# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Pipeline Extraction
#### 2.1.4 Entity Relationship Extraction Based on Mixed Models  
Relational extraction based on a mixed model refers to the use of different models for data processing according to the model characteristics, data characteristics, or different stages of natural language processing to achieve optimal results. A hybrid model combining RNN and CNN for biomedical relation extraction was proposed by [PERSON] et al. Experimental results show the complementary advantages of RNN and CNN in bio-medical relationship extraction, and the combination of RNN and CNN can effectively improve the performance of bio-medical relationship extraction [36]. [PERSON] et al. proposed an integrated model including a support vector machine (SVM), a CNN, and an RNN, which was able to effectively detect chemical-protein relationships in the bio-medical literature and achieved the highest performance in the 2017 Challenge Task [37]. Combining the social and domain characteristics of software knowledge-community texts, entity perception information, and dependency structure information, [PERSON] et al. proposed a model called ED-SEE, which extracts software knowledge entity relationships from unstructured user-generated content. It captures the context, semantic representation, and syntactic dependent representation of a sentence sequence using the bi-directional gating recursive unit (BiGRU) model and the GCN model, respectively. To obtain more syntactic dependence information, a weighted graph convolutional network based on [PERSON]'s cooling law was constructed by computing the syntactic dependencies between the nodes. Combining the entity type, relative entity location, and information of the entity mentioned, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic-dependent information of sentence sequences to improve the prediction performance of the software knowledge entity relationship classification [10].  
A sentence with overlapping relationships generates multiple conceptual instances based on different target-entity pairs. These same instances are referred to as overlapping instances. Accordingly, sentences with only one pair of target entities are referred to as normal instances. Even in the field of natural language processing, relationship extraction of sentences with overlapping relationships is a research-based difficulty. [PERSON] et al. proposed a model with BERT representation, Gaussian probability distribution operation, and external knowledge acquisition, and experimentally found that the Gaussian probability distribution plays an important role in promoting overlapping instance extraction, outperforming location features and entity attention mechanisms in bio-medical extraction [38]. [PERSON] et al. proposed a method to extract positive instances of relationships from various web sources, which introduces a human-in-the-loop component in the extraction pipeline [39]. [PERSON] et al. proposed the fact extractor, a complete NLP pipeline for reading the input text corpus and generating machine-readable statements, applying framework semantic linguistics theory, rather than binary techniques, to perform n-ary relation extraction [40].