{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:43:50Z","timestamp":1743000230717,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"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_65","type":"book-chapter","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T22:04:51Z","timestamp":1569967491000},"page":"840-851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dependency-Gated Cascade Biaffine Network for Chinese Semantic Dependency Graph Parsing"],"prefix":"10.1007","author":[{"given":"Zizhuo","family":"Shen","sequence":"first","affiliation":[]},{"given":"Huayong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dianqing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yanqiu","family":"Shao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"65_CR1","unstructured":"Xue, N., Palmer, M.: Automatic semantic role labeling for Chinese verbs. In: IJCAI, vol. 5 (2005)"},{"key":"65_CR2","doi-asserted-by":"crossref","unstructured":"Carreras, X., M\u00e0rquez, L.: Introduction to the CoNLL-2004 shared task: semantic role labeling. In: Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL (2004)","DOI":"10.3115\/1706543.1706571"},{"key":"65_CR3","doi-asserted-by":"crossref","unstructured":"Toutanova, K., Haghighi, A., Manning, C.D.: Joint learning improves semantic role labeling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (2005)","DOI":"10.3115\/1219840.1219913"},{"key":"65_CR4","doi-asserted-by":"crossref","unstructured":"Haji\u010d, J., et al.: The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task. Association for Computational Linguistics (2009)","DOI":"10.3115\/1596409.1596411"},{"key":"65_CR5","doi-asserted-by":"crossref","unstructured":"McDonald, R., et al.: Non-projective dependency parsing using spanning tree algorithms. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2005)","DOI":"10.3115\/1220575.1220641"},{"key":"65_CR6","unstructured":"Che, W., et al.: Semeval-2012 task 5: Chinese semantic dependency parsing. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation. Association for Computational Linguistics (2012)"},{"key":"65_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/978-3-319-12277-9_6","volume-title":"Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data","author":"Yu Ding","year":"2014","unstructured":"Ding, Yu., Shao, Y., Che, W., Liu, T.: Dependency graph based Chinese semantic parsing. In: Sun, M., Liu, Y., Zhao, J. (eds.) CCL\/NLP-NABD -2014. LNCS (LNAI), vol. 8801, pp. 58\u201369. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-12277-9_6"},{"key":"65_CR8","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: A neural transition-based approach for semantic dependency graph parsing. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11968"},{"key":"65_CR9","doi-asserted-by":"crossref","unstructured":"Dyer, C., et al.: Transition-based dependency parsing with stack long short-term memory. arXiv preprint arXiv:1505.08075 (2015)","DOI":"10.3115\/v1\/P15-1033"},{"key":"65_CR10","doi-asserted-by":"crossref","unstructured":"Dozat, T., Manning, C.D.: Simpler but more accurate semantic dependency parsing. arXiv preprint arXiv:1807.01396 (2018)","DOI":"10.18653\/v1\/P18-2077"},{"key":"65_CR11","doi-asserted-by":"crossref","unstructured":"Dozat, T., Qi, P., Manning, C.D.: Stanford\u2019s graph-based neural dependency parser at the conll 2017 shared task. In: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017)","DOI":"10.18653\/v1\/K17-3002"},{"key":"65_CR12","unstructured":"Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734 (2016)"},{"key":"65_CR13","doi-asserted-by":"crossref","unstructured":"Chen, D., Manning, C.: A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1082"},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"Weiss, D., et al.: Structured training for neural network transition-based parsing. arXiv preprint arXiv:1506.06158 (2015)","DOI":"10.3115\/v1\/P15-1032"},{"key":"65_CR15","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1162\/tacl_a_00101","volume":"4","author":"E Kiperwasser","year":"2016","unstructured":"Kiperwasser, E., Goldberg, Y.: Simple and accurate dependency parsing using bidirectional LSTM feature representations. Trans. Assoc. Comput. Linguist. 4, 313\u2013327 (2016)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"65_CR16","unstructured":"Semeniuta, S., Severyn, A., Barth, E.: Recurrent dropout without memory loss. arXiv preprint arXiv:1603.05118 (2016)"},{"key":"65_CR17","unstructured":"Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)"},{"key":"65_CR18","doi-asserted-by":"crossref","unstructured":"Moon, T., et al.: RNNDROP: a novel dropout for RNNs in ASR. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE (2015)","DOI":"10.1109\/ASRU.2015.7404775"},{"key":"65_CR19","unstructured":"Zilly, J.G., et al.: Recurrent highway networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70. JMLR. org (2017)"},{"key":"65_CR20","unstructured":"Mikolov, T., et al.: Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems (2013)"},{"key":"65_CR21","doi-asserted-by":"crossref","unstructured":"Peng, H., Thomson, S., Smith, N.A.: Deep multitask learning for semantic dependency parsing. arXiv preprint arXiv:1704.06855 (2017)","DOI":"10.18653\/v1\/P17-1186"}],"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_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T10:53:26Z","timestamp":1721818406000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32233-5_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322328","9783030322335"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32233-5_65","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)"}}]}}