This is a list of my Research Proposal about Design and Visualization of Financial Event Extraction System.
[1] Liu, X., Luo, Z., and Huang, H. Jointly multiple events extraction via attention based graph information aggregation. arXiv preprint arXiv:1809.09078 (2019).
[2] Chen, Y., Xu, L., Liu, K., Zeng, D., and Zhao, J. Event extraction via dynamic multi-pooling convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2015), pp. 167–176.
[3] Patwardhan, S., and Riloff, E. Effective information extraction with semantic afinity patterns and relevant regions. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (2007), pp. 717–727.
[4] Li, Q., Ji, H., and Huang, L. Joint event extraction via structured prediction with global features. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2013), pp. 73–82.
[5] McClosky, D., Surdeanu, M., and Manning, C. D. Event extraction as dependency parsing. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (2011), Association for Computational Linguistics, pp. 1626–1635.
[6] Ferguson, J., Lockard, C., Weld, D. S., and Hajishirzi, H. Semi-supervised event extraction with paraphrase clusters. arXiv preprint arXiv:1808.08622 (2018).
[7] Chambers, N., and Jurafsky, D. Template-based information extraction without the templates. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (2011), Association for Computational Linguistics, pp. 976–986.
[7] Chambers, N., and Jurafsky, D. Template-based information extraction without the templates. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (2011), Association for Computational Linguistics, pp. 976–986.
[8] Kim, J.-T., and Moldovan, D. I. Acquisition of linguistic patterns for knowledge based information extraction. IEEE transactions on knowledge and data engineering 7, 5 (1995), 713–724.
[9] Mamani G E H , Setio A A A , Ginneken B V , et al. Organ detection in thorax abdomen CT using multi-label convolutional neural networks[C]// Spie Medical Imaging. 2017.
[10] Yang X, Wang X, Zhang Y, et al. Distant Supervision for Relation Extraction via Group Selection[M]// Neural Information Processing. 2015.
[11] Shingo Takamatsu, Issei Sato, Hiroshi Nakagawa. Reducing wrong labels in distant supervision for relation extraction[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1. 2012.
[12] Ferguson, J., Lockard, C., Weld, D. S., and Hajishirzi, H. Semi-supervised event extraction with paraphrase clusters. arXiv preprint arXiv:1808.08622 (2018).
[13] Phil Blunsom, Trevor Cohn. Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing[C]// Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, 9-11 October 2010, MIT Stata Center, Massachusetts, USA, A meeting of SIGDAT, a Special Interest Group of the ACL. Association for Computational Linguistics, 2010.
[14] Kiperwasser, Eliyahu, Goldberg, Yoav. Easy-First Dependency Parsing with Hierarchical Tree LSTMs[J]. Transactions of the Association for Computational Linguistics, 4:445-461.