# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Entity Relational Extraction Based on Distant Supervision  
Existing neural-network-based methods have achieved great success. However, most supervised relational extraction models require large amounts of training data, which are expensive to obtain. To overcome this shortcoming, remote supervision has been introduced to construct large-scale data sets automatically. [PERSON] et al. proposed a remote supervision-based model that requires no annotation and can be used to represent the context of a sentence and the relationships mentioned around it, thus, enabling paragraph-level relationship classification [60]. [PERSON] et al. proposed a new segmented convolutional neural network (PCNNs) model using multi-instance learning for remote supervised relationship extraction [61]. [PERSON] et al. proposed a note of knowledge representation designed to utilize the knowledge information in a knowledge database to reduce the impact of noisy data on remote supervised relationship extraction. The distributed representation of the knowledge database is pre-trained using the knowledge representation learning (KRL) model, and then included in the relationship extraction to learn the sentence-level attention weights. Attention is focused on valid data by utilizing background information in the knowledge base [62]. In this approach, the quality of knowledge embedding is crucial in this model. Most LSTM-based models only learn word representations and cannot represent semantic blocks. Many studies have focused on single-relation extraction and have ignored multi-relationship extraction. [PERSON] et al. divided sentences into solid and nonsolid blocks and proposed block graph LSTM networks to simultaneously learn the representations of entities and relationships. The block LSTM, along with the graph LSTM, was integrated into the LSTM network for multi-relationship extraction [63]. A network relation extraction method based on remote supervised classes strategically selects seeds for training, extracts relation mentions across sentence boundaries, and integrates relation mentions to predict relations of knowledge-based groups [64]. [PERSON] et al. proposed a remote relationship extraction model trained to identify distant relationships using bootstrapped noise data, combining the article chapter structure, graphical information, and attention mechanisms proposed by [PERSON] et al. [65]. [PERSON] et al. proposed a novel GCN-based local-to-global graph convolutional network model called LGGCN, which encodes sentences in packets from a local to global perspective to improve the performance of remote-supervised relationship extraction [66]. A new remote supervised relationship extraction (DSRE) framework based on an adaptive dependent path and additional knowledge graph supervision called A\({}^{2}\)DSRE was proposed by [PERSON] et al. An advanced graph neural network, GeniePath, was introduced as an adaptive path layer in DSRE, and TransE was used to obtain the relation embedding from the knowledge graph as additional supervision [67]. [PERSON] et al. proposed a new framework for a remote-supervised relationship extraction task using a knowledge-attention-guided graph convolutional network, which consisted of two modules: a sentence embedding module and a multi-instance selection module [68]. The sentence embedding module uses the relational indicators in the lexical resources as prior knowledge to guide sentence embedding, which consists of a word-level knowledge attention layer and a graph convolutional layer. It uses relationship indicators obtained from the framework network to effectively capture information-rich language clues and generate more expressive sentence features. The multi-instance selection module uses the structure and semantic information in the knowledge graph as prior knowledge to guide the selection of multiple effective sentences, including knowledge graph embeddings and sentence-level knowledge attention layers.