{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:28:04Z","timestamp":1743074884954,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322328"},{"type":"electronic","value":"9783030322335"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-32233-5_60","type":"book-chapter","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T22:04:51Z","timestamp":1569967491000},"page":"773-786","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Constructing Chinese Macro Discourse Tree via Multiple Views and Word Pair Similarity"],"prefix":"10.1007","author":[{"given":"Yi","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomin","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peifeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaoming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"60_CR1","doi-asserted-by":"crossref","unstructured":"Galitsky, B., Ilvovsky, D.: Building dialogue structure from discourse tree of a question. In: Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pp. 17\u201323 (2018)","DOI":"10.18653\/v1\/W18-5703"},{"key":"60_CR2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.eswa.2018.10.002","volume":"118","author":"M Kraus","year":"2019","unstructured":"Kraus, M., Feuerriegel, S.: Sentiment analysis based on rhetorical structure theory: learning deep neural networks from discourse trees. Expert Syst. Appl. 118, 65\u201379 (2019)","journal-title":"Expert Syst. Appl."},{"key":"60_CR3","unstructured":"Chu, X., Research on representation schema, resource construction and computational modeling of macro discourse structure. Doctorate dissertation, Soochow University, Suzhou, (2019). [in Chinese]"},{"key":"60_CR4","unstructured":"Zhou, Y., Chu, X., Zhu, Q., Jiang, F., Li, P. Macro discourse-level relation classification based on macro semantics representation. J. Chin. Inform. Process. 33, 1\u20137+24 (2019). [in Chinese]"},{"key":"60_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, F., Li, P., Chu, X., Zhu, Q., Zhou, G.: Recognizing macro Chinese discourse structure on label degeneracy combination model. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 92\u2013104 (2018)","DOI":"10.1007\/978-3-319-99501-4_8"},{"key":"60_CR6","unstructured":"Carlson, L., Marcu, D., Okurowski, M.: RST discourse treebank. Linguistic Data Consortium (2002)"},{"key":"60_CR7","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1080\/01638538609544632","volume":"9","author":"WC Mann","year":"1986","unstructured":"Mann, W.C., Thompson, S.A.: Relational propositions in discourse. Discourse Process. 9, 57\u201390 (1986)","journal-title":"Discourse Process."},{"key":"60_CR8","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1515\/text.1.1988.8.3.243","volume":"8","author":"WC Mann","year":"1988","unstructured":"Mann, W.C., Thompson, S.A.: Rhetorical structure theory: Toward a functional theory of text organization. Text-Interdisc. J. Study Discourse 8, 243\u2013281 (1988)","journal-title":"Text-Interdisc. J. Study Discourse"},{"key":"60_CR9","first-page":"313","volume":"19","author":"M Marcus","year":"1993","unstructured":"Marcus, M., Sanrotini, B., Marcinkiewicz, M.: Building a large annotated corpus of English: the Penn Treebank. Comput. Linguist. 19, 313\u2013330 (1993)","journal-title":"Comput. Linguist."},{"key":"60_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5087\/dad.2010.003","volume":"1","author":"H Hernault","year":"2010","unstructured":"Hernault, H., Prendinger, H., Ishizuka, M.: HILDA: A discourse parser using support vector machine classification. Dialogue Discourse 1, 1\u201333 (2010)","journal-title":"Dialogue Discourse"},{"key":"60_CR11","doi-asserted-by":"crossref","unstructured":"Feng, V.W., Hirst, G.: A linear-time bottom-up discourse parser with constraints and post-editing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 511\u2013521 (2014)","DOI":"10.3115\/v1\/P14-1048"},{"key":"60_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1162\/coli_a_00314","volume":"44","author":"M Morey","year":"2018","unstructured":"Morey, M., Muller, P., Asher, N.: A dependency perspective on RST discourse parsing and evaluation. Comput. Linguist. 44, 197\u2013235 (2018)","journal-title":"Comput. Linguist."},{"key":"60_CR13","doi-asserted-by":"crossref","unstructured":"Li, Q., Li, T., Chang, B.: Discourse parsing with attention-based hierarchical neural networks. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 362\u2013371 (2016)","DOI":"10.18653\/v1\/D16-1035"},{"key":"60_CR14","doi-asserted-by":"crossref","unstructured":"Jia, Y., Ye, Y., Feng, Y., Lai, Y., Yan, R., Zhao, D.: Modeling discourse cohesion for discourse parsing via memory network. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 438\u2013443 (2018)","DOI":"10.18653\/v1\/P18-2070"},{"key":"60_CR15","doi-asserted-by":"crossref","unstructured":"Morey, M., Muller, P., Asher, N.: How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1319\u20131324 (2017)","DOI":"10.18653\/v1\/D17-1136"},{"key":"60_CR16","doi-asserted-by":"crossref","unstructured":"Sporleder, C., Lascarides, A.: Combining hierarchical clustering and machine learning to predict high-level discourse structure. In: Proceedings of the 20th International Conference on Computational Linguistics (2004)","DOI":"10.3115\/1220355.1220362"},{"key":"60_CR17","unstructured":"Chu, X., Jiang, F., Xu, S., Zhu, Q.: Building a macro Chinese discourse treebank. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (2018)"},{"key":"60_CR18","unstructured":"Jiang, F., Xu, S., Chu, X., Li, P., Zhu, Q., Zhou, G.: MCDTB: a macro-level Chinese discourse treebank. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3493\u20133504 (2018)"},{"key":"60_CR19","unstructured":"Chu, X., Jiang, F., Zhou, Y., Zhou, G., Zhu, Q.: Joint modeling of structure identification and nuclearity recognition in macro Chinese discourse TreeBank. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 536\u2013546 (2018)"},{"key":"60_CR20","doi-asserted-by":"crossref","unstructured":"Marcu, D.: A decision-based approach to rhetorical parsing. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 365\u2013372 (1999)","DOI":"10.3115\/1034678.1034736"},{"key":"60_CR21","doi-asserted-by":"crossref","unstructured":"Lin, Z., Kan, M.-Y., Ng, H.T.: Recognizing implicit discourse relations in the Penn discourse Treebank. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 343\u2013351 (2009)","DOI":"10.3115\/1699510.1699555"},{"key":"60_CR22","unstructured":"Guo, F., He, R., Jin, D., Dang, J., Wang, L., Li, X.: Implicit discourse relation recognition using neural tensor network with interactive attention and sparse learning. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 547\u2013558 (2018)"},{"key":"60_CR23","unstructured":"Xu, S., Li, P., Zhou, G., Zhu, Q.: Employing text matching network to recognize nuclearity in Chinese discourse. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 525\u2013535 (2018)"},{"key":"60_CR24","unstructured":"Joty, S., Carenini, G., Ng, R., Mehdad, Y.: Combining intra-and multi-sentential rhetorical parsing for document-level discourse analysis. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 486\u2013496 (2013)"},{"key":"60_CR25","first-page":"43","volume":"32","author":"F Jiang","year":"2018","unstructured":"Jiang, F., Chu, X., Xu, S., Li, P., Zhu, Q.: A macro discourse primary and secondary relation recognition method based on topic similarity. J. Chin. Inform. Process. 32, 43\u201350 (2018). [in Chinese]","journal-title":"J. Chin. Inform. Process."}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32233-5_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:14:01Z","timestamp":1710353641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32233-5_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322328","9783030322335"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32233-5_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dunhuang","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2019\/","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":"softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"492","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":"85","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":"56","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":"17% - 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","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","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)"}}]}}