{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:09:55Z","timestamp":1766066995062,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["10060086","10077553"],"award-info":[{"award-number":["10060086","10077553"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2017-0-01642"],"award-info":[{"award-number":["IITP-2017-0-01642"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018R1A5A7059549"],"award-info":[{"award-number":["2018R1A5A7059549"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Classifying semantic relations between entity pairs in sentences is an important task in natural language processing (NLP). Most previous models applied to relation classification rely on high-level lexical and syntactic features obtained by NLP tools such as WordNet, the dependency parser, part-of-speech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information related to the entity, which may be the most crucial feature for relation classification. To address these issues, we propose a novel end-to-end recurrent neural model that incorporates an entity-aware attention mechanism with a latent entity typing (LET) method. Our model not only effectively utilizes entities and their latent types as features, but also builds word representations by applying self-attention based on symmetrical similarity of a sentence itself. Moreover, the model is interpretable by visualizing applied attention mechanisms. Experimental results obtained with the SemEval-2010 Task 8 dataset, which is one of the most popular relation classification tasks, demonstrate that our model outperforms existing state-of-the-art models without any high-level features.<\/jats:p>","DOI":"10.3390\/sym11060785","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T11:15:58Z","timestamp":1560424558000},"page":"785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Semantic Relation Classification via Bidirectional LSTM Networks with Entity-Aware Attention Using Latent Entity Typing"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6238-8301","authenticated-orcid":false,"given":"Joohong","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea"},{"name":"Pingpong AI Research, Scatter Lab, Seoul 06103, Korea"}]},{"given":"Sangwoo","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9042-0599","authenticated-orcid":false,"given":"Yong Suk","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., and Grishman, R. (2015, January 5). Relation extraction: Perspective from convolutional neural networks. Proceedings of the NAACL Workshop on Vector Space Modeling for Natural Language Processing, Denver, CO, USA.","DOI":"10.3115\/v1\/W15-1506"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., \u00d3 S\u00e9aghdha, D., Pad\u00f3, S., Pennacchiotti, M., Romano, L., and Szpakowicz, S. (2009, January 4). Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, Linguistics, Boulder, CO, USA.","DOI":"10.3115\/1621969.1621986"},{"key":"ref_3","unstructured":"Rink, B., and Harabagiu, S. (2010, January 15\u201316). Utd: Classifying semantic relations by combining lexical and semantic resources. Proceedings of the 5th International Workshop on Semantic Evaluation, Uppsala, Sweden."},{"key":"ref_4","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, Berlin, Germany.","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref_5","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_6","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C. (2014, January 25\u201329). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_8","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014, January 23\u201329). Relation classification via convolutional deep neural network. Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Santos, C.N.D., Xiang, B., and Zhou, B. (2015). Classifying Relations by Ranking with Convolutional Neural Networks. arXiv.","DOI":"10.3115\/v1\/P15-1061"},{"key":"ref_10","unstructured":"Zhang, D., and Wang, D. (2015). Relation classification via recurrent neural network. arXiv."},{"key":"ref_11","unstructured":"Zhang, S., Zheng, D., Hu, X., and Yang, M. (November, January 30). Bidirectional long short-term memory networks for relation classification. Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, Shanghai, China."},{"key":"ref_12","unstructured":"Xiao, M., and Liu, C. (2016, January 11\u201316). Semantic relation classification via hierarchical recurrent neural network with attention. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."},{"key":"ref_13","unstructured":"Huang, X. (2016, January 11\u201316). Attention-based convolutional neural network for semantic relation extraction. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."},{"key":"ref_14","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_15","unstructured":"Xu, Y., Jia, R., Mou, L., Li, G., Chen, Y., Lu, Y., and Jin, Z. (2016). Improved relation classification by deep recurrent neural networks with data augmentation. arXiv."},{"key":"ref_16","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_18","unstructured":"Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., and Zhang, C. (2017). Disan: Directional self-attention network for rnn\/cnn-free language understanding. arXiv."},{"key":"ref_19","unstructured":"Tan, Z., Wang, M., Xie, J., Chen, Y., and Shi, X. (2017). Deep semantic role labeling with self-attention. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C.D. (2017, January 7\u201311). Position-aware attention and supervised data improve slot filling. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1004"},{"key":"ref_23","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","unstructured":"Sang, E.F., and De Meulder, F. (2003). Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yoon, S., Shin, J., and Jung, K. (2018, January 1\u20136). Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-1142"},{"key":"ref_26","unstructured":"Zeiler, M.D. (2012). ADADELTA: An adaptive learning rate method. arXiv."},{"key":"ref_27","unstructured":"Ng, A.Y. (2004, January 4\u20138). Feature selection, L 1 vs. L 2 regularization, and rotational invariance. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada."},{"key":"ref_28","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_29","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv."},{"key":"ref_30","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy."},{"key":"ref_31","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_32","unstructured":"Yu, M., Gormley, M., and Dredze, M. (2014, January 12). Factor-based compositional embedding models. Proceedings of the NIPS Workshop on Learning Semantics, Montreal, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wei, F., Li, S., Ji, H., Zhou, M., and Wang, H. (2015). A dependency-based neural network for relation classification. arXiv.","DOI":"10.3115\/v1\/P15-2047"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, K., Feng, Y., Huang, S., and Zhao, D. (2015). Semantic relation classification via convolutional neural networks with simple negative sampling. arXiv.","DOI":"10.18653\/v1\/D15-1062"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 12\u201317). Hierarchical attention networks for document classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, D., and Manning, C. (, January 25\u201329). A fast and accurate dependency parser using neural networks. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1082"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/785\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:58:06Z","timestamp":1760187486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["sym11060785"],"URL":"https:\/\/doi.org\/10.3390\/sym11060785","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,6,13]]}}}