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We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features.
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proceedings of the Eighteenth International Conference on Machine Learning. pp. 282–289 (2001)
2001
Earlier work this paper cites.
Roth, D., Yih, W.t.: A Linear Programming Formulation for Global Inference in Natural Language Tasks. In: Proceedings of the 8th Conference on Computational Natural Language Learning. pp. 1–8 (2004)
2004
Earlier work this paper cites.
Bach, N., Badaskar, S.: A Review of Relation Extraction. Tech. rep., Carnegie Mellon University (2007)
2007
Earlier work this paper cites.
Roth, D., tau Yih, W.: Global Inference for Entity and Relation Identification via a Linear Programming Formulation. In: Introduction to Statistical Relational Learning. MIT Press (2007)
2007
Earlier work this paper cites.
Ratinov, L., Roth, D.: Design Challenges and Misconceptions in Named Entity Recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning. pp. 147–155 (2009)
2009
Earlier work this paper cites.
Kate, R.J., Mooney, R.J.: Joint Entity and Relation Extraction Using Card-pyramid Parsing. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning. pp. 203–212 (2010)
2010
Earlier work this paper cites.
Jiang, J.: Information Extraction from Text. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 11–41 (2012)
2012
Earlier work this paper cites.
Thomas, P., Starlinger, J., Vowinkel, A., Arzt, S., Leser, U.: GeneView: A comprehensive semantic search engine for PubMed. Nucleic Acids Research 40
2012
Earlier work this paper cites.
Blanco, R., Cambazoglu, B.B., Mika, P., Torzec, N.: Entity Recommendations in Web Search. In: Proceedings of the 12th International Semantic Web Conference - Part II. pp. 33–48 (2013)
2013
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. CoRR abs/1412.6980
2014
Earlier work this paper cites.
Li, Q., Ji, H.: Incremental Joint Extraction of Entity Mentions and Relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 402–412 (2014)
2014
Earlier work this paper cites.
Miwa, M., Sasaki, Y.: Modeling Joint Entity and Relation Extraction with Table Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pp. 1858–1869 (2014)
2014
Cited alongside, same era.
Pennington, J., Socher, R., Manning, C.: Glove: Global Vectors for Word Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pp. 1532–1543 (2014)
2014
Cited alongside, same era.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15
2014
Cited alongside, same era.
Ballesteros, M., Dyer, C., Smith, N.A.: Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. pp. 349–359 (2015)
2015
Cited alongside, same era.
Dozat, T., Manning, C.D.: Deep Biaffine Attention for Neural Dependency Parsing. In: Proceedings of the 5th International Conference on Learning Representations (2017)
2017
Later among the works it cites.
Dozat, T., Qi, P., Manning, C.D.: Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task. In: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. pp. 20–30 (2017)
2017
Later among the works it cites.
Katiyar, A., Cardie, C.: Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 917–928 (2017)
2017
Later among the works it cites.
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2015
Cited alongside, same era.
Gupta, P., Schütze, H., Andrassy, B.: Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. pp. 2537–2547 (2016)
2016
Cited alongside, same era.
Kiperwasser, E., Goldberg, Y.: Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations. Transactions of ACL 4
2016
Cited alongside, same era.
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural Architectures for Named Entity Recognition. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 260–270 (2016)
2016
Cited alongside, same era.
Miwa, M., Bansal, M.: End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1105–1116 (2016)
2016
Cited alongside, same era.
Nguyen, T.H., Grishman, R.: Combining Neural Networks and Log-linear Models to Improve Relation Extraction. In: Proceedings of IJCAI Workshop on Deep Learning for Artificial Intelligence (2016)
2016
Cited alongside, same era.
Adel, H., Schütze, H.: Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 1723–1729 (2017)
2017
Cited alongside, same era.
2017
Later among the works it cites.
Pawar, S., Bhattacharyya, P., Palshikar, G.: End-to-end relation extraction using neural networks and markov logic networks. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. pp. 818–827 (2017)
2017
Later among the works it cites.
Zhang, M., Zhang, Y., Fu, G.: End-to-End Neural Relation Extraction with Global Optimization. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 1730–1740 (2017)
2017
Later among the works it cites.
Zheng, S., Hao, Y., Lu, D., Bao, H., Xu, J., Hao, H., Xu, B.: Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257
2017
Later among the works it cites.
2017
Later among the works it cites.
Bekoulis, G., Deleu, J., Demeester, T., Develder, C.: Adversarial training for multi-context joint entity and relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 2830–2836 (2018)
2018
Closest in time.
Bekoulis, G., Deleu, J., Demeester, T., Develder, C.: Joint entity recognition and relation extraction as a multi-head selection problem. Expert Systems with Applications 114
2018
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Wang, S., Zhang, Y., Che, W., Liu, T.: Joint Extraction of Entities and Relations Based on a Novel Graph Scheme. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. pp. 4461–4467 (2018)
2018
Closest in time.