# A Survey of Information Extraction Based on Deep Learning
###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline
**IE Methods** & **Main Ideas** & **Characteristics** \\ \hline \multirow{8}{*}{Methods based on rule and dictionary} & Rule based methods: by & The method based on manual \\  & summarizing the rules, experts & rules can achieve high accuracy \\  & construct a large number of rule & on small data sets, but it has no \\  & templates, and extract information & adaptability to a large number of \\  & based on the templates. & data sets or new fields. The \\  & Dictionary based methods: to & establishment of new rule bases \\  & establish a dictionary of recognition & and dictionaries requires a lot of \\  & objects; the process of information & time and manpower. These rules \\  & extraction is the process of & often depend on specific \\  & searching in the dictionary or & languages, and it is difficult to \\  & professional domain & cover all languages. \\  & knowledge database. & Although the method based on \\  & Supervised training is carried out & statistical machine learning has \\  & by using the manually labelled & significantly improved results \\ Methods based on & corpus, and then the prediction is & compared with the previous \\ statistical & realized by using the trained & methods, it also requires a lot of \\ machine learning & machine learning model. Common & manual annotation by people \\  & methods include HMM, MEM, & with professional field knowledge, \\  & SVM, ME, CRF, etc. & and the cost of labor and time is \\  & The complex pattern recognition & \\  & problems are solved by & It is applicable of big data \\ Methods based on & automatically identifying & processing and automatically \\ deep learning & information features and internal & learns sentence features without \\  & laws through complex network & complex feature engineering. \\  & structures. Common methods & \\  & include CNN, RNN, etc. & \\ \hline \end{tabular}
\end{table} 
Table 1: The Development history of IE. In a review of the existing relevant literature, [PERSON] et al. discussed the progress of deep learning in terms of word annotation, syntactic analysis, emotion analysis, machine translation, and text classification [4]. He believes that the accuracy of the results of deep learning algorithms depends on the amount of training data. Combining existing knowledge (i.e., traditional machine learning methods) with deep learning methods to improve learning efficiency is the next research direction. [PERSON] introduced convolutional neural networks and recurrent neural networks and detailed the BERT, XLNet, and ERNIE3 pretraining language models, and argued that many challenges remain to be overcome for deep learning in NLP to be implemented on a large scale, despite its dramatic recent great successes [5]. [PERSON] et al. used CiteSpace and VOSviewer to draw a knowledge graph of the research countries, institutions, journal distribution, keyword co-occurrence, occited network clustering, and time axis view of deep learning in the field of natural language processing to clarify the research context. By reviewing the important literature in the field, the research trends, main problems, and development bottlenecks of deep learning in the field of natural language processing were summarized, and the corresponding solutions and ideas were provided [6]. [PERSON] et al. divided the common problems in the implementation of multi-modal deep learning into four categories: modal representation, modal interpretation, modal fusion, and modal alignment, and sub-classified and discussed various problems [7]. It can be seen that most of the previous literature reviews analyzed the pre-training model, field application, research trajectory, and distribution of NLP. There are few in-depth analyses and elaborations from the perspective of IE, the foundation and core issue of deep learning in natural language processing. Therefore, we review the latest literature on the