# Machine Learning Based Representative Spatio-Temporal Event Documents Classification
## 6 Discussion and Conclusions  
In this paper, we propose a BiLSTM-based RSTDoc classifier. Existing research on event sentence extraction only needs to consider words or grammatical elements related to events. However, classification of RSTDocs is a difficult problem because even if an event is not important for understanding the contents of a document, it is not classified as a representative spatio-temporal event. We built a gold standard of 10,000 training data to create an RSTDoc classification model. We conducted an experiment comparing the performance of traditional machine learning methods and deep-learning-based models. Experimental results show that BiLSTM outperforms conventional machine learning classifiers and conventional CNN-based classifiers.  
Although BiLSTM outperforms conventional CNNs and LSTMs in RSTEDoc classification performance, the limitation of this study is that the performance is still not high compared to general document classifiers.  
Our study does not guarantee the classification performance for other languages or data types outside of Korean news articles, as we constructed our training data using Korean news articles from the National Institute of Korean Language. Therefore, in the future, there is a need to construct training data using various types of documents other than Korean news articles and in languages other than Korean.  
Our ultimate research goal is to detect representative spatio-temporal events, and this study is a preliminary investigation into the classification of representative spatio-temporal event documents, which is a necessary step for detecting representative spatio-temporal events. Based on this study, further work is needed to develop a framework for detecting representative spatio-temporal event documents from real-time text data generated online, and apply it in the field.  
In order to improve the performance of typical spatio-temporal event document detection, research should be conducted on how to additionally utilize information related to name entity recognition in article text instead of simply using only article text as input data. It is also necessary to conduct research to further refine the model by adding an attention mechanism to the deep learning model in future research.  
Figure 4: Learning curve.  
Recently, deep-learning-based document classification models have demonstrated improved performance by utilizing Graph Convolutional Networks [35; 36; 37]. Therefore, as a future research direction, it is necessary to extend the classification model from a simple word-vector-based input to a graph-based model that incorporates information on interactions and relationships between words, sentences, and between sentences, to better utilize the information in the document.  
Conceptualization, B.K.; methodology, H.-J.J.; software, B.K.; validation, B.K.; investigation, H.-J.J. and B.K.; data curation, Y.Y.; writing--original draft preparation, B.K.; writing--review and editing, H.-J.J. and J.S.P.; visualization, B.K.; supervision, H.-J.J.; funding acquisition, Y.Y.; project administration, B.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Government (MSIT) (No. 2021R1F1A1049387).  
Not applicable.  
The authors declare no conflict of interest.