@article{(Open Science Index):https://publications.waset.org/pdf/10007217,
	  title     = {Causal Relation Identification Using Convolutional Neural Networks and Knowledge Based Features},
	  author    = {Tharini N. de Silva and  Xiao Zhibo and  Zhao Rui and  Mao Kezhi},
	  country	= {},
	  institution	= {},
	  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.
},
	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {11},
	  number    = {6},
	  year      = {2017},
	  pages     = {696 - 701},
	  ee        = {https://publications.waset.org/pdf/10007217},
	  url   	= {https://publications.waset.org/vol/126},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 126, 2017},
	}