## 3 Overview of the Model
### Relational Extraction Model Based on Multiple Feature Binding  
For one of the relationship extraction tasks, this paper obtains more semantic feature information by BERT encoder in order to make full use of the information contained in Chinese text based on the BERT model to improve the accuracy of relationship extraction. This paper classifies the textual relations based on a BERT-based Chinese pre-trained model. After analyzing the data in the dataset, the relationship of most sentences is contained between two entities. If two entities are close to each other and there is no relationship in between, then the relationship may be hidden among the second entity behind. For example, in a news article that mentions a relationship between two people, the names of these people may be closely related, or there may be some words spaced apart. By adding the distance information between two entities, the location of the entity relationship information can be better determined. Therefore, the distance between entities (expressed in number of words or characters, for example) can be used as a feature to help the model determine the relationship between them. Second, the relationship between two entities is usually related to the length of the sentence. Length features can help identify differences between texts of different lengths. For example, in shorter texts, relationships may be easier to identify because there is less other information in the text, while in longer texts, relationships may be more difficult to identify because there is more interfering information. Therefore, in many cases, length features can provide useful clues to help the model perform relationship extraction more accurately. The lexical properties of Chinese often contain rich information features. In this paper, we divide the sentences into words and extract the lexical properties by THULAC [20]. (THULAC is an efficient Chinese lexical analysis toolkit.) These properties are then compared with other lexical properties, such as adjectives and prepositions. Nouns contain relationships with a greater possibility. In general, nouns or verbs are more likely to contain relations compared to other word types. For example, [PERSON], [PERSON], and [PERSON] swore brotherhood, becoming brothers. Using THULAC for part-of-speech tagging results in the following ([PERSON]\(\backslash\)noun, \(\backslash\)w [PERSON]\(\backslash\)noun, and\(\backslash\)c [PERSON]\(\backslash\)noun source brotherhood\(\backslash\)verb, \(\backslash\)w becoming\(\backslash\)verb brothers\(\backslash\)noun). Here, \"swoe brotherhood\" is a verb, and \"brothers\" is a noun, indicating a relationship between each pair of entities. Finally, multiple features are stitched together and fed into the model. The experimental results show that the F1 values of the method are improved on several Chinese relationship extraction datasets, and the recognition rates of relationships for different categories are greatly improved, respectively. In addition, the model obtains more features by introducing word vectors that have been externally passed, where the word vectors are pre-trained. The word vector of the \(i\)-th word is \(V_{i}^{w}\), which is obtained by looking up the table using the pre-trained word vector, and the other feature vectors are \(V_{i}^{w}\), where \(V_{i}^{w}\) represents is the \(j\)-th feature. The final word representations are stitched together as word vectors and feature vectors as follows:  
\[X_{i}=\left[ \begin{array}{cc}V_{i}^{w};V_{i}^{w1}\ldots;V_{i}^{wm}\end{array} \right]\]  
Among them, m means that in addition to the word vector, there are m kinds of features. In this paper, \(m=3\). For sentence-level representation, directly input the word representation into BiLSTM for encoding, use \(F\) (forward) and \(B\) (backward) to represent the two directions. \(h_{i}\) (hidden) and \(c_{i}\) (cell) represent hidden information and global information; then the output at the \(i\)-th moment is:  
\[F_{i}=\left[ \begin{array}{cc}F_{hi};F_{Gi};B_{hi};B_{ci}\end{array}