# K-EPIC: Entity-PerceIved Context Representation in Korean Relation Extraction
## 1 Introduction  
The importance of research on automatic information extraction is recently increasing [1] with the advent of massive unstructured documents. Information Extraction (IE), which provides the basic research for extracting structured information from unstructured resources, is considered promising research in the field of natural language processing (NLP). Among principal research in IE [2], Relation Extraction (RE) aims to predict the relation between two entities in a single sentence. RE is a significant task especially in the field of Knowledge Base Population (KBP) since it extracts structured triples. Additionally, it is used for advanced research, including Question and Answering (QA) systems, Summarization, Dialogue Systems, and Information Retrieval (IR) [3]. To obtain the final prediction form as (_entity1_, _relation_, _entity2_) in RE tasks, it is important to determine the precise meaning of two entities along with the context of the sentence [2]. For example, as shown in Figure 1, it is much easier to predict the relation \"_org: place_of_headquarters_\" if a person already knows the meaning of the subject \"[3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][11][11][12][13][15][14][16][17][18][19][21][19][22][20][21][22][23][24][25][26][27][28][29][20][21][21][22][24][26][29][21][2  
entities separated with [SEP] token [5], or another replaces entities with corresponding Named Entity Recognition (NER) tags [6]. The former implicitly represents the sentences without any entity-specific tokens, and the latter merely replaces two entities with specific tokens, such as <entity 1>, resulting in losing their meaning. To overcome these problems, [PERSON] et al. [7] utilizes the explicit representation of entities and predicts the relation on an English RE dataset [8; 9]. With the previous RE research in English, BERT-Ko-RE [10] and KLUE-RE [11], which are recently published data on Korean RE, elevate the level of research in Korean. In addition, [PERSON] et al. [10] demonstrates the performance of multilingual-BERT (mBERT) [4] trained with the corpus in 104 languages. However, a limited number of RE studies considering two linguistic properties of Korean which are different from English exist. One is that the role of the word in most Korean sentences is decided when the postposition is fully combined [12]. For instance, by combining '2-1 (root word; the prime minister)' and '1 (postposition)', the word '2-1 (r)' finally acts as the subject. Therefore, it is important to extract the comprehensive meaning of the word which is a combined form of root word and postposition. The other property is the free word order in Korean sentences. Contrary to English, the order of words is not a substantial obstacle to comprehending the meaning of the sentence, since the role of phrase or word is decided according to the types of a postposition [13]. Owing to the aforementioned characteristics, language models that have not been trained with Korean corpus exhibit limited performance on Korean RE. In this paper, we propose entity-perceived context representation and Korean language models. To apply our K-EPIC method to the Korean pre-trained language models, the entities of the dataset are marked with entity position tokens. Since we aim to predict relation by capturing the meaning of entities considering the linguistic properties especially in Korean, we demonstrate our experiments on the BERT-Ko-RE-dataset [10] and KLUE-RE dataset [11].