@InProceedings{yin-EtAl:2018:S18-12,
  author    = {Yin, Zhongbo  and  Luo, Zhunchen  and  Wei, Luo  and  Bin, Mao  and  Changhai, Tian  and  Yuming, Ye  and  Shuai, Wu},
  title     = {IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {811--815},
  abstract  = {This paper presents our participation for sub- task1 (1.1 and 1.2) in SemEval 2018 task 7: Semantic Relation Extraction and Classifica- tion in Scientific Papers (Ga ́bor et al., 2018). We experimented on this task with two meth- ods: CNN method and traditional pipeline method. We use the context between two en- tities (included) as input information for both methods, which extremely reduce the noise effect. For the CNN method, we construct a simple convolution neural network to auto- matically learn features from raw texts with- out any manual processing. Moreover, we use the softmax function to classify the entity pair into a specific relation category. For the tradi- tional pipeline method, we use the Hackabout method as a representation which is described in section3.5. The CNN method’s result is much better than traditional pipeline method (49.1% vs. 42.3% and 71.1% vs. 54.6% ).},
  url       = {http://www.aclweb.org/anthology/S18-1129}
}