# An Easy Partition Approach for Joint Entity and Relation Extraction
## 2 Related Work  
The task of TE aims to extract entities and their relations from unstructured text and represent them as relational triples. The traditional TE method is usually divided into two subtasks: NER and RC. NER identifies entity boundaries and types from text, while RC determines the semantic relation between entity pairs. Various methods have been proposed for both subtasks. Table 1 shows the main methods. For NER, there are span-based methods [19; 20] that predict entity spans by scoring all possible text segments, and tag-based methods [21] that assign a tag to each token indicating its entity type and position. For RC, there are methods that use independent encoders for each subtask [3; 22], treating them as span classification and span pair classification problems, respectively. However, these methods suffer from weak interaction between the two subtasks, which may lead to inconsistent predictions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Method Type** & **Meaning of Methods** & **Related Paper** \\ \hline \multirow{3}{*}{Span-based method} & In NER tasks, it refers to the process of identifying entity spans in text and classifying them. & [19; 20] \\  & In RE tasks, it is the process of classifying entity span pairs. & [19; 20] \\  & In NER, it refers to assigning a label to tokens in text, often using “BIo” and “BIoG” to label token positions, and \\ \multirow{2}{*}{Tag-based method} & combining token positions and entity types as labels for entities, such as B-location. & [21; 23] \\  & In RE tasks, a combination of token position, relation type, and entity position is often used as labels, such as B-C-O-1. \\ \multirow{2}{*}{C-O-1} & Usually using T \(\times\) N \(\times\) the matrix of N represents the sentence sequence, T represents the entity type or relation type, and N represents the sentence length. In NER, take the diagonal of the matrix as the starting position of the entity, find the \\ \multirow{2}{*}{Table filling method} & corresponding ending position of the entity in the upper triangle of the matrix, and mark it as 1. Determine the entity in the sentence according to the matrix. \\ \multirow{2}{*}{In RE, find the position corresponding to the token pair of the head or tail of the entity pair in the matrix, mark it as 1, and determine the entity pairs with relation based on the matrix. The main idea of this method is to represent entities and relationships as nodes and edges in the graph, and then use graph algorithms to process them. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common technology paradigm of entity recognition and relation extraction. To address this limitation, recent works have explored joint extraction methods that combine NER and RC using a single model. These methods can be roughly categorized into three paradigms: 1. Based on the table filling method [15; 24; 25], which presents entities and relations in tables. For example, [PERSON] et al. [15] used a table for each type of entity and relation. By combining the results from NER and RE, relational triples can be extracted. 2. Based on the method of sequence tagging, the tag system encodes both entity position and relation type for each token [21]. Then, a triplet is formed according to the tag of the token. For example, [PERSON] et al. [21] proposed a novel tagging scheme and changed the extraction problem to a sequence labeling problem. To solve overlapping relations, [PERSON] et al. [23] designed a handshaking tagging scheme and transformed the task into a token pair linking problem. 3. Graph-based methods [26; 27], which build graphs for entities and relations, and perform graph neural network operations on them. For example, [PERSON] et al. [27] constructed an entity-relation graph based on dependency parsing results, and applied graph convolutional networks to capture global semantic information. In the joint extraction method, the two subtasks usually share the encoding layer with weak independence.