{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T17:26:55Z","timestamp":1757611615943,"version":"3.44.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863302"},{"type":"electronic","value":"9783030863319"}],"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\/978-3-030-86331-9_5","type":"book-chapter","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T02:05:57Z","timestamp":1630721157000},"page":"68-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Biased Coin Flip Process for Nonparametric Topic Modeling"],"prefix":"10.1007","author":[{"given":"Justin","family":"Wood","sequence":"first","affiliation":[]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Corey","family":"Arnold","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Xing, E.P.: Dynamic non-parametric mixture models and the recurrent Chinese restaurant process: with applications to evolutionary clustering. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2008, 24\u201326 April 2008, Atlanta, Georgia, USA, pp. 219\u2013230 (2008)","DOI":"10.1137\/1.9781611972788.20"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Azzalini, A., Bowman, A.W.: A look at some data on the old faithful geyser. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 39(3), 357\u2013365 (1990)","DOI":"10.2307\/2347385"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Bacallado, S., Favaro, S., Power, S., Trippa, L.: Perfect sampling of the posterior in the hierarchical pitman-YOR process. Bayesian Anal. 1(1), 1\u201325 (2021)","DOI":"10.1214\/21-BA1269"},{"key":"5_CR4","unstructured":"Blei, D.M., et al.: Hierarchical topic models and the nested Chinese restaurant process. In: Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, 8\u201313 December 2003, Vancouver and Whistler, British Columbia, Canada], pp. 17\u201324 (2003)"},{"key":"5_CR5","unstructured":"Blei, D.M., et al.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993\u20131022 (2003)"},{"issue":"2","key":"5_CR6","first-page":"863","volume":"49","author":"F Camerlenghi","year":"2021","unstructured":"Camerlenghi, F., Lijoi, A., Pr\u00fcnster, I.: Survival analysis via hierarchically dependent mixture hazards. Ann. Stat. 49(2), 863\u2013884 (2021)","journal-title":"Ann. Stat."},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Christensen, R., Johnson, W.: Modelling accelerated failure time with a Dirichlet process. Biometrika 75(4), 693\u2013704 (1988)","DOI":"10.1093\/biomet\/75.4.693"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Diana, A., Matechou, E., Griffin, J., Johnston, A., et al.: A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK. Ann. Appl. Stat. 14(1), 473\u2013493 (2020)","DOI":"10.1214\/19-AOAS1315"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Escobar, M.D., West, M.: Bayesian density estimation and inference using mixtures. J. Am. Stat. Assoc. 90(430), 577\u2013588 (1995)","DOI":"10.1080\/01621459.1995.10476550"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Ferguson, T.S.: A Bayesian analysis of some nonparametric problems. Ann. Stat., 209\u2013230 (1973)","DOI":"10.1214\/aos\/1176342360"},{"key":"5_CR11","unstructured":"Finkel, J.R., Grenager, T., Manning, C.D.: The infinite tree. In: ACL 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, 23\u201330 June 2007, Prague, Czech Republic (2007)"},{"key":"5_CR12","unstructured":"Griffiths, T.L., Ghahramani, Z.: The Indian buffet process: an introduction and review. J. Mach. Learn. Res. 12, 1185\u20131224 (2011)"},{"key":"5_CR13","unstructured":"Heinrich, G.: Infinite LDA implementing the HDP with minimum code complexity (2011)"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Ishwaran, H., James, L.F.: Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior information. J. Comput. Graph. Stat. 11(3), 508\u2013532 (2002)","DOI":"10.1198\/106186002411"},{"key":"5_CR15","unstructured":"Ishwaran, H., James, L.F.: Generalized weighted Chinese restaurant processes for species sampling mixture models. Statistica Sinica, 1211\u20131235 (2003)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Izenman, A.J., Sommer, C.J.: Philatelic mixtures and multimodal densities. J. Am. Stat. Assoc. 83(404), 941\u2013953 (1988)","DOI":"10.1080\/01621459.1988.10478683"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Krueger, R., Rashidi, T.H., Vij, A.: A Dirichlet process mixture model of discrete choice: comparisons and a case study on preferences for shared automated vehicles. J. Choice Modelling 36, 100229 (2020)","DOI":"10.1016\/j.jocm.2020.100229"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Lehnert, L., Littman, M.L., Frank, M.J.: Reward-predictive representations generalize across tasks in reinforcement learning. PLoS Comput. Biol. 16(10), e1008317 (2020)","DOI":"10.1371\/journal.pcbi.1008317"},{"key":"5_CR19","unstructured":"Li, W., et al.: Nonparametric Bayes pachinko allocation. In: UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, 19\u201322 July 2007, pp. 243\u2013250 (2007)"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Lijoi, A., Pr\u00fcnster, I., Walker, S.G., et al.: Bayesian nonparametric estimators derived from conditional Gibbs structures. Ann. Appl. Probab. 18(4), 1519\u20131547 (2008)","DOI":"10.1214\/07-AAP495"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Masumura, R., Asami, T., Oba, T., Sakauchi, S.: Hierarchical latent words language models for automatic speech recognition. J. Inf. Process. 29, 360\u2013369 (2021)","DOI":"10.2197\/ipsjjip.29.360"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"McAuliffe, J.D., et al.: Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat. Comput. 16(1), 5\u201314 (2006)","DOI":"10.1007\/s11222-006-5196-2"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Muchene, L., Safari, W.: Two-stage topic modelling of scientific publications: a case study of University of Nairobi, Kenya. Plos One 16(1), e0243208 (2021)","DOI":"10.1371\/journal.pone.0243208"},{"key":"5_CR24","unstructured":"Newman, D., Asuncion, A.U., Smyth, P., Welling, M.: Distributed inference for latent Dirichlet allocation. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3\u20136 December 2007, pp. 1081\u20131088 (2007)"},{"key":"5_CR25","unstructured":"Paisley, J.: A simple proof of the stick-breaking construction of the Dirichlet process (2010)"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Paisley, J.W., Carin, L.: Hidden Markov models with stick-breaking priors. IEEE Trans. Signal Process. 57(10), 3905\u20133917 (2009)","DOI":"10.1109\/TSP.2009.2024987"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Papaspiliopoulos, O., Roberts, G.O.: Retrospective Markov chain monte Carlo methods for Dirichlet process hierarchical models. Biometrika 95(1), 169\u2013186 (2008)","DOI":"10.1093\/biomet\/asm086"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Porteous, I., Newman, D., Ihler, A.T., Asuncion, A.U., Smyth, P., Welling, M.: Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, 24\u201327 August 2008, pp. 569\u2013577 (2008)","DOI":"10.1145\/1401890.1401960"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Postman, M., Huchra, J.P., Geller, M.J.: Probes of large-scale structure in the corona borealis region. Astron. J. 92, 1238\u20131247 (1986)","DOI":"10.1086\/114257"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Ramage, D., Manning, C.D., Dumais, S.T.: Partially labeled topic models for interpretable text mining. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 21\u201324 August 2011, pp. 457\u2013465 (2011)","DOI":"10.1145\/2020408.2020481"},{"key":"5_CR31","unstructured":"Serviansky, H., et al.: Set2Graph: learning graphs from sets. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Shi, Y., Laud, P., Neuner, J.: A dependent Dirichlet process model for survival data with competing risks. Lifetime Data Anal., 1\u201321 (2020)","DOI":"10.1007\/s10985-020-09506-0"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Teh, Y.W.: A hierarchical Bayesian language model based on Pitman-YOR processes. In: ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17\u201321 July 2006 (2006)","DOI":"10.3115\/1220175.1220299"},{"key":"5_CR34","unstructured":"Teh, Y.W., G\u00f6r\u00fcr, D., Ghahramani, Z.: Stick-breaking construction for the Indian buffet process. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico, 21\u201324 March 2007, pp. 556\u2013563 (2007)"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101(476), 1566\u20131581 (2006)","DOI":"10.1198\/016214506000000302"},{"key":"5_CR36","unstructured":"Teh, Y.W., Kurihara, K., Welling, M.: Collapsed variational inference for HDP. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3\u20136 December 2007, pp. 1481\u20131488 (2007)"},{"key":"5_CR37","unstructured":"Thibaux, R., Jordan, M.I.: Hierarchical beta processes and the Indian buffet process. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico, 21\u201324 March 2007, pp. 564\u2013571 (2007)"},{"key":"5_CR38","unstructured":"Wallach, H.M.: Structured topic models for language. Ph.D. thesis, University of Cambridge Cambridge, UK (2008)"},{"key":"5_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-3-642-02158-9_26","volume-title":"Algorithmic Aspects in Information and Management","author":"Y Wang","year":"2009","unstructured":"Wang, Y., Bai, H., Stanton, M., Chen, W.-Y., Chang, E.Y.: PLDA: parallel latent Dirichlet allocation for large-scale applications. In: Goldberg, A.V., Zhou, Y. (eds.) AAIM 2009. LNCS, vol. 5564, pp. 301\u2013314. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02158-9_26"},{"key":"5_CR40","unstructured":"Williamson, S., Wang, C., Heller, K.A., Blei, D.M.: The IBP compound Dirichlet process and its application to focused topic modeling. In: ICML (2010)"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Wood, J., et al.: Source-LDA: enhancing probabilistic topic models using prior knowledge sources. In: 33rd IEEE International Conference on Data Engineering (2016)","DOI":"10.1109\/ICDE.2017.99"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86331-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T22:04:24Z","timestamp":1756937064000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86331-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863302","9783030863319"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86331-9_5","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":"2 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lausanne","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","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":"5 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iapr.org\/icdar2021","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":"340","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":"182","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":"54% - 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":"2.9","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":"4.9","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Additionally, 13 competition reports are included.","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)"}}]}}