# DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT
## 5 Conclusions  
In this paper, we propose a DeNERT-KG model that can extract the subject, object, and relationships from a sentence. The ability to understand and process people's language is very important for computers to learn on their own. To understand a person's language, in particular, one must be able to find the subject and object underlying the sentence and find the relationship between the two. To carry out these tasks, we use the DeNERT model with Deep Q-Network to extract the subject, object, and type of each word. It also applies the extracted information to the relationship extraction model applied with knowledge graph embedding so that the object can finally be given, and so the relationship information can be extracted. When inputting sentences, DeNERT-KG, which can extract subject, object, and relationship, is a very important model for use in various fields. However, as it is still a model that goes through the steps of extracting relationships with NER, it can be given through one model, creating a high-performance model that can extract triples of entities and relationships. Already, many studies are being actively conducted in the field of NER and relationship extraction. However, studies to perform both NER and relationship extraction tasks at the same time are relatively limited due to the complexity of the task. We not only propose a model that can perform two tasks at once but also show the possibility of using DQN in the NER task and the possibility of the knowledge graph in relation extraction task. Additionally, designing and building a model that can extract quadruple information at once, not only subject, object, and relationships, but also the emotion between subject and object, is expected to make an important contribution to the NLP task. Still, there are some challenges such as handling multiple entities. If the proposed model has multiple entities in the sentence, each entity can be identified as a separate entity and extract multiple relationships, but there is a limitation that cannot be extracted to equivalent entities in the same relationship. When the list of multiple entities is extracted, it can be resolved to some extent by adding an automatic postprocessing process. In future work, we could create a robust model for multiple entities. Additionally, there is a challenge to overcome the limitation of the dependency on the dataset. Only extracting the relationship of the proposed model is possible, especially for the relationship specified in the dataset. To overcome this limitation, we could utilize techniques such as distant supervision to enable the extraction of unlabeled relationships. Conceptualization, [PERSON] ([PERSON]), [PERSON] ([PERSON]), and O.J. ; methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, O.J. ; resources, O.J. ; data curation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing, O.J. ; visualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); supervision, O.J. ; project administration, O.J. ; funding acquisition, O.J. All authors have read and agreed to the published version of the manuscript. This research was funded by Basic Science Research Program through the NRF(National Research Foundation of Korea), and the MSIT(Ministry of Science and ICT), Korea, under the National Program for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline Matching-the-Blanks [23] & - & - & 71.5 \\ C-GCN + PA-LSTM [22] & 71.3 & 65.4 & 68.2 \\ PA-LSTM [21] & 65.7 & 64.5 & 65.1 \\ Logistic Regression [22] & 73.5 & 49.9 & 59.4 \\
**ALBERT + KG (DeNERT-KG)** & **71.8** & **73.1** & **72.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison result on TACRED dataset.