# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Pipeline Extraction
#### 2.1.1 CNNs  
[PERSON] et al. applied deep convolutional neural networks to perform relationship extraction, which is feature extraction by a neural network that avoids manual feature extraction and realizes end-to-end relationship extraction [13]. Solid relation extraction using a simple CNN model consisting of input, convolution, pooling, and softmax layers was performed by [PERSON] et al. [14]. In order to reduce the application limitations of CNNs, [PERSON] et al. introduced a fast algorithm for CNNs based on [PERSON]'s minimum filtering algorithm to improve the implementation efficiency of the convolutional neural network algorithm [15]. [PERSON] et al. conducted an in-depth analysis of the CNN and considered that CNN has good prospects in the application of images, videos, voice, and text recognition, but the CNNalgorithm still needs to be optimized. At the same time, they discussed the improvement of CNN in the aspects of layer design, activation function, loss function, regularization, optimization, and fast calculation. [PERSON] also proposed a language CNN model for a statistical language modeling task [16; 17]. [PERSON] et al. proposed a new convolution attention network (CAN) for chemical-disease relationship (CDR) extraction, which is designed to perform convolution operations on the shortest dependency path (SDP) between chemical and disease pairs to produce deep semantic dependence features. An attention mechanism is then employed to capture weighted semantic-dependent representations related to knowledge representations learned from the knowledge database. It mainly consists of a representation layer, a convolutional layer, a knowledge-based attention layer, and a softmax layer. The experimental results show that dependent information and prior knowledge were effective for CDR extraction and that prior knowledge was able to significantly improve performance, but the method only considered intra- and inter-sentence CDR extraction and ignored the uniformity of documents [18]. [PERSON] et al. proposed a knowledge-oriented convolutional neural network (K-CNN) designed to perform causality extraction. The K-CNN architecture contains two collaborative channels (as shown in Figure 2), including knowledge-oriented channels and data-oriented channels. These models combine both prior human knowledge and the information derived from data in a complementary manner to extract causality from natural language texts. Filter selection and clustering techniques have also been proposed to reduce dimensionality and improve the performance of the K-CNN. To the best of our knowledge, the automatic selection of target entities identified by causality as well as the more efficient extraction of complex causality has not yet been investigated in the relevant literature. Future attempts can consider the application of K-CNN models to relationship extraction tasks other than causality and explore potential applications of K-CNN in other domain-specific tasks [19]. A dual convolutional neural network (dual CNN) model based on a knowledge-attention mechanism was proposed by [PERSON] et al. The model includes an input layer, a convolutional layer, a pooling layer, a merging layer, and a fully connected layer (as shown in Figure 3). The model inserts word embeddings and supervised information from the knowledge database into the CNN, performs convolutions and pools, and combines a knowledge database and a CNN structure in the fully connected layer [20]. [PERSON] et al. proposed a relationship extraction method for domain knowledge graph construction.