# A Survey of Information Extraction Based on Deep Learning
## Appendix A Summary of Characteristics and Applications of Typical Methods
\begin{tabular}{c c c} \hline \hline
**Typical Model** & **Characteristic** & **Application** \\ \hline \multirow{6}{*}{KERE (2020)} & Extracting knowledge information from the knowledge graph to \multirow{2}{*}{KETE (2020)} & \multirow{2}{*}{Biomedical relationship} \\  & generate knowledge-oriented word embeddings can enhance the & \\  & effectiveness of the word embedding and using lexical features as & \\  & supplementary information for semantic understanding can reduce & \\  & semantic ambiguity and manually annotate [41]. & \\ \hline \multirow{2}{*}{NAM (2019)} & [43] demonstrates combining contextual information and knowledge & Chemical-disease \\  & representation with an attention mechanism. & relation extraction. \\ \hline \multirow{4}{*}{ATLOP (2021)} & Adaptive thresholds replace global thresholds, and the local context & \multirow{2}{*}{MILOP (2021)} & Document-level \\  & pool shifts attention from the pre-trained language model to the & & relationship extraction in \\  & localization-relevant context, alleviating multi-label and multi-entity & problems [45]. & \\ \hline \multirow{2}{*}{HDP (2019)} & Converts the multi-pair-triplet extraction into a sequence generation & \multirow{2}{*}{Multi-relationship triples} \\  & task; generates a hybrid binary-pointer sequence extraction to alleviate & \\  & the entity overlap problem [53]. & \\ \hline \multirow{2}{*}{KSBERT (2021)} & Integrates domain-specific external lexical and syntactic knowledge into \multirow{2}{*}{KSBERT (2021)} & Military entities and \\  & end-to-end neural networks to solve the overlap problem [54]. & relationship extraction. \\ \hline \multirow{2}{*}{KEWE-BERT (2021)} & Overlays the token embeddings and knowledge embeddings of BERT & \multirow{2}{*}{Construction of the \multirow{2}{*}{and TransR output with [55].} & \multirow{2}{*}{\begin{tabular}{c} Manufacturing domain \\ knowledge map. \\ \end{tabular} } \\  & & \\ \hline \multirow{2}{*}{Gated-K-BERT (2021)} & Combines knowledge-aware attention representations with BERT; entities and their relations are jointly extracted using a gating fusion & Study of the associations \\  & & & between depression and \\  & sharing mechanism [56]. & \\ \hline \multirow{4}{*}{GraphRel (2019)} & Combining the RNN and GCN, extracting the sequence features and & \multirow{2}{*}{Joint entity and relationship extraction; alleviate} \\  & region-dependent features of each word, considering the recessive & \\  & features between word pairs, and considering the interaction between named entities and relationships through the relationship-weighted & \\  & GCN [57]. & \\ \hline \multirow{4}{*}{A2 DSEE (2021)} & Advanced graph neural network GeniePath is introduced in DSRE to \multirow{4}{*}{A2 DSEE (2021)} & Reduce noise and remote \\  & incorporate additional supervised information from the knowledge & supervision relationship \\  & graph through the margin between the representation of the retraction & \multirow{2}{*}{extraction.} \\  & package and the pre-trained knowledge graph embeddings [67]. & \\ \hline \multirow{2}{*}{REIF (2022)} & Bridges the gap of realizing the influence of sub-sampling in \multirow{4}{*}{DMCNN (2015)} & Solve the problem of high \\  & deep learning. & noise interference. \\ \hline \multirow{4}{*}{JRNN (2016)} & Vocabulary and sentence-level features can be automatically extracted & \multirow{2}{*}{Single-task event extraction.} \\  & from plain text without complex NLP preprocessing; using dynamic \multirow{2}{*}{\begin{tabular}{c} Multi-pooling layers to store more valuable information based on event \\ triggers and event arguments [71]. \\ \end{tabular} } \\  & multi-pooling layers to store more valuable information based on event & \\  & triggers and event arguments [71]. & \\ \hline \multirow{4}{*}{Event extraction} & Based on the bi-directional RNN, introducing the memory matrix can \multirow{4}{*}{JRNN (2016)} & Multi-event information \\  & effectively capture the dependencies between the argument & \multirow{2}{*}{extraction.} \\  & element-roles and the trigger sub-types [73]. & \\ \hline \multirow{4}{*}{DCFEE (2018)} & Remote monitoring automatically tags event reference annotation & \multirow{2}{*}{\begin{tabular}{c} Online event extraction of \\ financial news and Chinese \\ financial texts. \\ \end{tabular} } \\  & triggers and arguments throughout the document, which includes & \\  & sentence-level event extraction and document-level event & \\  & extraction [87]. & \\ \hline \multirow{4}{*}{Event extraction} & In the entity extraction module, the BiLSTM is used to capture the \multirow{4}{*}{HNN-EE (2019)} & Joint entity and \multirow{4}{*}{\begin{tabular}{c} Event extraction. \\ event extraction. \\ \end{tabular} } \\  & long-distance dependence information; in the event extraction module, & \\  & the self-attention layer captures the internal structure of the sequence, & \\  & and the gated convolution layer extracts the higher-level feature [86]. & \\ \hline \multirow{4}{*}{CNN-BiGRU (2021)} & The word vector and position vectors are stitched as input, and \multirow{4}{*}{\
\begin{tabular}{c} Event-trigger \\ word extraction. \\ \end{tabular} } \\  & word-level features are extracted using CNN and sentence-level & \\  & features [85] using BiGRU. & \\ \hline \multirow{2}{*}{DEGREE (2022)} & A data-efficient model that formulates event extraction as a conditional & Focus on low-resource \\  & generation problem. & end-to-end event extraction. \\ \hline \multirow{4}{*}{UIE (2022)} & Structured extraction languages operate to uniformly encode different & \multirow{2}{*}{
\begin{tabular}{c} Unified extraction of \\ entities, relationships, and \\ events in general fields. \\ \end{tabular} } \\  & extraction structures, adaptively generate target draws, and capture the & \\  & common IE capability [80] through large-scale pre-trained & \\  & *text-to-structure\" models. & \\ \hline \hline \end{tabular}