{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:29:59Z","timestamp":1764174599050,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030017156"},{"type":"electronic","value":"9783030017163"}],"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_16","type":"book-chapter","created":{"date-parts":[[2018,10,6]],"date-time":"2018-10-06T14:11:06Z","timestamp":1538835066000},"page":"183-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Scientific Keyphrase Extraction: Extracting Candidates with Semi-supervised Data Augmentation"],"prefix":"10.1007","author":[{"given":"Qianying","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daisuke","family":"Kawahara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,7]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ammar, W., Peters, M.E., Bhagavatula, C., Power, R.: The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval@ACL 2017, 3\u20134 August 2017, Vancouver, Canada, pp. 592\u2013596 (2017)","key":"16_CR1","DOI":"10.18653\/v1\/S17-2097"},{"unstructured":"Batista, G.E., Bazzan, A.L., Monard, M.C.: Balancing training data for automated annotation of keywords: a case study. In: WOB, pp. 10\u201318 (2003)","key":"16_CR2"},{"doi-asserted-by":"crossref","unstructured":"Bird, S., Loper, E.: NLTK: the natural language toolkit. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, p. 31. Association for Computational Linguistics (2004)","key":"16_CR3","DOI":"10.3115\/1219044.1219075"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"16_CR5","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JP Chiu","year":"2016","unstructured":"Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357\u2013370 (2016)","journal-title":"Trans. Assoc. Comput. Linguist."},{"doi-asserted-by":"crossref","unstructured":"Danesh, S., Sumner, T., Martin, J.H.: Sgrank: combining statistical and graphical methods to improve the state of the art in unsupervised keyphrase extraction. In: Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, *SEM 2015, 4\u20135 June 2015, Denver, Colorado, USA, pp. 117\u2013126 (2015)","key":"16_CR6","DOI":"10.18653\/v1\/S15-1013"},{"doi-asserted-by":"crossref","unstructured":"Dong, L., Mallinson, J., Reddy, S., Lapata, M.: Learning to paraphrase for question answering. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9\u201311 September 2017, Copenhagen, Denmark, pp. 875\u2013886 (2017)","key":"16_CR7","DOI":"10.18653\/v1\/D17-1091"},{"doi-asserted-by":"crossref","unstructured":"Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 1262\u20131273 (2014)","key":"16_CR8","DOI":"10.3115\/v1\/P14-1119"},{"unstructured":"Hosseini, H., Kannan, S., Zhang, B., Poovendran, R.: Deceiving Google\u2019s perspective API built for detecting toxic comments. arXiv preprint arXiv:1702.08138 (2017)","key":"16_CR9"},{"unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)","key":"16_CR10"},{"doi-asserted-by":"crossref","unstructured":"Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9\u201311 September 2017, Copenhagen, Denmark, pp. 2021\u20132031 (2017)","key":"16_CR11","DOI":"10.18653\/v1\/D17-1215"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014)","key":"16_CR12"},{"unstructured":"Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Conference on Empirical Methods in Natural Language Processing, pp. 366\u2013376 (2010)","key":"16_CR13"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Conference on Empirical Methods in Natural Language Processing, pp. 257\u2013266 (2009)","key":"16_CR14","DOI":"10.3115\/1699510.1699544"},{"unstructured":"Lopez, P., Romary, L.: HUMB: automatic key term extraction from scientific articles in grobid. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, pp. 248\u2013251. Association for Computational Linguistics, Stroudsburg (2010)","key":"16_CR15"},{"doi-asserted-by":"crossref","unstructured":"Luan, Y., Ostendorf, M., Hajishirzi, H.: Scientific information extraction with semi-supervised neural tagging. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9\u201311 September 2017, Copenhagen, Denmark, pp. 2641\u20132651 (2017)","key":"16_CR16","DOI":"10.18653\/v1\/D17-1279"},{"doi-asserted-by":"crossref","unstructured":"Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7\u201312 August 2016, Berlin, Germany, vol. 1: Long Papers (2016)","key":"16_CR17","DOI":"10.18653\/v1\/P16-1101"},{"unstructured":"Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25\u201326 July 2004, Barcelona, Spain, pp. 404\u2013411 (2004)","key":"16_CR18"},{"unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)","key":"16_CR19"},{"issue":"Oct","key":"16_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25\u201329 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532\u20131543 (2014)","key":"16_CR21","DOI":"10.3115\/v1\/D14-1162"},{"unstructured":"Samanta, S., Mehta, S.: Towards crafting text adversarial samples. arXiv preprint arXiv:1707.02812 (2017)","key":"16_CR22"},{"issue":"1","key":"16_CR23","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/MCI.2012.2228600","volume":"8","author":"F Schwenker","year":"2013","unstructured":"Schwenker, F.: Ensemble methods: foundations and algorithms [book review]. IEEE Comput. Int. Mag. 8(1), 77\u201379 (2013)","journal-title":"IEEE Comput. Int. Mag."},{"issue":"6","key":"16_CR24","first-page":"448","volume":"SMC\u20136","author":"I Tomek","year":"1976","unstructured":"Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC\u20136(6), 448\u2013452 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"11","key":"16_CR25","first-page":"769","volume":"SMC\u20136","author":"I Tomek","year":"1976","unstructured":"Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. SMC\u20136(11), 769\u2013772 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"doi-asserted-by":"crossref","unstructured":"Wang, C., Li, S.: CoRankBayes: Bayesian learning to rank under the co-training framework and its application in keyphrase extraction. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2241\u20132244. ACM, New York (2011)","key":"16_CR26","DOI":"10.1145\/2063576.2063936"},{"key":"16_CR27","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/978-3-642-00831-3_29","volume-title":"Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy","author":"C Wang","year":"2009","unstructured":"Wang, C., Li, S., Wang, W.: Experiment research on feature selection and learning method in keyphrase extraction. In: Li, W., Moll\u00e1-Aliod, D. (eds.) ICCPOL 2009. LNCS (LNAI), vol. 5459, pp. 305\u2013312. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-00831-3_29"},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Li, S.: PKU\\_ICL at SemEval-2017 Task 10: Keyphrase extraction with model ensemble and external knowledge. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 934\u2013937 (2017)","key":"16_CR28","DOI":"10.18653\/v1\/S17-2161"},{"doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Kasai, J., Radev, D.: Robust multilingual part-of-speech tagging via adversarial training. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 976\u2013986. Association for Computational Linguistics (2018)","key":"16_CR29","DOI":"10.18653\/v1\/N18-1089"}],"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_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:06:01Z","timestamp":1709809561000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01716-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030017156","9783030017163"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01716-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}