{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T10:49:00Z","timestamp":1649155740340},"reference-count":26,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2021,9,1]]},"DOI":"10.1587\/transinf.2020edp7249","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:18:56Z","timestamp":1630448336000},"page":"1486-1495","source":"Crossref","is-referenced-by-count":0,"title":["Gated Convolutional Neural Networks with Sentence-Related Selection for Distantly Supervised Relation Extraction"],"prefix":"10.1587","volume":"E104.D","author":[{"given":"Yufeng","family":"CHEN","sequence":"first","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, the School of Computer and Information Technology, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"LI","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, the School of Computer and Information Technology, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingya","family":"LI","sequence":"additional","affiliation":[{"name":"China Institute of Marine Technology & Economy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinan","family":"XU","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, the School of Computer and Information Technology, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"LIU","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, the School of Computer and Information Technology, Beijing Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] M. Mintz, S. Bills, R. Snow, and D. Jurafsky, \u201cDistant supervision for relation extraction without labeled data,\u201d Proc. Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp.1003-1011, 2009. 10.3115\/1690219.1690287","DOI":"10.3115\/1690219.1690287"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] D. Zeng, K. Liu, Y. Chen, and J. Zhao, \u201cDistant supervision for relation extraction via piece-wise convolutional neural networks,\u201d Proc. Conference on Empirical Methods in Natural Language Processing, pp.1753-1762, 2015. 10.18653\/v1\/d15-1203","DOI":"10.18653\/v1\/D15-1203"},{"key":"3","unstructured":"[3] G. Ji, K. Liu, S. He, L. Xu, and J. Zhao, \u201cDistant supervision for relation extraction with sentence-level attention and entity descriptions,\u201d Proc. AAAI-2017, pp.3060-3066, 2017."},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Y. Li, G. Long, T. Shen, T. Zhou, L. Yao, H. Huo, and J. Jiang, \u201cSelf-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction,\u201d Proc. AAAI-2020, pp.8269-8276, 2020. 10.1609\/aaai.v34i05.6342","DOI":"10.1609\/aaai.v34i05.6342"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. Lin, S. Shen, Z. Liu, H. Luan, and M. Sun, \u201cNeural relation extraction with selective attention over instances,\u201d Proc. 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.2124-2133, 2016. 10.18653\/v1\/p16-1200","DOI":"10.18653\/v1\/P16-1200"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] C. Yuan, H. Huang, C. Feng, X. Liu, and X. Wei, \u201cDistant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding,\u201d Proc. AAAI Conference on Artificial Intelligence, pp.7418-7425, 2019. 10.1609\/aaai.v33i01.33017418","DOI":"10.1609\/aaai.v33i01.33017418"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] H. Yuan, \u201cCombined Networks with Multi-level Attention for Distantly-Supervised Relation Extraction,\u201d Journal of Physics Conference Series, vol.1550, pp.1-4, 2020. 10.1088\/1742-6596\/1550\/3\/032065","DOI":"10.1088\/1742-6596\/1550\/3\/032065"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] T. Liu, X. Zhang, W. Zhou, and W. Jia, \u201cNeural relation extraction via inner-sentence noise reduction and transfer learning,\u201d Proc. Conference on Empirical Methods in Natural Language Processing, pp.2195-2204, 2018. 10.18653\/v1\/d18-1243","DOI":"10.18653\/v1\/D18-1243"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] S. Vashishth, R. Joshi, and S. Prayaga, \u201cReside: Improving distantly-supervised neural relation extraction using side information,\u201d Proc. Conference on Empirical Methods in Natural Language Processing, pp.1257-1266, 2018.","DOI":"10.18653\/v1\/D18-1157"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] T. Liu, K. Wang, B. Chang, and Z. Sui, \u201cA soft-label method for noise-tolerant distantly supervised relation extraction,\u201d Proc. Conference on Empirical Methods in Natural Language Processing, pp.1790-1795, 2017. 10.18653\/v1\/d17-1189","DOI":"10.18653\/v1\/D17-1189"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"12","unstructured":"[12] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, and L. Kaiser, \u201cAttention is all you need,\u201d Advances in Neural Information Processing Systems, pp.5998-6008, 2017."},{"key":"13","unstructured":"[13] Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, \u201cTranslating embeddings for modeling multi relational data,\u201d Proc. International Conference on Neural Information Processing Systems, pp.2787-2795, 2013."},{"key":"14","unstructured":"[14] N. Kalchbrenner, L. Espeholt, K. Simonyan, A. Oord, A. Graves, and K. Kavukcuoglu, \u201cNeural machine translation in linear time,\u201d arXiv preprint arXiv, 1610.10099, 2016."},{"key":"15","unstructured":"[15] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. Dauphin, \u201cConvolutional sequence to sequence learning,\u201d Proc. 34th International Conference on Machine Learning, pp.1243-1252, 2017."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] S. Riedel, L. Yao, and A. McCallum, \u201cModeling relations and their mentions without labeled text,\u201d Proc. Machine Learning and Knowledge Discovery in Databases, pp.148-163, 2010. 10.1007\/978-3-642-15939-8_10","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"17","unstructured":"[17] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cEfficient estimation of word representations in vector space,\u201d arXiv preprint arXiv, 1301.3781, 2013."},{"key":"18","unstructured":"[18] M. Surdeanu, J. Tibshirani, R. Nallapati, and C.D. Manning, \u201cMulti-instance multi-label learning for relation extraction,\u201d Proc. Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.455-465, 2012."},{"key":"19","unstructured":"[19] R. Hoffmann, C. Zhang, X. Ling, L. Zettlemoyer, and D.S. Weld, \u201cKnowledge-based weak supervision for information extraction of overlapping relations,\u201d Proc. ACL-2011, pp.541-550, 2011."},{"key":"20","unstructured":"[20] J. Feng, M. Huang, L. Zhao, Y. Yang, and X. Zhu, \u201cReinforcement learning for relation classification from noisy data,\u201d Proc. AAAI-2018, pp.5779-5786, 2018."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] P. Qin, W. Xu, and W.Y. Wang, \u201cRobust distant supervision relation extraction via deep reinforcement learning,\u201d Proc. ACL, pp.2137-2147, 2018. 10.18653\/v1\/p18-1199","DOI":"10.18653\/v1\/P18-1199"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] Y. Wu, D. Bamman, and S. Russell, \u201cAdversarial training for relation extraction,\u201d Proc. Conference on Empirical Methods in Natural Language Processing, pp.1778-1783, 2017. 10.18653\/v1\/d17-1187","DOI":"10.18653\/v1\/D17-1187"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] P. Qin, W. Xu, and W.Y. Wang, \u201cDSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction,\u201d Proc. 56th Annual Meeting of the Association for Computational Linguistics, pp.496-505, 2018. 10.18653\/v1\/p18-1046","DOI":"10.18653\/v1\/P18-1046"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] G. Wang, W. Zhang, R. Wang, Y. Zhou, X. Chen, W. Zhang, H. Zhu, and H. Chen, \u201cLabel-free distant supervision for relation extraction via knowledge graph embedding,\u201d Proc. Conference on Empirical Methods in Natural Language Processing, pp.2246-2255, 2018. 10.18653\/v1\/d18-1248","DOI":"10.18653\/v1\/D18-1248"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] X. Huang, B. Zhang, Y. Ye, X. Chen, and X. Li, \u201cA Noise Adaptive Model for Distantly Supervised Relation Extraction,\u201d Proc. CCF International Conference on Natural Language Processing and Chinese Computing, pp.519-530, 2020. 10.1007\/978-3-030-60450-9_41","DOI":"10.1007\/978-3-030-60450-9_41"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] X. Li, Y. Chen, J. Xu, and Y. Zhang, \u201cAttention-Based Gated Convolutional Neural Networks for Distant Supervised Relation Extraction,\u201d Proc. Chinese Computational Linguistics, pp.246-257, 2019. 10.1007\/978-3-030-32381-3_20","DOI":"10.1007\/978-3-030-32381-3_20"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/9\/E104.D_2020EDP7249\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T03:55:52Z","timestamp":1630727752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/9\/E104.D_2020EDP7249\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,1]]},"references-count":26,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2020edp7249","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,1]]},"article-number":"2020EDP7249"}}