, pp. 81\u201388 (2009)"},{"issue":"12","key":"46_CR5","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TVCG.2013.212","volume":"19","author":"J Choo","year":"2013","unstructured":"Choo, J., Lee, C., Reddy, C.K., Park, H.: Utopian: user-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Visual Comput. Graph. 19(12), 1992\u20132001 (2013)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"key":"46_CR6","unstructured":"Evert, S.: Google web 1t 5-grams made easy (but not for the computer). In: Proceedings of the NAACL HLT 2010 Sixth Web as Corpus Workshop, pp. 32\u201340 (2010)"},{"key":"46_CR7","unstructured":"Blei, D.M., Griffiths, T.L., Jordan, M.I., Tenenbaum, J.B.: Hierarchical topic models and the nested Chinese restaurant process. In: Advances in Neural Information Processing Systems, pp. 17\u201324 (2004)"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Iwata, T., Yamada, T., Ueda, N.: Probabilistic latent semantic visualization: topic model for visualizing documents. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 363\u2013371 (2008)","DOI":"10.1145\/1401890.1401937"},{"key":"46_CR9","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-Softmax. In: Proceedings of International Conference on Learning Representations (2017)"},{"key":"46_CR10","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"46_CR11","unstructured":"Kingma, D.P., Rezende, D.J., Mohamed, S., Welling, M.: Semi-supervised learning with deep generative models. In: Proceedings of Advances in Neural Information Processing Systems, pp. 3581\u20133589 (2014)"},{"key":"46_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"46_CR13","unstructured":"Lacoste-Julien, S., Sha, F., Jordan, M.I.: DiscLDA: discriminative learning for dimensionality reduction and classification. In: Proceedings of Advances in Neural Information Processing Systems, pp. 897\u2013904. (2009)"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Le, T., Akoglu, L.: ContraVis: contrastive and visual topic modeling for comparing document collections. In: Proceedings of The World Wide Web Conference, pp. 928\u2013938 (2019)","DOI":"10.1145\/3308558.3313617"},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Le, T.M., Lauw, H.W.: Probabilistic latent document network embedding. In: 2014 IEEE International Conference on Data Mining, pp. 270\u2013279 (2014)","DOI":"10.1109\/ICDM.2014.119"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Lee, H., Kihm, J., Choo, J., Stasko, J., Park, H.: iVisClustering: an interactive visual document clustering via topic modeling. In: Computer graphics forum, vol. 31, No. 3pt3, pp. 1155\u20131164. Oxford, UK: Blackwell Publishing Ltd. (2012)","DOI":"10.1111\/j.1467-8659.2012.03108.x"},{"key":"46_CR17","unstructured":"Leow, Y.Y., Laurent, T., Bresson, X.: GraphTSNE: a visualization technique for graph-structured data. arXiv preprint arXiv:1904.06915 (2019)"},{"key":"46_CR18","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"46_CR19","unstructured":"Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. In: Proceedings of International Conference on Learning Representations (2017)"},{"key":"46_CR20","unstructured":"Blei, D.M., McAuliffe, J.D.: Supervised topic models. In: Proceedings of Advances in Neural Information Processing Systems, pp. 121\u2013128 (2008)"},{"issue":"2","key":"46_CR21","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1023\/A:1009953814988","volume":"3","author":"AK McCallum","year":"2000","unstructured":"McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3(2), 127\u2013163 (2000)","journal-title":"Inf. Retrieval"},{"key":"46_CR22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"46_CR23","unstructured":"Morin, F., Bengio, Y.: Hierarchical probabilistic neural network language model. In: International Workshop on Artificial Intelligence and Statistics, pp. 246\u2013252 (2005)"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 248\u2013256 (2009)","DOI":"10.3115\/1699510.1699543"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Ramage, D., Manning, C.D., Dumais, S.: Partially labeled topic models for interpretable text mining. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 457\u2013465 (2011)","DOI":"10.1145\/2020408.2020481"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Rozemberczki, B., Davies, R., Sarkar, R., Sutton, C.: GEMSEC: graph embedding with self clustering. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 65\u201372 (2019)","DOI":"10.1145\/3341161.3342890"},{"key":"46_CR27","unstructured":"Srivastava, A., Sutton, C.: Autoencoding variational inference for topic models. arXiv preprint arXiv:1703.01488 (2017)"},{"key":"46_CR28","doi-asserted-by":"crossref","unstructured":"Tang, J., Liu, J., Zhang, M., Mei, Q.: Visualizing large-scale and high-dimensional data. In: Proceedings of the 25th International Conference on World Wide Web, pp. 287\u2013297 (2016)","DOI":"10.1145\/2872427.2883041"},{"key":"46_CR29","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of International Conference on Learning Representations (2018)"},{"key":"46_CR30","unstructured":"Wu, M., Goodman, N.: Multimodal generative models for scalable weakly-supervised learning. In: Proceedings of Advances in Neural Information Processing Systems, pp. 5575\u20135585 (2018)"},{"key":"46_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lauw, H.W.: Topic modeling on document networks with adjacent-encoder. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(04), pp. 6737\u20136745 (2020)","DOI":"10.1609\/aaai.v34i04.6152"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86523-8_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:04:35Z","timestamp":1757455475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86523-8_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865221","9783030865238"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86523-8_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}