SlowGuess/ACL_2020_OCR huggingface dataset:
Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2006. Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 801-808, Sydney, Australia. Association for Computational Linguistics.
Alessandro Sperduti and Antonina Starita. 1997. Supervised neural networks for the classification of structures. IEEE Trans. Neural Networks, 8(3):714-735.
Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2008. YAGO: A large ontology from wikipedia and wordnet. J. Web Semant.
Robert Endre Tarjan. 1972. Depth-first search and linear graph algorithms. SIAM J. Comput., 1(2):146-160.
Paola Velardi, Stefano Faralli, and Roberto Navigli. 2013. OntoLearn reloaded: A graph-based algorithm for taxonomy induction. Computational Linguistics, 39(3):665-707.
Chengyu Wang, Xiaofeng He, and Aoying Zhou. 2017. A short survey on taxonomy learning from text corpora: Issues, resources and recent advances. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark. Association for Computational Linguistics.
Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Qili Zhu. 2012. Probase: a probabilistic taxonomy for text understanding. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, Scottsdale, AZ, USA, May 20-24, 2012, pages 481-492. ACM.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net.
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in Neural Information Processing Systems, NeurIPS 2018, 3-8 December 2018, Montreal, Canada, pages 4805-4815.
Yue Yu, Jie Chen, Tian Gao, and Mo Yu. 2019. DAGGNN: DAG structure learning with graph neural networks. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97, pages 7154-7163. PMLR.
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, and Eric P. Xing. 2018. Dags with NO TEARS: continuous optimization for structure learning. In Advances in Neural Information Processing Systems, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pages 9492-9503.