# 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 RNN (2012) & Handles both internal feedback connections and feed-forward connections between the units. & Suitable for the extraction of timing features. \\ \hline CNN (2014) & The structure is simple, and the neural network is used for feature extraction, which avoids the tedious manual feature & Suitable to process data with correlation. \\ \hline PCNNs (2015) & Regard the remote-supervised relation extraction problem as a multi-instance problem, and a convolution structure with a segmented maximum pool is used to automatically learn related features. \\ \hline CAN (2018) & Produces deep semantic dependency features, introducing attention mechanisms to capture dependency representation [18]. \\ \hline LSTM (2014) & Produces long-term dependencies from the corpus, but the network structure is more complex. & Machine translation, dialogue generation, encoding, and decoding. \\ \hline K-CNN (2019) & Contains two collaborative channels: the knowledge-oriented channel and data-oriented channel and combines the information obtained from the two channels [19]. \\ \hline CRN (2020) & Extracts non-superordinate relationships from the unstructured text [20]. & Food field. \\ \hline \multirow{4}{*}{Entity relation extraction} & Uses the character-level and sentence-level attention mechanisms, finds the words that have a significant impact on the output, and gives them a higher weight to better obtain their semantic information [25]. \\ \cline{1-1}  & Focuses on words with decisive influence on sentence relationship extraction, creates word-level low-dimensional entity vectors at the embedding layer, and feeds the word embedding and position embedding results to the BiGRU layer to obtain high-level features [11]. \\ \hline \multirow{4}{*}{GREG (2020)} & The two modules of the model are synchronized during training, and each of the model’s modules is designed to deal with local relationships and global relationships separately [26]. \\ \cline{1-1}  & Takes full advantage of the document structure and external knowledge of [9]. & Medical relationship extraction at the document level. \\ \hline \multirow{4}{*}{AGCN (2020)} & Uses context and structural knowledge, combines GCN and a recurrent network-based encoder, and employs a new attention-based pruning strategy [30]. & Drug-drug interaction extraction. \\ \hline \multirow{4}{*}{FC-GCN (2021)} & Creates the un-directed graphs based on the combined features, uses the atomic features as the nodes, constructs the edges between the nodes according to the combination rules, and considers the prior knowledge and avoids the error caused by the resolution [31]. \\ \cline{1-1}  & A dense connectivity graph attention network is used to update the representation of nodes and a two-step iterative algorithm to update the representation of edges [34]. \\ \hline BioGraphSAGE (2021) & Biological semantic and positional features are combined to improve the identification of long-distance entity relationships [35]. & Biological entity relationship extraction. \\ \hline \hline \end{tabular}