{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:30:05Z","timestamp":1783074605451,"version":"3.54.6"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030017156","type":"print"},{"value":"9783030017163","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-01716-3_21","type":"book-chapter","created":{"date-parts":[[2018,10,6]],"date-time":"2018-10-06T14:11:06Z","timestamp":1538835066000},"page":"250-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Attention-Based CNN-BLSTM Networks for Joint Intent Detection and Slot Filling"],"prefix":"10.1007","author":[{"given":"Yufan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,10,7]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Shen, B., Inkpen, D.: Speech intent recognition for robots (2017)","DOI":"10.1109\/MCSI.2016.042"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Liu, B., Lane, I.: Attention-based recurrent neural network models for joint intent detection and slot filling (2016)","DOI":"10.21437\/Interspeech.2016-1352"},{"key":"21_CR3","unstructured":"Liu, B., Line, I.: Recurrent neural network structured output prediction for spoken language understanding (2015)"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"21_CR5","unstructured":"Zhang, X., Wang, H.: A joint model of intent determination and slot filling for spoken language understanding, pp. 5690\u20135694 (2016)"},{"key":"21_CR6","unstructured":"Haffner, P., Tur, G., Wright, J.H.: Optimizing SVMs for complex call classification (2003)"},{"issue":"3","key":"21_CR7","doi-asserted-by":"publisher","first-page":"5432","DOI":"10.1016\/j.eswa.2008.06.054","volume":"36","author":"J Chen","year":"2009","unstructured":"Chen, J., Huang, H., Tian, S., et al.: Feature selection for text classification with Naive Bayes. Expert Syst. Appl. Int. J. 36(3), 5432\u20135435 (2009)","journal-title":"Expert Syst. Appl. Int. J."},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM (2014)","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"21_CR9","unstructured":"Xiao, Y., Cho, K.: Efficient character-level document classification by combining convolution and recurrent layers (2016)"},{"issue":"2","key":"21_CR10","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1145\/1282080.1282082","volume":"6","author":"J Xiao","year":"2007","unstructured":"Xiao, J., Wang, X., Liu, B.: The study of a nonstationary maximum entropy Markov model and its application on the pos-tagging task. ACM Trans. Asian Lang. Inf. Process. 6(2), 7 (2007)","journal-title":"ACM Trans. Asian Lang. Inf. Process."},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Raymond, C., Riccardi, G.: Generative and discriminative algorithms for spoken language understanding (2007)","DOI":"10.21437\/Interspeech.2007-448"},{"key":"21_CR12","unstructured":"Aliannejadi, M., Kiaeeha, M., Khadivi, S., et al.: Graph-based semi-supervised conditional random fields for spoken language understanding using unaligned data (2017)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Xu, P., Sarikaya, R.: Convolutional neural network based triangular CRF for joint intent detection and slot filling (2014)","DOI":"10.1109\/ASRU.2013.6707709"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Yao, K., Peng, B., Zhang, Y., et al.: Spoken language understanding using long short-term memory neural networks (2015)","DOI":"10.1109\/SLT.2014.7078572"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Vu, N.T., Gupta, P., Adel, H., et al.: Bi-directional recurrent neural network with ranking loss for spoken language understanding (2016)","DOI":"10.1109\/ICASSP.2016.7472841"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Kurata, G., Xiang, B., Zhou, B., et al.: Leveraging sentence-level information with encoder LSTM for natural language understanding (2016)","DOI":"10.18653\/v1\/D16-1223"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, S., Yu, K.: Encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding (2017)","DOI":"10.1109\/ICASSP.2017.7953243"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Guo, D., Tur, G., Yih, W.T., et al.: Joint semantic utterance classification and slot filling with recursive neural networks (2015)","DOI":"10.1109\/SLT.2014.7078634"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Liu, B., Lane, I.: Joint online spoken language understanding and language modeling with recurrent neural networks (2016)","DOI":"10.18653\/v1\/W16-3603"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Weigelt, S., Hey, T., Landh\u00e4u\u00dfer, M.: Integrating a dialog component into a framework for spoken language understanding (2018)","DOI":"10.1145\/3194104.3194105"},{"issue":"4","key":"21_CR21","first-page":"39","volume":"1","author":"C Zhou","year":"2015","unstructured":"Zhou, C., Sun, C., Liu, Z., et al.: A C-LSTM neural network for text classification. Comput. Sci. 1(4), 39\u201344 (2015)","journal-title":"Comput. Sci."},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Yao, K., Peng, B., Zhang, Y., et al.: Spoken language understanding using long short-term memory neural networks. In: IEEE \u2013 Institute of Electrical & Electronics Engineers, pp. 189\u2013194 (2014)","DOI":"10.1109\/SLT.2014.7078572"},{"key":"21_CR23","unstructured":"Word2vec Homepage. http:\/\/code.google.com\/archive\/p\/word2vec\/"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Yin, W., Sch\u00fctze, H., Xiang, B., et al.: ABCNN: attention-based convolutional neural network for modeling sentence pairs (2015)","DOI":"10.1162\/tacl_a_00244"},{"key":"21_CR25","unstructured":"Morin, F., Bengio, Y.: Hierarchical probabilistic neural network language model. Aistats (2005)"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Hemphill, C.T., Godfrey, J.J., Doddington, G.R.: The ATIS spoken language systems pilot corpus. In: Proceedings of the Darpa Speech & Natural Language Workshop, pp. 96\u2013101 (1990)","DOI":"10.3115\/116580.116613"},{"key":"21_CR28","unstructured":"Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: International Conference on Machine Learning, pp. 2342\u20132350. JMLR.org (2015)"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01716-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:07:05Z","timestamp":1709809625000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01716-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030017156","9783030017163"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01716-3_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"7 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cips-cl.org\/static\/CCL2018\/index.html","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":"www.softconf.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"84","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":"33","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":"0","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":"39% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}