References(37)
[1]
Y. J. Zhang, P. F. Li, and G. D. Zhou, Classifying temporal relations between events by deep biLSTM, in Proc. 2018 Int. Conf. on Asian Language Processing, Bandung, Indonesia, 2018, pp. 267-272.
[2]
L. Derczynski and R. Gaizauskas, Using signals to improve automatic classification of temporal relations, arXiv preprint arXiv: 1203.5055, 2012.
[3]
G. A. Miller, Wordnet: A lexical database for English, Commun. ACM, vol. 38, no. 11, pp. 39-41, 1995.
[4]
I. Mani, M. Verhagen, B. Wellner, C. M. Lee, and J. Pustejovsky, Machine learning of temporal relations, in Proc. 21st Int. Conf. on Computational Linguistics and the 44th Annu. Meeting of the Association for Computational Linguistics, Sydney, Australia, 2006, pp. 753-760.
[5]
F. Cheng and Y. Miyao, Classifying temporal relations by bidirectional LSTM over dependency paths, in Proc. 55th Annu. Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 1-6.
[6]
Y. L. Meng, A. Rumshisky, and A. Romanov, Temporal information extraction for question answering using syntactic dependencies in an LSTM-based architecture, in Proc. 2017 Conf. on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017, pp. 887-896.
[7]
P. K. Choubey and R. H. Huang, A sequential model for classifying temporal relations between intra-sentence events, in Proc. 2017 Conf. on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017, pp. 1796-1802.
[8]
C. H. Zhang, M. Zhou, X. Han, Z. Hu, and Y. Ji, Knowledge graph embedding for hyper-relational data, Tsinghua Science and Technology, vol. 22, no. 2, pp. 185-197, 2017.
[9]
M. Verhagen, R. Gaizauskas, F. Schilder, M. Hepple, G. Katz, and J. Pustejovsky, Semeval-2007 task 15: Tempeval temporal relation identification, in Proc. 4th Int. Workshop on Semantic Evaluations, Prague, Czech Republic, 2007, pp. 75-80.
[10]
M. Verhagen, R. Saurí, T. Caselli, and J. Pustejovsky, Semeval-2010 task 13: Tempeval-2, in Proc. 5th Int. Workshop on Semantic Evaluation, Uppsala, Sweden, 2010, pp. 57-62.
[11]
N. UzZaman, H. Llorens, L. Derczynski, J. Allen, M. Verhagen, and J. Pustejovsky, SemEval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations, in Proc. 2nd Joint Conf. on Lexical and Computational Semantics (*SEM), Volume 2: Proc. 7th Int. Workshop on Semantic Evaluation (SemEval 2013), Atlanta, GA, USA, 2013, pp. 1-9.
[12]
N. Chambers, S. Wang, and D. Jurafsky, Classifying temporal relations between events, in Proc. 45th Annu. Meeting of the Association for Computational Linguistics Companion Volume Proc. Demo and Poster Sessions, Prague, Czech Republic, 2007, pp. 173-176.
[13]
A. Leeuwenberg and M. F. Moens, Structured learning for temporal relation extraction from clinical records, in Proc. 15th Conf. of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Valencia, Spain, 2017, pp. 1150-1158.
[14]
N. Chambers, Navytime: Event and Time Ordering from Raw Text. Annapolis, MD, USA: Naval Academy, 2013.
[15]
M. Miwa and M. Bansal, End-to-end relation extraction using LSTMs on sequences and tree structures, in Proc. 54th Annu. Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016, 1105-1116.
[16]
X. Han, B. Y. Li, and Z. R. Wang, An attention-based neural framework for uncertainty identification on social media texts, Tsinghua Science and Technology, vol. 25, no. 1, pp. 117-126, 2020.
[17]
R. Y. Xin, J. Zhang, and Y. T. Shao, Complex network classification with convolutional neural network, Tsinghua Science and Technology, vol. 25, no. 4, pp. 447-457, 2020.
[18]
J. Tourille, O. Ferret, A. Névéol, and X. Tannier, Neural architecture for temporal relation extraction: A bi-LSTM approach for detecting narrative containers, in Proc 55th Annu. Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 224-230.
[19]
N. Laokulrat, M. Miwa, Y. Tsuruoka, and T. Chikayama, Uttime: Temporal relation classification using deep syntactic features, in Proc. 2nd Joint Conf. on Lexical and Computational Semantics (*SEM), Volume 2: Proc. 7th International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, GA, USA, 2013, pp. 88-92.
[20]
M. Gori, G. Monfardini, and F. Scarselli, A new model for learning in graph domains, in Proc. 2005 IEEE Int. Joint Conf. on Neural Networks, Montreal, Canada, 2005, pp. 729-734.
[21]
M. Henaff, J. Bruna, and Y. LeCun, Deep convolutional networks on graph-structured data, arXiv preprint arXiv: 1506.05163, 2015.
[22]
M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, in Proc. 30th Int. Conf. on Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 3844-3852.
[23]
T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv: 1609.02907, 2016.
[24]
D. Marcheggiani and I. Titov, Encoding sentences with graph convolutional networks for semantic role labeling, in Proc. 2017 Conf. on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017, pp. 1506-1515.
[25]
Y. H. Zhang, P. Qi, and C. D. Manning, Graph convolution over pruned dependency trees improves relation extraction, in Proc. 2018 Conf. on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, pp. 2205-2215.
[26]
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, Graph attention networks, arXiv preprint arXiv: 1710.10903, 2017.
[27]
D. Busbridge, D. Sherburn, P. Cavallo, and N. Y. Hammerla, Relational graph attention networks, arXiv preprint arXiv: 1904.05811, 2019.
[28]
Y. Xu, L. L. Mou, G. Li, Y. C. Chen, H. Peng, and Z. Jin, Classifying relations via long short term memory networks along shortest dependency paths, in Proc. 2015 Conf. on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, pp. 1785-1794.
[29]
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, in Proc. 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, Minneapolis, Minnesota, 2019, pp. 4171-4186.
[30]
X. Liu, Z. C. Luo, and H. Y. Huang, Jointly multiple events extraction via attention-based graph information aggregation, in Proc. 2018 Conf. on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, pp. 1247-1256.
[31]
P. Zhou, Z. Y. Qi, S. C. Zheng, J. M. Xu, H. Y. Bao, and B. Xu, Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling, Proc. 26th Int. Conf. on Computational Linguistics: Technical Papers, Osaka, Japan, 2016, pp. 3485-3495.
[32]
H. Zhu, Y. K. Lin, Z. Y. Liu, J. Fu, T. S. Chua, and M. S. Sun, Graph neural networks with generated parameters for relation extraction, in Proc. 57th Annu. Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 1331-1339.
[33]
A. Santoro, D. Raposo, D. G. T. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap, A simple neural network module for relational reasoning. in Proc. 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 4974-4983.
[34]
N. Chambers, T. C. B. McDowell, and S. Bethard, Dense event ordering with a multi-pass architecture, Trans. Assoc Comput Linguist, vol. 2, pp. 273-284, 2014.
[35]
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv: 1412.6980, 2014.
[36]
X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, in Proc. 14th Int. Conf. on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2011, pp. 315-323.
[37]
Y. L. Meng and A. Rumshisky, Context-aware neural model for temporal information extraction, in Proc. 56th Annu. Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, pp. 527-536.