# K-EPIC: Entity-PerceIved Context Representation in Korean Relation Extraction
## 3 Proposed Method
### Relation Extraction with K-EPIC  
We extend the methods of providing additional entity information in four ways, inspired by the work of [PERSON] et al. [7]. Our four types of K-EPIC methods are illustrated in Figure 3. We put [e1 sp] and [e1 sp] at the start and end positions of the subject entity. Similarly, we place [e2 sp] and [e2 sp] at the start and end positions of the object entity, respectively. Because e1 does not always precede e2 in all sentences, our K-EPIC identifies the position of each entity and inserts the entity position tokens before and after the entity. Except for nonK-EPIC, we use this final preprocessed sentence, \(SI\), as an input. Detailed information regarding each K-EPIC is explained below.  
**nonK-EPIC** nonK-EPIC representation merely utilizes the [CLS] token of the input sentence in the final layer of language models. Note that nonK-EPIC inputs do not include any entity position tokens. As the hidden states barely include information about entity position, the models are reluctant to find the precise position of entities and their meanings explicitly. nonK-EPIC uses \(h_{V}\) as the final hidden representation, which is identical to the hidden state vector of the [CLS] token.  
**K-EPIC\({}_{V}\)** Whereas nonK-EPIC is not created from \(SI\), the vanilla method of our K-EPIC, K-EPIC\({}_{V}\) processes [CLS] of the \(SI\). Similar to nonK-EPIC, K-EPIC\({}_{V}\) uses \(h_{V}\) as the final hidden representation. We can compare the effect of entity position tokens between nonK-EPIC and K-EPIC\({}_{V}\).  
**K-EPIC\({}_{S}\)** K-EPIC\({}_{S}\) exploits \(h_{S}\), which represents the concatenation of the start position token of each entity, [e1 sp] and [e2 sp]. The concatenated vector is then fed into the linear projection layer to obtain the final relation. The representation enables the models to perceive entity information.  
**K-EPIC\({}_{E}\)** In contrast to K-EPIC\({}_{S}\), K-EPIC\({}_{E}\) uses only end position tokens, i.e., [e1 sp] and [e2 sp], in the same manner as K-EPIC\({}_{S}\). In K-EPIC\({}_{E}\), the final hidden representation, \(h_{E}\), is the concatenation of these two end position tokens.  
**K-EPIC\({}_{SE}\)** We also suggest the combination representation of K-EPIC\({}_{S}\) and K-EPIC\({}_{E}\) that utilizes all position tokens of the entities in \(SI\), i.e., [e1 sp], [e1 sp], [e2 sp], and [e2 sp]. K-EPIC\({}_{SE}\) uses \(h_{SE}\), which is also the concatenated result of all entity position tokens. We  
Figure 2: Overview of our K-EPIC process utilizing RE.  
analyzed the performance differences between these methods based on the number of entity position tokens.  
In summary, the input of nonK-EPIC consists of only original sentence with [CLS] and [SEP] tokens. _SI_, which includes entity position tokens, is applied to K-EPIC\({}_{V}\), K-EPIC\({}_{S}\), K-EPIC\({}_{E}\), and K-EPIC\({}_{SE}\). The input embedding, segment embedding, and position embedding of the _SI_ are used to create the representation. The final representation, \(h\), is then fed into the linear layer and projected on the size of total relations. Consequently, the softmax result denotes the final probability for predicting the relation, as shown in Equation (2):  
\[Pr(rSI)=softmax(W^{T}\cdot h+b) \tag{2}\]  
where \(W\) and \(b\) are learnable parameters.