{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T11:11:20Z","timestamp":1726053080773},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322359"},{"type":"electronic","value":"9783030322366"}],"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\/978-3-030-32236-6_28","type":"book-chapter","created":{"date-parts":[[2019,9,29]],"date-time":"2019-09-29T23:23:57Z","timestamp":1569799437000},"page":"318-326","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Sequence-to-Action Architecture for Character-Based Chinese Dependency Parsing with Status History"],"prefix":"10.1007","author":[{"given":"Hang","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jinan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yufeng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"28_CR1","unstructured":"Hatori, J., Matsuzaki, T., Miyao, Y., Tsujii, J.I.: Incremental joint approach to word segmentation, POS tagging, and dependency parsing in Chinese. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pp. 1045\u20131053. Association for Computational Linguistics (2012)"},{"key":"28_CR2","unstructured":"Nivre, J. An efficient algorithm for projective dependency parsing. In: Proceedings of the Eighth International Conference on Parsing Technologies, pp. 149\u2013160 (2003)"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, Y., Che, W., Liu, T.: Character-level Chinese dependency parsing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, vol. 1, pp. 1326\u20131336 (2014)","DOI":"10.3115\/v1\/P14-1125"},{"issue":"1","key":"28_CR4","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1587\/transinf.2015EDP7118","volume":"99","author":"Z Guo","year":"2016","unstructured":"Guo, Z., Zhang, Y., Su, C., Xu, J., Isahara, H.: Character-level dependency model for joint word segmentation, POS tagging, and dependency parsing in Chinese. IEICE Trans. Inf. Syst. 99(1), 257\u2013264 (2016)","journal-title":"IEICE Trans. Inf. Syst."},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Kurita, S., Kawahara, D., Kurohashi, S.: Neural joint model for transition-based Chinese syntactic analysis. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, pp. 1204\u20131214 (2017)","DOI":"10.18653\/v1\/P17-1111"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, Z., Ju, Y., Zhao, H.: Neural character-level dependency parsing for Chinese. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12002"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Dyer, C., Ballesteros, M., Ling, W., Matthews, A., Smith, N.A.: Transition-based dependency parsing with stack long short-term memory. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Volume 1: Long Papers, vol. 1, pp. 334\u2013343 (2015)","DOI":"10.3115\/v1\/P15-1033"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Gauthier, J., Rastogi, A., Gupta, R., Manning, C.D., Potts, C.: A fast unified model for parsing and sentence understanding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, vol. 1, pp. 1466\u20131477 (2016)","DOI":"10.18653\/v1\/P16-1139"},{"key":"28_CR9","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2, pp. 3104\u20133112. MIT Press (2014)"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Nivre, J.: Incrementality in deterministic dependency parsing. In: Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together, pp. 50\u201357. Association for Computational Linguistics (2004)","DOI":"10.3115\/1613148.1613156"},{"key":"28_CR11","unstructured":"Chen, X., Xu, L., Liu, Z., Sun, M., Luan, H.: Joint learning of character and word embeddings. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 1236\u20131242. AAAI Press (2015)"},{"key":"28_CR12","unstructured":"Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734 (2016)"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Volume 1: Long Papers, vol. 1, pp. 1556\u20131566 (2015)","DOI":"10.3115\/v1\/P15-1150"},{"key":"28_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Jiang, W., Huang, L., Liu, Q., Lv, Y.: A cascaded linear model for joint Chinese word segmentation and part-of-speech tagging. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (2008)","DOI":"10.3115\/1599081.1599130"},{"key":"28_CR16","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"issue":"1","key":"28_CR17","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1: Long Papers, pp. 2227\u20132237 (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"28_CR19","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32236-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T01:04:06Z","timestamp":1695258246000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-32236-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322359","9783030322366"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32236-6_28","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)"}}]}}