{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:16:01Z","timestamp":1742991361558,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030551865"},{"type":"electronic","value":"9783030551872"}],"license":[{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-55187-2_44","type":"book-chapter","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T23:04:00Z","timestamp":1598310240000},"page":"611-617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clustering Approach to Topic Modeling in Users Dialogue"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6208-691X","authenticated-orcid":false,"given":"E.","family":"Feldina","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8992-9654","authenticated-orcid":false,"given":"O.","family":"Makhnytkina","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,25]]},"reference":[{"key":"44_CR1","unstructured":"Hiraoka, T., Tsuchida, M., Watanabe, Y.: Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings (2017)"},{"key":"44_CR2","unstructured":"Liu, H., Lin, T., Sun, H., Lin, W., Chang, C.-W., Zhong, T., Rudnicky, A.: RubyStar: A Non-Task-Oriented Mixture Model Dialog System (2017)"},{"key":"44_CR3","doi-asserted-by":"publisher","unstructured":"Koltsov, S., Pashakhin, S., Dokuka, S.: A full-cycle methodology for news topic modeling and user feedback research. In: Staab, S., Koltsova, O., Ignatov, D. (eds.) Social Informatics. SocInfo 2018. LNCS, vol. 11185, pp. 308\u2013321. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01129-1_19","DOI":"10.1007\/978-3-030-01129-1_19"},{"key":"44_CR4","unstructured":"Sanandres, E., Llanos, R., Madariaga, C.: Topic Modeling of Twitter Conversations (2018)"},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Shilkina, N., Maltseva, A., Makhnytkina, O., Titova, M., Gubernatorova, E., Katsko, I., Mirzabalaeva, F., Shusharina, S.: Social media as a display of students\u2019 communication culture: case of educational, professional and labor verbal markers analysis. In: Communications in Computer and Information Science, pp. 384\u2013397 (2019)","DOI":"10.1007\/978-3-030-13283-5_29"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Liu, L., Huang, H., Gao, Y., Zhang, Y., Wei, X.: Neural variational correlated topic modeling. In: The World Wide Web Conference, pp. 1142\u20131152 (2019)","DOI":"10.1145\/3308558.3313561"},{"key":"44_CR7","unstructured":"Ram, A., Prasad, R., Khatri, C., Venkatesh, A.: Conversational AI: the science behind the alexa prize (2017)"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Boteanu, A., Chernova, S.: Modeling topics in user dialog for interactive tablet media. In: AAAI Workshop, pp. 2\u20138 (2012)","DOI":"10.1609\/aiide.v8i5.12573"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Hisano, R.: Learning topic models by neighborhood aggregation. In: Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI 2019, pp. 2498\u20132505 (2019)","DOI":"10.24963\/ijcai.2019\/347"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Beg, M., Javed, H.: Topic modelling with fuzzy document representation. In: Advances in Computing and Data Sciences, pp. 577\u2013587 (2019)","DOI":"10.1007\/978-981-13-9942-8_54"},{"key":"44_CR11","unstructured":"Dieng, A., Ruiz, F., Blei, D.: The Dynamic Embedded Topic Model (2019)"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Nugmanova, A., Smirnov, A., Lavrentyeva, G., Chernykh, I.: Strategy of the negative sampling for training retrieval-based dialogue systems. In: IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 844\u2013848 (2019)","DOI":"10.1109\/PERCOMW.2019.8730665"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, S., Li, D., Li, X., Xu, Z.: Combine topic modeling with semantic embedding: embedding enhanced topic model. IEEE Trans. Knowl. Data Eng. 1 (2019)","DOI":"10.1109\/TKDE.2019.2922179"},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Mao, Q., Feng, B., Pan, S.: A Bayesian nonparametric topic model for user interest modeling. In: Conference: 2014 IEEE 17th International Conference on Computational Science and Engineering, pp. 527\u2013534 (2014)","DOI":"10.1109\/CSE.2014.122"},{"key":"44_CR15","unstructured":"M\u00e4hr, M., Hoffmann, H., Zetti, D.: Topic modelling and explorative search. In: Conference: Workshop DARIAH-CH (2018)"},{"key":"44_CR16","unstructured":"Korshunova, I., Xiong, H., Fedoryszak, M., Theis, L.: Discriminative Topic Modeling with Logistic LDA (2019)"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Tkachenko, M., Lauw, H.: CompareLDA: a topic model for document comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7112\u20137119 (2019)","DOI":"10.1609\/aaai.v33i01.33017112"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, F., Jiang, F., Jin, S., Xu, J.: A topic model for hierarchical documents. In: 1st IEEE International Conference on Data Science in Cyberspace (2016)","DOI":"10.1109\/DSC.2016.97"},{"key":"44_CR19","doi-asserted-by":"crossref","unstructured":"Gerlach, M., Peixoto, T., Altmann, E.: A network approach to topic models. Sci. Adv. (2018)","DOI":"10.1126\/sciadv.aaq1360"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Pfeifer, D., Leidner, J.: Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling (2019)","DOI":"10.1007\/978-3-030-15712-8_38"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Iwata, T., Hirao, T., Ueda, N.: Topic models for unsupervised cluster matching. IEEE Trans. Knowl. Data Eng. 1 (2017)","DOI":"10.1109\/TKDE.2017.2778720"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Krasnashchok, K., Cherif, A.: Coherence regularization for neural topic models. In: Advances in Neural Networks (2019)","DOI":"10.1007\/978-3-030-22796-8_45"},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Nan, F., Ding, R., Nallapati, R., Xiang, B.: Topic Modeling with Wasserstein Autoencoders (2019)","DOI":"10.18653\/v1\/P19-1640"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Khatri, C., Goel, R., Hedayatnia, B., Metanillou, A., Venkatesh, A., Gabriel, R., Mandal, A.: Contextual topic modeling for dialog systems. In: Conference: 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 892\u2013899 (2018)","DOI":"10.1109\/SLT.2018.8639552"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Ma, Y., Fosler-Lussier, E.: Detecting \u2018Request Alternatives\u2019 user dialog acts from dialog context. In: Situated Dialog in Speech-Based Human-Computer Interaction (2016)","DOI":"10.1007\/978-3-319-21834-2_9"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-55187-2_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T19:28:28Z","timestamp":1668022108000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-55187-2_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,25]]},"ISBN":["9783030551865","9783030551872"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-55187-2_44","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020,8,25]]},"assertion":[{"value":"25 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of SAI Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/IntelliSys","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}