{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:52:02Z","timestamp":1763599922856,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.<\/jats:p>","DOI":"10.3390\/info11020079","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T11:55:56Z","timestamp":1580471756000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction"],"prefix":"10.3390","volume":"11","author":[{"given":"Xiaoyu","family":"Han","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenkai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tinglei","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,31]]},"reference":[{"key":"ref_1","unstructured":"Zhou, G., Su, J., Zhang, J., and Zhang, M. (2005, January 25\u201330). Exploring various knowledge in relation extraction. Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, Ann Arbor, MI, USA."},{"key":"ref_2","unstructured":"Surdeanu, M., Tibshirani, J., Nallapati, R., and Manning, C.D. (2012, January 12\u201314). Multi-instance Multi-label Learning for Relation Extraction. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea."},{"key":"ref_3","unstructured":"Socher, R., Huval, B., Manning, C.D., and Ng, A.Y. (2012, January 12\u201314). Semantic Compositionality through Recursive Matrix-Vector Spaces. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea."},{"key":"ref_4","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014, January 11). Relation Classification via Convolutional Deep Neural Network. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., and Jurafsky, D. (2009, January 2\u20137). Distant Supervision for Relation Extraction without Labeled Data. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, Singapore.","DOI":"10.3115\/1690219.1690287"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ji, G., Liu, K., He, S., and Zhao, J. (2017, January 4\u20139). Distant Supervision for Relation Extraction with Sentence-level Attention and Entity descriptions. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10953"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zeng, D., Liu, K., Chen, Y., and Zhao, J. (2015, January 17\u201321). Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1203"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, K., Feng, Y., Huang, S., and Zhao, D. (2015, January 17\u201321). Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1062"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., and Jin, Z. (2015, January 17\u201321). Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1206"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wei, F., Li, S., Ji, H., Zhou, M., and Houfeng, W. (2015, January 26\u201331). A Dependency-Based Neural Network for Relation Classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China.","DOI":"10.3115\/v1\/P15-2047"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., and Xu, B. (2016, January 7\u201312). Attention-based Bidirectional Long Short-term Memory Networks for Relation Classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, L., Cao, Z., de Melo, G., and Liu, Z. (2016, January 7\u201312). Relation Classification via Multi-Level Attention CNNs. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1123"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Riedel, S., Yao, L., and McCallum, A. (2010, January 10\u201314). Modeling Relations and Their Mentions without Labeled Text. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Dublin, Ireland.","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"ref_14","unstructured":"Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., and Weld, D.S. (2011, January 19\u201324). Knowledge-based Weak Supervision for Information Extraction of Overlapping Relations. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, Portland, OR, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, Y., Shen, S., Liu, Z., Luan, H., and Sun, M. (2016, January 7\u201312). Neural Relation Extraction with Selective Attention over Instances. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1200"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qin, P., Weiran, X., and Wang, W.Y. (2018, January 15\u201320). DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1046"},{"key":"ref_17","unstructured":"Chen, Y.J., and Hsu, J.Y.J. (2016, January 12\u201313). Chinese Relation Extraction by Multiple Instance Learning. Proceedings of the PWorkshops at the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wen, J., Sun, X., Ren, X., and Su, Q. (2018, January 1\u20136). Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-2059"},{"key":"ref_19","unstructured":"Li, Z., Ding, N., Liu, Z., Zheng, H., and Shen, Y. (August, January 28). Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge. Proceedings of the 57th Conference of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_20","unstructured":"Xu, J., Wen, J., Sun, X., and Su, Q. (2017). A discourse-level named entity recognition and relation extraction dataset for chinese literature text. arXiv."},{"key":"ref_21","unstructured":"Le, H.T., Cerisara, C., and Denis, A. (2018, January 2\u20133). Do Convolutional Networks need to be Deep for Text Classification?. Proceedings of the Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA."},{"key":"ref_22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed Representations of Words and Phrases and Their Compositionality. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_23","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013, January 5\u201310). Translating Embeddings for Modeling Multi-relational Data. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., and Zhu, X. (2015, January 25\u201330). Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of the Twenty-ninth AAAI conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"ref_25","unstructured":"Luo, B., Feng, Y., Wang, Z., Zhu, Z., Huang, S., Yan, R., and Zhao, D. (August, January 30). Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, Y., Bamman, D., and Russell, S. (2017, January 7\u201311). Adversarial Training for Relation Extraction. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1187"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, T., Huang, M., and Zhao, L. (2018, January 2\u20137). Learning Structured Representation for Text Classification via Reinforcement Learning. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12047"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Feng, J., Huang, M., Zhao, L., Yang, Y., and Zhu, X. (2018, January 2\u20137). Reinforcement Learning for Relation Classification from Noisy Data. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12063"},{"key":"ref_29","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_30","unstructured":"Zhang, D., and Wang, D. (2015). Relation classification via recurrent neural network. arXiv."},{"key":"ref_31","unstructured":"Lin, Z., Feng, M., dos Santos, C.N., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017). A Structured Self-attentive Sentence Embedding. arXiv."},{"key":"ref_32","unstructured":"Yu, M., Yin, W., Hasan, K.S., dos Santos, C., Xiang, B., and Zhou, B. (August, January 30). Improved Neural Relation Detection for Knowledge Base Question Answering. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, BC, Canada."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:53:34Z","timestamp":1760172814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,31]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["info11020079"],"URL":"https:\/\/doi.org\/10.3390\/info11020079","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2020,1,31]]}}}