{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:34:00Z","timestamp":1743125640226,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922696"},{"type":"electronic","value":"9783030922702"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92270-2_44","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T11:06:00Z","timestamp":1638788760000},"page":"513-525","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analyzing Vietnamese Legal Questions Using Deep Neural Networks with\u00a0Biaffine Classifiers"],"prefix":"10.1007","author":[{"given":"Nguyen","family":"Anh Tu","sequence":"first","affiliation":[]},{"given":"Hoang","family":"Thi Thu Uyen","sequence":"additional","affiliation":[]},{"given":"Tu","family":"Minh Phuong","sequence":"additional","affiliation":[]},{"given":"Ngo","family":"Xuan Bach","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Bach, N.X., Cham, L.T.N., Thien, T.H.N., Phuong, T.M.: Question analysis for vietnamese legal question answering. In: Proceedings of KSE, pp. 154\u2013159 (2017)","DOI":"10.1109\/KSE.2017.8119451"},{"key":"44_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-29894-4_16","volume-title":"PRICAI 2019: Trends in Artificial Intelligence","author":"N Xuan Bach","year":"2019","unstructured":"Xuan Bach, N., Khuong Duy, T., Minh Phuong, T.: A POS tagging model for Vietnamese social media text using BiLSTM-CRF with rich features. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11672, pp. 206\u2013219. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29894-4_16"},{"issue":"1","key":"44_CR3","first-page":"112","volume":"20","author":"NX Bach","year":"2020","unstructured":"Bach, N.X., Thanh, P.D., Oanh, T.T.: Question analysis towards a Vietnamese question answering system in the education domain. Cybern. Inf. Technol. 20(1), 112\u2013128 (2020)","journal-title":"Cybern. Inf. Technol."},{"key":"44_CR4","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. TACL 5, 135\u2013146 (2017)","journal-title":"TACL"},{"key":"44_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186 (2019)"},{"key":"44_CR6","unstructured":"Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. In: Proceedings of ICLR (2017)"},{"key":"44_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-662-45237-0_19","volume-title":"Computer Information Systems and Industrial Management","author":"H-T Duong","year":"2014","unstructured":"Duong, H.-T., Ho, B.-Q.: A Vietnamese question answering system in Vietnam\u2019s legal documents. In: Saeed, K., Sn\u00e1\u0161el, V. (eds.) CISIM 2014. LNCS, vol. 8838, pp. 186\u2013197. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45237-0_19"},{"issue":"5\u20136","key":"44_CR8","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5\u20136), 602\u2013610 (2005)","journal-title":"Neural Netw."},{"key":"44_CR9","unstructured":"He, Z., Wang, X., Wei, W., Feng, S., Mao, X., Jiang, S.: A survey on recent advances in sequence labeling from deep learning models. arXiv preprint arXiv:2011.06727v1 (2020)"},{"key":"44_CR10","unstructured":"He, P., Liu, X., Gao, J., Chen, W.: DeBERTa: decoding-enhanced BERT with disentangled attention. In: Proceedings of ICLR (2021)"},{"issue":"8","key":"44_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Kien, P.M., Nguyen, H.T., Bach, N.X., Tran, V., Nguyen, M.L., Phuong, T.M.: Answering legal questions by learning neural attentive text representation. In: Proceedings of COLING, pp. 988\u2013998 (2020)","DOI":"10.18653\/v1\/2020.coling-main.86"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp. 1746\u20131751 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"44_CR14","unstructured":"Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML, pp. 282\u2013289 (2001)"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Le-Hong, P., Bui, D.T.: A factoid question answering system for Vietnamese. In: Proceedings of Web Conference Companion, Workshop Track, pp. 1049\u20131055 (2018)","DOI":"10.1145\/3184558.3191535"},{"issue":"110","key":"44_CR16","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(110), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Z., Zhang, M., Wang, R., Li, S., Si, L.: Self-attentive biaffine dependency parsing. In: Proceedings of IJCAI, pp. 5067\u20135073 (2019)","DOI":"10.24963\/ijcai.2019\/704"},{"key":"44_CR18","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692v1 (2019)"},{"key":"44_CR19","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: Proceedings of ICLR (2019)"},{"key":"44_CR20","unstructured":"Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of ICLR (2013)"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Mrini, K., Dernoncourt, F., Tran, Q.H., Bui, T., Chang, W., Nakashole, N.: Rethinking self-attention: towards interpretability in neural parsing. In: Proceedings of EMNLP Findings, pp. 731\u2013742 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.65"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Nguyen, D.Q., Pham., S.Q.: A Vietnamese question answering system. In: Proceedings of KSE, pp. 26\u201332 (2009)","DOI":"10.1109\/KSE.2009.42"},{"key":"44_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/978-3-030-15712-8_47","volume-title":"Advances in Information Retrieval","author":"DQ Nguyen","year":"2019","unstructured":"Nguyen, D.Q., Verspoor, K.: End-to-end neural relation extraction using deep biaffine attention. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11437, pp. 729\u2013738. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-15712-8_47"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Nguyen, A.T.: PhoBERT: pre-trained language models for Vietnamese. In: Proceedings of EMNLP, pp. 1037\u20131042 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.92"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Song, X., Petrak, J., Roberts, A.: A deep neural network sentence level classification method with context information. In: Proceedings of EMNLP, pp. 900\u2013904 (2018)","DOI":"10.18653\/v1\/D18-1107"},{"key":"44_CR27","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of NIPS (2014)"},{"key":"44_CR28","doi-asserted-by":"crossref","unstructured":"Tran, V.M., Nguyen, V.D., Tran, O.T., Pham, U.T.T., Ha, T.Q.: An experimental study of Vietnamese question answering system. In: Proceedings of IALP, pp. 152\u2013155 (2009)","DOI":"10.1109\/IALP.2009.39"},{"key":"44_CR29","unstructured":"Tran, V.M., Le, D.T., Tran, X.T., Nguyen, T.T.: A model of Vietnamese person named entity question answering system. In: Proceedings of PACLIC, pp. 325\u2013332 (2012)"},{"key":"44_CR30","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-319-10061-6_15","volume-title":"New Frontiers in Artificial Intelligence","author":"OT Tran","year":"2014","unstructured":"Tran, O.T., Ngo, B.X., Le Nguyen, M., Shimazu, A.: Answering legal questions by mining reference information. In: Nakano, Y., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2013. LNCS (LNAI), vol. 8417, pp. 214\u2013229. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10061-6_15"},{"key":"44_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of NIPS, pp. 6000\u20136010 (2017)"},{"key":"44_CR32","doi-asserted-by":"crossref","unstructured":"Vu, T., Nguyen, D.Q., Nguyen, D.Q., Dras, M., Johnson, M.: VnCoreNLP: a Vietnamese natural language processing toolkit. In: Proceedings NAACL Demonstrations, pp. 56\u201360 (2018)","DOI":"10.18653\/v1\/N18-5012"},{"key":"44_CR33","unstructured":"Yadav, V., Bethard, S.: A survey on recent advances in named entity recognition from deep learning models. In: Proceedings of COLING, pp. 2145\u20132158 (2018)"},{"key":"44_CR34","unstructured":"Yang, K., Deng, J.: Strongly incremental constituency parsing with graph neural networks. In: Proceedings of NeurIPS (2020)"},{"key":"44_CR35","unstructured":"Yang, S., Wang, Y., Chu, X.: A survey of deep learning techniques for neural machine translation. arXiv preprint arXiv:2002.07526v1 (2020)"},{"key":"44_CR36","doi-asserted-by":"crossref","unstructured":"Yu, J., Bohnet, B., Poesio, M.: Named entity recognition as dependency parsing. In: Proceedings of ACL, pp. 6470\u20136476 (2020)","DOI":"10.18653\/v1\/2020.acl-main.577"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92270-2_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:28:49Z","timestamp":1710257329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92270-2_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922696","9783030922702"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92270-2_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}