{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:55:19Z","timestamp":1742921719771,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757618"},{"type":"electronic","value":"9783030757625"}],"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\/978-3-030-75762-5_28","type":"book-chapter","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T09:07:43Z","timestamp":1620464863000},"page":"346-357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion"],"prefix":"10.1007","author":[{"given":"Zijian","family":"Wang","sequence":"first","affiliation":[]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiangfeng","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Jianqi","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"key":"28_CR1","unstructured":"Asghar, N.: Automatic extraction of causal relations from natural language texts: a comprehensive survey. arXiv preprint arXiv:1605.07895 (2016)"},{"key":"28_CR2","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","volume":"72","author":"T Chen","year":"2017","unstructured":"Chen, T., Xu, R., He, Y., Wang, X.: Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst. Appl. 72, 221\u2013230 (2017)","journal-title":"Expert Syst. Appl."},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural 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), pp. 167\u2013176. Association for Computational Linguistics (July 2015)","DOI":"10.3115\/v1\/P15-1017"},{"key":"28_CR4","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)"},{"key":"28_CR5","unstructured":"Girju, R., Moldovan, D.I., et al.: Text mining for causal relations. In: FLAIRS Conference, pp. 360\u2013364 (2002)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Grishman, R.: Domain modeling for language analysis. Tech. rep., New York Univ. NY (1988)","DOI":"10.21236\/ADA203444"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Hendrickx, I., et al.: Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 33\u201338. Association for Computational Linguistics (2010)","DOI":"10.3115\/1621969.1621986"},{"key":"28_CR8","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"issue":"4","key":"28_CR9","first-page":"567","volume":"24","author":"F Jian","year":"2011","unstructured":"Jian, F., Zong-Tian, L., Wei, L., Wen, Z.: Event causal relation extraction based on cascaded conditional random fields. Pattern Recognit. Artif. Intell. 24(4), 567\u2013573 (2011)","journal-title":"Pattern Recognit. Artif. Intell."},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/978-3-030-47426-3_57","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"X Jin","year":"2020","unstructured":"Jin, X., Wang, X., Luo, X., Huang, S., Gu, S.: Inter-sentence and implicit causality extraction from Chinese corpus. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 739\u2013751. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47426-3_57"},{"issue":"1","key":"28_CR11","first-page":"31","volume":"4","author":"J Kontos","year":"1991","unstructured":"Kontos, J., Sidiropoulou, M.: On the acquisition of causal knowledge from scientific texts with attribute grammars. Int. J. Appl. Expert Syst. 4(1), 31\u201348 (1991)","journal-title":"Int. J. Appl. Expert Syst."},{"key":"28_CR12","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.eswa.2018.08.009","volume":"115","author":"P Li","year":"2019","unstructured":"Li, P., Mao, K.: Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts. Expert Syst. Appl. 115, 512\u2013523 (2019)","journal-title":"Expert Syst. Appl."},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Li, S., Zhao, Z., Liu, T., Hu, R., Du, X.: Initializing convolutional filters with semantic features for text classification. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1884\u20131889 (2017)","DOI":"10.18653\/v1\/D17-1201"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2124\u20132133 (2016)","DOI":"10.18653\/v1\/P16-1200"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)","DOI":"10.18653\/v1\/P16-1101"},{"key":"28_CR16","unstructured":"Mart\u00ednez, E., Shwartz, V., Gurevych, I., Dagan, I.: Neural disambiguation of causal lexical markers based on context. In: IWCS 2017\u201312th International Conference on Computational Semantics-Short papers (2017)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Riaz, M., Girju, R.: Another look at causality: discovering scenario-specific contingency relationships with no supervision. In: 2010 IEEE Fourth International Conference on Semantic Computing, pp. 361\u2013368. IEEE (2010)","DOI":"10.1109\/ICSC.2010.19"},{"key":"28_CR18","unstructured":"Shen, Y., Huang, X.J.: Attention-based convolutional neural network for semantic relation extraction. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2526\u20132536 (2016)"},{"key":"28_CR19","first-page":"37","volume":"2013","author":"A Sorgente","year":"2013","unstructured":"Sorgente, A., Vettigli, G., Mele, F.: Automatic extraction of cause-effect relations in natural language text. DART@ AI* IA 2013, 37\u201348 (2013)","journal-title":"DART@ AI* IA"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Strubell, E., Verga, P., Belanger, D., McCallum, A.: Fast and accurate entity recognition with iterated dilated convolutions. arXiv preprint arXiv:1702.02098 (2017)","DOI":"10.18653\/v1\/D17-1283"},{"key":"28_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"28_CR22","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1016\/j.neucom.2015.09.066","volume":"173","author":"S Zhao","year":"2016","unstructured":"Zhao, S., Liu, T., Zhao, S., Chen, Y., Nie, J.Y.: Event causality extraction based on connectives analysis. Neurocomputing 173, 1943\u20131950 (2016)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75762-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:16:47Z","timestamp":1710350207000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75762-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757618","9783030757625"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75762-5_28","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":"9 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"23% - 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":"7","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)"}}]}}