{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:06:05Z","timestamp":1743116765427,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":45,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819724208"},{"type":"electronic","value":"9789819724215"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-2421-5_11","type":"book-chapter","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:01:48Z","timestamp":1715414508000},"page":"155-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lifelong Hierarchical Topic Modeling via\u00a0Non-negative Matrix Factorization"],"prefix":"10.1007","author":[{"given":"Zhicheng","family":"Lin","sequence":"first","affiliation":[]},{"given":"Jiaxing","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Zhiqi","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Yanghui","family":"Rao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,12]]},"reference":[{"key":"11_CR1","unstructured":"Ahmed, A., Hong, L., Smola, A.: Nested Chinese restaurant franchise process: applications to user tracking and document modeling. In: ICML, pp. 1426\u20131434 (2013)"},{"key":"11_CR2","unstructured":"Alvarez-Melis, D., Jaakkola, T.S.: Tree-structured decoding with doubly-recurrent neural networks. In: ICLR (2017)"},{"issue":"1","key":"11_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1020281327116","volume":"50","author":"C Andrieu","year":"2003","unstructured":"Andrieu, C., De Freitas, N., Doucet, A., Jordan, M.I.: An introduction to MCMC for machine learning. Mach. Learn. 50(1), 5\u201343 (2003)","journal-title":"Mach. Learn."},{"key":"11_CR4","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Card, D., Tan, C., Smith, N.A.: Neural models for documents with metadata. In: ACL, pp. 2031\u20132040 (2018)","DOI":"10.18653\/v1\/P18-1189"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Carlson, A., Betteridge, J., Wang, R.C., Hruschka\u00a0Jr, E.R., Mitchell, T.M.: Coupled semi-supervised learning for information extraction. In: WSDM, pp. 101\u2013110 (2010)","DOI":"10.1145\/1718487.1718501"},{"key":"11_CR7","unstructured":"Chen, X.: Learning with sparsity: Structures, optimization and applications. Ph.D. thesis, Carnegie Mellon University (2013)"},{"issue":"7","key":"11_CR8","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1109\/TKDE.2019.2904687","volume":"32","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Wu, J., Lin, J., Liu, R., Zhang, H., Ye, Z.: Affinity regularized non-negative matrix factorization for lifelong topic modeling. IEEE Trans. Knowl. Data Eng. 32(7), 1249\u20131262 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, H., Wu, J., Wang, X., Liu, R., Lin, M.: Modeling emerging, evolving and fading topics using dynamic soft orthogonal NMF with sparse representation. In: ICDM, pp. 61\u201370 (2015)","DOI":"10.1109\/ICDM.2015.96"},{"key":"11_CR10","unstructured":"Chen, Z., Liu, B.: Topic modeling using topics from many domains, lifelong learning and big data. In: ICML. vol.\u00a032, pp. 703\u2013711 (2014)"},{"key":"11_CR11","unstructured":"Chen, Z., Ma, N., Liu, B.: Lifelong learning for sentiment classification. arXiv preprint arXiv:1801.02808 (2018)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ding, C., Zhang, Z., Rao, Y., Xie, H.: Tree-structured topic modeling with nonparametric neural variational inference. In: ACL\/IJCNLP, pp. 2343\u20132353 (2021)","DOI":"10.18653\/v1\/2021.acl-long.182"},{"issue":"6","key":"11_CR13","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1007\/s10618-014-0384-8","volume":"29","author":"J Choo","year":"2015","unstructured":"Choo, J., Lee, C., Reddy, C.K., Park, H.: Weakly supervised nonnegative matrix factorization for user-driven clustering. Data Min. Knowl. Discov. 29(6), 1598\u20131621 (2015)","journal-title":"Data Min. Knowl. Discov."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Dai, L., Zhu, R., Wang, J.: Joint nonnegative matrix factorization based on sparse and graph laplacian regularization for clustering and co-differential expression genes analysis. Complex. 2020, 3917812:1\u20133917812:10 (2020)","DOI":"10.1155\/2020\/3917812"},{"key":"11_CR15","unstructured":"Duan, Z., et al.: Sawtooth factorial topic embeddings guided gamma belief network. In: ICML, pp. 2903\u20132913 (2021)"},{"key":"11_CR16","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: AISTATS, vol.\u00a015, pp. 315\u2013323 (2011)"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Greene, D., O\u2019Callaghan, D., Cunningham, P.: How many topics? stability analysis for topic models. In: ECML\/PKDD, vol.\u00a08724, pp. 498\u2013513 (2014)","DOI":"10.1007\/978-3-662-44848-9_32"},{"key":"11_CR18","unstructured":"Griffiths, T., Jordan, M., Tenenbaum, J., Blei, D.: Hierarchical topic models and the nested Chinese restaurant process. In: NIPS, vol.\u00a016, pp. 17\u201324 (2003)"},{"key":"11_CR19","unstructured":"Gupta, P., Chaudhary, Y., Runkler, T.A., Sch\u00fctze, H.: Neural topic modeling with continual lifelong learning. In: ICML, vol.\u00a0119, pp. 3907\u20133917 (2020)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Isonuma, M., Mori, J., Bollegala, D., Sakata, I.: Tree-structured neural topic model. In: ACL, pp. 800\u2013806 (2020)","DOI":"10.18653\/v1\/2020.acl-main.73"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Kim, D., Kim, S., Oh, A.: Modeling topic hierarchies with the recursive Chinese restaurant process. In: CIKM, pp. 783\u2013792 (2012)","DOI":"10.1145\/2396761.2396861"},{"key":"11_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"11_CR23","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)"},{"key":"11_CR24","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. CoRR abs\/1612.00796 (2016)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Lau, J.H., Newman, D., Baldwin, T.: Machine reading tea leaves: automatically evaluating topic coherence and topic model quality. In: EACL, pp. 530\u2013539 (2014)","DOI":"10.3115\/v1\/E14-1056"},{"key":"11_CR26","unstructured":"Lee, D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS. vol.\u00a013, pp. 556\u2013562 (2000)"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Lin, T., Hu, Z., Guo, X.: Sparsemax and relaxed wasserstein for topic sparsity. In: WSDM, pp. 141\u2013149 (2019)","DOI":"10.1145\/3289600.3290957"},{"issue":"4","key":"11_CR28","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11518-018-5375-7","volume":"27","author":"R Liu","year":"2018","unstructured":"Liu, R., Wang, X., Wang, D., Zuo, Y., Zhang, H., Zheng, X.: Topic splitting: a hierarchical topic model based on non-negative matrix factorization. J. Syst. Sci. Syst. Eng. 27(4), 479\u2013496 (2018)","journal-title":"J. Syst. Sci. Syst. Eng."},{"key":"11_CR29","unstructured":"Miao, Y., Grefenstette, E., Blunsom, P.: Discovering discrete latent topics with neural variational inference. In: ICML, pp. 2410\u20132419 (2017)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Mimno, D., Li, W., McCallum, A.: Mixtures of hierarchical topics with pachinko allocation. In: ICML, pp. 633\u2013640 (2007)","DOI":"10.1145\/1273496.1273576"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Ming, Z.Y., Wang, K., Chua, T.S.: Prototype hierarchy based clustering for the categorization and navigation of web collections. In: SIGIR, pp.\u00a02\u20139 (2010)","DOI":"10.1145\/1835449.1835453"},{"issue":"5","key":"11_CR32","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/3191513","volume":"61","author":"T Mitchell","year":"2018","unstructured":"Mitchell, T., et al.: Never-ending learning. Commun. ACM 61(5), 103\u2013115 (2018)","journal-title":"Commun. ACM"},{"issue":"2","key":"11_CR33","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TPAMI.2014.2318728","volume":"37","author":"JW Paisley","year":"2015","unstructured":"Paisley, J.W., Wang, C., Blei, D.M., Jordan, M.I.: Nested hierarchical dirichlet processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 256\u2013270 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Qin, X., Lu, Y., Chen, Y., Rao, Y.: Lifelong learning of topics and domain-specific word embeddings. In: ACL\/IJCNLP (Findings), pp. 2294\u20132309 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.202"},{"issue":"45","key":"11_CR36","doi-asserted-by":"publisher","first-page":"12679","DOI":"10.1073\/pnas.1525793113","volume":"113","author":"K Rohe","year":"2016","unstructured":"Rohe, K., Qin, T., Yu, B.: Co-clustering directed graphs to discover asymmetries and directional communities. PNAS 113(45), 12679\u201312684 (2016)","journal-title":"PNAS"},{"key":"11_CR37","unstructured":"Sethuraman, J.: A constructive definition of dirichlet priors. Statistica Sinica 639\u2013650 (1994)"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Silver, D.L.: Machine lifelong learning: challenges and benefits for artificial general intelligence. In: AGI, vol.\u00a06830, pp. 370\u2013375 (2011)","DOI":"10.1007\/978-3-642-22887-2_45"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Tan, C., Card, D., Smith, N.A.: Friendships, rivalries, and trysts: characterizing relations between ideas in texts. In: ACL, pp. 773\u2013783 (2017)","DOI":"10.18653\/v1\/P17-1072"},{"key":"11_CR40","unstructured":"Teh, Y., Jordan, M., Beal, M., Blei, D.: Sharing clusters among related groups: hierarchical dirichlet processes. In: NIPS, vol. 17, pp. 1385\u20131392 (2004)"},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Viegas, F., et al.: Cluwords: exploiting semantic word clustering representation for enhanced topic modeling. In: WSDM, pp. 753\u2013761 (2019)","DOI":"10.1145\/3289600.3291032"},{"key":"11_CR42","doi-asserted-by":"crossref","unstructured":"Viegas, F., Cunha, W., Gomes, C., Pereira, A., Rocha, L., Goncalves, M.: Cluhtm-semantic hierarchical topic modeling based on cluwords. In: ACL, pp. 8138\u20138150 (2020)","DOI":"10.18653\/v1\/2020.acl-main.724"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Neural mixed counting models for dispersed topic discovery. In: ACL, pp. 6159\u20136169 (2020)","DOI":"10.18653\/v1\/2020.acl-main.548"},{"issue":"7","key":"11_CR44","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1109\/TNNLS.2012.2197412","volume":"23","author":"Z Xu","year":"2012","unstructured":"Xu, Z., Chang, X., Xu, F., Zhang, H.: L$${}_{\\text{1\/2 }}$$ regularization: A thresholding representation theory and a fast solver. IEEE Trans. Neural Networks Learn. Syst. 23(7), 1013\u20131027 (2012)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"11_CR45","unstructured":"Zhao, H., Phung, D., Huynh, V., Le, T., Buntine, W.L.: Neural topic model via optimal transport. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2421-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:05:24Z","timestamp":1715414724000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2421-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819724208","9789819724215"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2421-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apweb-waim2023.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}