Causal Relation Identification Using Convolutional Neural Networks and Knowledge Based Features
Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.
[1] R. Girju, "Automatic Detection of Causal Relations for Question Answering," in Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering-Volume 12, 2003.
[2] C. S. Khoo, S. Chan and Y. Niu, "Extracting Causal Knowledge from a Medical Database Using Graphical Patterns," in Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, 2000.
[3] D. Garcia, "COATIS, an NLP system to locate expressions of actions connected by causality links," in Knowledge Acquisition, Modeling and Mangement, 1997.
[4] R. Girju and D. Moldovan, "Text mining for causal relations," in FLAIRS Conference, 2002.
[5] E. Blanco, N. Castell and D. I. Moldovan, "Causal relation extraction," in LREC, 2008.
[6] D. Chang and K. Choi, "Causal relation extraction using cue phrase and lexical pair probabilities," in Natural Language Processing– IJCNLP 2004, 2004.
[7] Y. Kim, "Convolutional Neural Networks for Sentence Classification," in Proceedings of EMNLP, 2014.
[8] D. Zeng, K. Liu, S. Lai, G. Zhou and e. a. Jun Zhao, "Relation classification via convolutional deep neural network," in COLING, 2014.
[9] T. H. Nguyen, "Relation Extraction: Perspective from Convolutional Neural Networks".
[10] Y. Xu, R. Jia, L. Mou, G. Li, Y. Chen, Y. Lu and Z. Jin, "Improved relation classification by deep recurrent neural networks with data augmentation," 2016.
[11] S. Zhang, D. Zheng, X. Hu and a. M. Yang, "Bidirectional long short-term memory networks for relation classification," in Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation pages, 2015.
[12] Linlin Wang, Z. Cao, G. d. Melo and Z. Liu, "Relation Classification via Multi-Level Attention CNNs," 2016.
[13] T. H. Nguyen and R. Grishman, "Combining neural networks and log-linear models to improve relation extraction," 2015.
[14] L. Tan, "Pywsd: Python Implementations of Word Sense Disambiguation (WSD) Technologies," https://github.com/alvations/pywsd, 2014.
[15] S. Banerjee and T. Pedersen, "An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet," in In Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing, 2002.
[16] T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality," in Proceedings of NIPS, 2013.
[1] R. Girju, "Automatic Detection of Causal Relations for Question Answering," in Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering-Volume 12, 2003.
[2] C. S. Khoo, S. Chan and Y. Niu, "Extracting Causal Knowledge from a Medical Database Using Graphical Patterns," in Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, 2000.
[3] D. Garcia, "COATIS, an NLP system to locate expressions of actions connected by causality links," in Knowledge Acquisition, Modeling and Mangement, 1997.
[4] R. Girju and D. Moldovan, "Text mining for causal relations," in FLAIRS Conference, 2002.
[5] E. Blanco, N. Castell and D. I. Moldovan, "Causal relation extraction," in LREC, 2008.
[6] D. Chang and K. Choi, "Causal relation extraction using cue phrase and lexical pair probabilities," in Natural Language Processing– IJCNLP 2004, 2004.
[7] Y. Kim, "Convolutional Neural Networks for Sentence Classification," in Proceedings of EMNLP, 2014.
[8] D. Zeng, K. Liu, S. Lai, G. Zhou and e. a. Jun Zhao, "Relation classification via convolutional deep neural network," in COLING, 2014.
[9] T. H. Nguyen, "Relation Extraction: Perspective from Convolutional Neural Networks".
[10] Y. Xu, R. Jia, L. Mou, G. Li, Y. Chen, Y. Lu and Z. Jin, "Improved relation classification by deep recurrent neural networks with data augmentation," 2016.
[11] S. Zhang, D. Zheng, X. Hu and a. M. Yang, "Bidirectional long short-term memory networks for relation classification," in Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation pages, 2015.
[12] Linlin Wang, Z. Cao, G. d. Melo and Z. Liu, "Relation Classification via Multi-Level Attention CNNs," 2016.
[13] T. H. Nguyen and R. Grishman, "Combining neural networks and log-linear models to improve relation extraction," 2015.
[14] L. Tan, "Pywsd: Python Implementations of Word Sense Disambiguation (WSD) Technologies," https://github.com/alvations/pywsd, 2014.
[15] S. Banerjee and T. Pedersen, "An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet," in In Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing, 2002.
[16] T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality," in Proceedings of NIPS, 2013.
@article{"International Journal of Information, Control and Computer Sciences:75657", author = "Tharini N. de Silva and Xiao Zhibo and Zhao Rui and Mao Kezhi", title = "Causal Relation Identification Using Convolutional Neural Networks and Knowledge Based Features", abstract = "Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.
", keywords = "Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.", volume = "11", number = "6", pages = "696-6", }
