## 2 Related Research
### Machine Learning Approach  
The traditional machine learning-based approach solves the relation extraction task as a classification problem. Supervised machine learning-based approaches train the parameters of the classifier with a labeled training dataset, and then test the model performance on a test dataset of unlabeled categories. Methods based on feature engineering and kernel function are machine learning methods. The feature engineering-based approach requires explicit conversion of specific relation instances into feature vectors that are acceptable to the classifier. The kernel function-based approach is a direct extraction of the instance structure tree and uses the kernel function instead of using the inner product of feature vectors when determining the spacing in entity relations. [PERSON] et al. [15] proposed a supervised machine learning approach to discover the relations between medical problems, treatments, and tests mentioned in electronic medical records. The method was used to identify relations between concepts and assign their semantic types through a single support vector machine classifier. [PERSON] et al. [16] proposed a model of novel embedding features based on knowledge and BioSentVec. The method systematically investigates these features. In addition, the method investigates the effects of distance-based and word-based features. SVM and decision trees are also classical machine learning classification methods. SVM automatically finds those support vectors that have a better discriminatory power for classification, and the resulting classifier is constructed to maximize the district classification and class interval. [PERSON] et al. [17] proposed a text classifier construction method based on fuzzy support vector machines with decision trees. The method measures inter-sample relationships based on SVM and parallel to the plane of the classification plane as a tangent sphere, and it is combined with a decision tree to effectively solve the multi-classification problem. Decision trees are constructed using a top-down recursive approach to the tree, and test attributes are selected at each node of the tree using an information gain metric. [PERSON] et al. [18] proposed a fuzzy decision tree method. The method reduces the sensitivity of the generated decision tree to changes in attribute values through a fuzzy algorithm. In addition, a threshold value on the affiliation value of the object is introduced, thereby reducing the number of rules needed for decision-making. This method reduces the running time and improves classification accuracy. Fuzzy classification is widely used in machine learning. Fuzzy classification deals with linguistic uncertainty in instances based on fuzzy logic, in which case each instance is not explicit and has different affiliations in different classifications. Traditional fuzzy methods use each classifier separately for classification. [PERSON] et al. [19] proposed a new fuzzy ID3 algorithm for generating comprehensible fuzzy classification rules. The method can accurately estimate the interaction of attributes by cumulative information estimation, thus finding a sequence of rules with close to minimum classification cost. Although traditional machine learning-based methods for biomedical relation extraction reduce the manual burden to some extent compared with rule-based methods, they require a large amount of feature engineering and need further improvement in recognition rate.