# GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
## 4 Experiment and Results
### Model Performance Comparison
However, this changes drastically when we go to the second task: comparing global extractions. In this task, each input is given as documents, and only relations that are labeled as 'global relations' within the test set are evaluated. Here, we can see our proposed model truly shining. All three GREG variants show a significantly higher F1 score compared to the other models. Additionally, one can observe that the GREG variants reflect the performances of their local relation extraction module, indicating that our proposed approach's performance is largely affected by how its local relation extraction module performs on local relation extraction. Thus, we can observe the GREG(Context) showing the highest performance, followed by variants GREG(LSTM) and GREG(CNN) accordingly, which matches how the LSTM Context model [16], LSTM baseline model [23], and CNN baseline model [22] perform. For the baseline models, their low performances are likely caused by the bottleneck problem [8] triggered by the document sized input. Finally, we see how each model fares in overall performance. While we used the standard test set for the DocRED dataset [4] to evaluate the models' ability to extract relations in various lengths of input, we used the Korean Documents of History dataset [6] to measure each model's overall performance on dealing with document inputs in general. Therefore, when using our Korean dataset, each model is given a document sized inputs for evaluation. However, while the GREG variants are only given the document as a single input, the baseline models received the input by both document and sentence by sentence. This enables a fair overall performance comparison on both local and global relation extractions. As seen in Table 2, all three GREG models vastly outperform the models they are based off. Proving the proposed method greatly advances the model's performance in dealing with documents. By pure performance, we can observe our original model GREG(Context) showing the highest performance, followed by the LSTM Context Model [16] and the GREG(LSTM) variant. We suspect that, despite the latter GREG variants displaying higher performances in global relation  
extraction, the other two GREG variants came up short because the number of local relations vastly outnumber the number of global relations within each document--thus leading to an overall lower F1-score in general. This again strengthens our suspicion that our proposed method depends greatly on which local relation extraction model is used. Through these evaluations, we presented that our proposed module shows the highest performance among all others in global relation extraction. Even when we evaluate overall performance, it still performs better than all the other models by a significant amount. Proving our proposed approach is an effective way to extract global relations from documents while minimizing the loss of performance on local relation extractions. The advantage we have over the other models is simple. Other models struggle to contain and compress as much data as they can, yet attempting to not lose any vital information during a single process. Our approach allows the model not to suffer from this problem by taking a divide and conquer approach. Extracting each vital information from a sentence one at a time enables to not lose any vital information. In addition, when it recreates the gathered summary into a knowledge graph, it enables the model to recognize all the vital information it needs for global relation extraction--thus allowing the task to be done without having to bite more than the model can chew.