# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning  
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Entity relationship extraction is a basic task in the field of NLP. The task is defined as specifying the appropriate relationship types for entity pairs in sentences. Accurate relationship extraction results facilitate accurate text interpretation, discourse processing, and higher-level natural language processing systems. Various relationships extracted from text contribute to the construction of knowledge graphs and downstream tasks that require a relational understanding of text, such as intelligent question answering, bio-medical knowledge discovery, and dialog systems.  
Compared with traditional methods, the advantage of deep learning techniques lies in the ability to learn an appropriate feature representation for the current problem in an end-to-end manner, replacing the hand-crafted features of the past [8]. Early rule-based relation extraction methods manually constructed language rules to represent the semantic features of sentences, realizing entity recognition and relation-type discrimination. Rule-based entity relationship extraction can be divided into two categories, including trigger word- and dependency-based relationship extraction methods. Manual construction rules can be tailored for specific fields with high accuracy and are easy to implement on small-scale datasets. However, based on the rules of different relationship extraction methods, which depend on domain knowledge and artificial feature extraction, the specific rules of different fields require experts to construct the learning models. Considering all possible rules is difficult and requires considerable time and energy. The performance of relationship extraction methods depends on the quality and scale of the rules, and exhibits  
Figure 1: Overall architecture of IE based on DL.  
the characteristics of poor field-migration performance and low recall. Subsequently, a machine learning-based relationship extraction method was proposed. Machine learning-based relationship extraction methods use feature engineering and annotation data to obtain better performance, effectively reducing the dependence on linguistics and domain knowledge, and have shown strong domain transfer abilities. However, eigenvector- and kernel-function-based methods can produce error propagation through learning pipelines, which greatly limits the performance of the model [9].  
By avoiding tedious manual feature extraction and improving error propagation in feature extraction, relationship extraction methods based on deep learning have become a focus of recent research [10]. Compared with traditional machine learning based on statistical models, deep learning-based methods use word vectors in text as input to achieve end-to-end feature extraction through neural networks, and no longer rely on manually defined features [11]. At present, entity relationship extraction based on DL can be roughly divided into supervised and distant supervised learning-based relationship extractions according to the training methods, in which relationship extraction based on supervised learning includes pipeline extraction and joint extraction.