# A Survey of Information Extraction Based on Deep Learning
###### Abstract  
As a core task and an important link in the fields of natural language understanding and information retrieval, information extraction (IE) can structure and semanticize unstructured multi-modal information. In recent years, deep learning (DL) has attracted considerable research attention to IE tasks. Deep learning-based entity relation extraction techniques have gradually surpassed traditional feature- and kernel-function-based methods in terms of the depth of feature extraction and model accuracy. In this paper, we explain the basic concepts of IE and DL, primarily expounding on the research progress and achievements of DL technologies in the field of IE. At the level of IE tasks, it is expounded from entity relationship extraction, event extraction, and multi-modal information extraction three aspects, and creates a comparative analysis of various extraction techniques. We also summarize the prospects and development trends in DL in the field of IE as well as difficulties requiring further study. It is believed that research can be carried out in the direction of multi-model and multi-task joint extraction, information extraction based on knowledge enhancement, and information fusion based on multi-modal at the method level. At the model level, further research should be carried out in the aspects of strengthening theoretical research, model lightweight, and improving model generalization ability. deep learning; information extraction; entity relationship extraction; event extraction; multi-modal information extraction +
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advanced levels of speech recognition, visual object recognition, object detection, and many other fields [2]. Different from traditional shallow learning, deep learning emphasizes the depth of the model structure, and obtains deep meaning by increasing the model's depth. Secondly, deep learning clarifies the importance of feature learning, and transforms the feature representation of the sample in the original space into a new feature space through layer-by-layer feature transformation, thus, making classification or prediction easier. Compared with classical machine learning, the characteristics of DL are shown in Table 2.  
Currently, the rapid development of deep learning has attracted widespread attention from academia and industry. Owing to their excellent feature selection and extraction abilities, these technologies have exerted an increasingly important influence on many tasks, including machine translation, object recognition, and image segmentation. Meanwhile, natural language processing (NLP), computer vision (CV), and speech recognition (SR) have been widely implemented. Therefore, the use of deep learning technology to promote the development of the field of natural language processing is the focus of current research. At the same time, scholars at home and abroad have devoted considerable effort and attention to this field and have carried out a great deal of research. Although existing deep learning algorithm models, such as CNNs and RNNs, have been widely used in the field of natural language processing, no major breakthroughs have been recently reported. It may be considered that research on deep learning in the field of natural language processing is still in its infancy, and a series of problems remain to be solved around DL-IE.