Opera-oriented character relations extraction for role interaction and behaviour Understanding: a deep learning approach

References

• Beamer, B., A. Rozovskaya, and R. Girju. 2008. “Automatic Semantic Relation Extraction with Multiple Boundary Generation.” AAAI’08, Proceedings of the twenty-third AAAI conference on artificial intelligence, Chicago, Illinois, USA, July, 824–829.
   Google Scholar
• Bollegala, D. T., Y. Matsuo, and M. Ishizuka. 2010. “Relational Duality: Unsupervised Extraction of Semantic Relations Between Entities on the Web.” WWW’10, Proceedings of the international conference on world wide web, Raleigh, North Carolina, USA, April, 151–160.
   Google Scholar
• Bollegala, D., Y. Matsuo, and M. Ishizuka. 2011. “Relation Adaptation: Learning to Extract Novel Relations with Minimum Supervision.” IJCAI’11, Proceedings of the international joint conference on artificial intelligence, Barcelona, Catalonia, Spain, July, 2205–2210.
   Google Scholar
• Bundschus, M., A. Bauer-Mehren, V. Tresp, L. Furlong, and H. P. Kriegel. 2013. “Digging for Knowledge with Information Extraction: A Case Study on Human Gene-Disease Associations.” CIKM’10, Proceedings of the ACM conference on information and knowledge management, Toronto, Ontario, Canada, October, 1845–1848.
   Google Scholar
• Chan, Y. S., and D. Roth. 2010. “Exploiting Background Knowledge for Relation Extraction.” COLING’10, Proceedings of the 23rd international conference on computational linguistics, 152–160.
   Google Scholar
• Che, W., Z. Li, and T. Liu. 2010. “LTP: A Chinese Language Technology Platform.” COLING’10, Proceedings of the 23rd international conference on computational linguistics, 13–16.
   Google Scholar
• Chen, Y., Q. Zheng, and P. Chen. 2015. “Feature Assembly Method for Extracting Relations in Chinese.” Artificial Intelligence 228 (C): 179–194. doi: 10.1016/j.artint.2015.07.003
   Google Scholar
• Chung, J., C. Gulcehre, K. H. Cho, et al. 2014. “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.” arXiv preprint, arXiv:1412.3555 [cs.NE].
   Google Scholar
• Cortes, C., and V. Vapnik. 1995. “Support-vector Networks.” Machine Learning 20 (3): 273–297.
   Web of Science ®Google Scholar
• Cummins, R., M. Zhang, and T. Briscoe. 2016. “Constrained Multi-Task Learning for Automated Essay Scoring.” ACL’16, Proceedings of the meeting of the association for computational linguistics, 789–799.
   Google Scholar
• Gavaldà, R., P. W. Keller, J. Pineau, and D. Precup. 2006. “PAC-Learning of Markov Models with Hidden State.” ECML’06, Proceedings of the European conference on machine learning, Berlin, Germany, September, 150–161.
   Google Scholar
• Goldberg, Y. 2016. “A Primer on Neural Network Models for Natural Language Processing.” Journal of Artificial Intelligence Research 57: 345–420. doi: 10.1613/jair.4992
   Web of Science ®Google Scholar
• Harabagiu, S. M., C. A. Bejan, and P. Morarescu. 2005. “Shallow Semantics for Relation Extraction.” IJCAI’05, Proceedings of the nineteenth international joint conference on artificial intelligence, Edinburgh, Scotland, Uk, July 30-August, 1061–1066.
   Google Scholar
• Huang, Z. H., W. Xu, and K. Yu. 2015. “Bidirectional LSTM-CRF models for sequence tagging.” arXiv preprint, arXiv:1508.01991 [cs.CL].
   Google Scholar
• Kalchbrenner, N., E. Grefenstette, and P. Blunsom. 2014. “A Convolutional Neural Network for Modelling Sentences.” ACL’14, Proceedings of the 52nd annual meeting of the association for computational linguistics, 655–665.
   Google Scholar
• Kirschnick, J., H. Hemsen, and V. Markl. 2016. “JEDI: Joint Entity and Relation Detection using Type Inference.” ACL’16, Proceedings of the annual meeting of the association for computational linguistics, 61–66.
   Google Scholar
• Kluegl, P., M. Toepfer, F. Lemmerich, A. Hotho, and F. Puppe. 2012. “Collective Information Extraction with Context-Specific Consistencies.” ECML’12, Proceedings of the European conference on machine learning, 728–743.
   Google Scholar
• Lample, G., M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer. 2016. “Neural architectures for named entity recognition.” NAACL’16, Proceedings of the 15th conference of the North American chapter of the association for computational linguistics: Human language technologies, 260–270.
   Google Scholar
• Liang, Z., C. Yuan, B. Leng, and X. Wang. 2015. “Recognition of Person Relation Indicated by Predicates.” NLPCC’15, Proceedings of the 4th CCF Conference on natural language processing & Chinese computing, 313–324.
   Google Scholar
• Lin, C. X., B. Zhao, T. Weninger, J. Han, and B. Liu. 2010. “Entity Relation Discovery from Web Tables and Links.” WWW’10, Proceedings of the international conference on world wide web, 1145–1146.
   Google Scholar
• Luan, M., I. W. Tsang, K. Ming, A. Chai, and L. C. Hai. 2014. “Robust Domain Adaptation for Relation Extraction via Clustering Consistency.” ACL’14, Proceedings of the 52nd annual meeting of the association for computational linguistics, 807–817.
   Google Scholar
• Luo, L., Z. Yang, P. Yang, Yin Zhang, Lei Wang, Hongfei Lin, Jian Wang, and Jonathan Wren. 2018. “An Attention-Based BiLSTM-CRF Approach to Document-Level Chemical Named Entity Recognition.” Bioinformatics (oxford, England) 34 (8): 1381–1388. doi: 10.1093/bioinformatics/btx761
   PubMed Web of Science ®Google Scholar
• MacCartney, B., M. Galley, and C. D. Manning. 2008. “A Phrase-based Alignment Model for Natural Language Inference.” EMNLP’08, Proceedings of the conference on empirical methods in natural language processing, 802–811.
   Google Scholar
• Matsuo, Y., J. Mori, M. Hamasaki, K. Ishida, H. Takeda, H. Takeda, et al. 2006. “POLYPHONET: An Advanced Social Network Extraction System from the Web.” WWW’06, Proceedings of the international conference on world wide web, 397–406.
   Google Scholar
• McCallum, A., D. Freitag, and F. C. N. Pereira. 2000. “Maximum Entropy Markov Models for Information Extraction and Segmentation.” ICML’00, Proceedings of the international conference on machine learning, 591–598.
   Google Scholar
• Mikolov, T., K. Chen, G. Corrado, et al. 2013. “Efficient Estimation of Word Representations in Vector Space.” arXiv preprint, arXiv:1301.3781 [cs.CL].
   Google Scholar
• Peng, C., J. Gu, and L. Qian. 2012. “Research on Tree Kernel-Based Personal Relation Extraction.” Communications in Computer & Information Science 333: 225–236. doi: 10.1007/978-3-642-34456-5_21
   Google Scholar
• Reichartz, F., H. Korte, and G. Paass. 2010. “Semantic Relation Extraction with Kernels Over Typed Dependency Trees.” KDD’10, Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data, 773–782.
   Google Scholar
• Shen, W., J. Wang, P. Luo, M. Wang, and C. Yao. 2011. “REACTOR: A Framework for Semantic Relation Extraction and Tagging Over Enterprise Data.” WWW’11, Proceedings of the international conference on world wide web, Hyderabad, India, March 28 – April, 121–122.
   Google Scholar
• Suchanek, F. M., G. Ifrim, and G. Weikum. 2006. “Combining Linguistic and Statistical Analysis to Extract Relations from Web Documents.” SIGKDD’06, Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, 712–717.
   Google Scholar
• Tandon, N., C. Hariman, J. Urbani, A. Rohrbach, M. Rohrbach, and G. Weikum. 2016. “Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags.” AAAI’16, Proceedings of the thirtieth AAAI conference on artificial intelligence, 243–250.
   Google Scholar
• Wang, L., Z. Cao, G. D. Melo, and Z. Liu. 2016. “Relation Classification via Multi-Level Attention CNNs.” ACL’16, Proceedings of the annual meeting of the association for computational linguistics, 1298–1307.
   Google Scholar
• Wang, C., J. Fan, A. Kalyanpur, and D. Gondek. 2011. “Relation Extraction with Relation Topics.” EMNLP’11, Proceedings of the international conference on empirical methods in natural language processing, 1426–1436.
   Google Scholar
• Yap, W., and T. Baldwin. 2009. “Experiments on Pattern-Based Relation Learning.” CIKM’09, Proceedings of the 18th ACM conference on information and knowledge management, 1657–1660.
   Google Scholar
• Zeng, D., K. Liu, S. Lai, et al. 2014. “Relation Classification via Convolutional Deep Neural Network.” COLING’14, Proceedings of the 25th international conference on computational linguistics, 2335–2344.
   Google Scholar
• Zhang, C., R. Hoffmann, and D. S. Weld. 2012. “Ontological Smoothing for Relation Extraction with Minimal Supervision.” AAAI’12, Proceedings of the twenty-sixth AAAI conference on artificial intelligence, 157–163.
   Google Scholar
• Zhao, X., P. Jin, and L. Yue. 2010. “Automatic Temporal Expression Normalization with Reference Time Dynamic-Choosing.” COLING’10, Proceedings of the 23rd international conference on computational linguistics, 1498–1506.
   Google Scholar
• Zhu, J., Z. Nie, X. Liu, B. Zhang, and J. R. Wen. 2009. “StatSnowball: A Statistical Approach to Extracting Entity Relationships.” WWW’09, Proceedings of the international conference on world wide web, Madrid, Spain, April, 101–110.
   Google Scholar
• Zhu, C., X. Qiu, X. Chen, et al. 2015. “A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network.” ACL’15, Proceedings of the 53rd annual meeting of the association for computational linguistics, 1159–1168.
   Google Scholar