# Research on Intelligent Perception Algorithm for Sensitive Information
## 3 Methodology
### Classifier  
We input the context semantic vector \(R_{E}\) and the entity vector \(R_{K}\) obtained by the KG module in Section 3.1.2 into the concat module to concatenate the two vectors into forming a 968-dimensional vector R, as shown in (11). The flow of the whole classifier is shown in Figure 5.  
\[R=R_{E}\bigoplus R_{K} \tag{11}\]  
The classifier compares the words in the obtained vector R. The similarity calculation formula used for the comparison is shown in (12). We compare the word, \(t_{i}\), in the input sentence with the different types of the words, \(t_{j}\), in the sensitive word category library and take the largest \(\text{Sim}(t_{i}\),\(t_{j})\) to assign to \(\text{Smw}(t_{i})\) to receive the similarity score, \(\text{Smw}(t_{i})\), for the word, \(t_{i}\), and classify the sentence into the class c in which the \(t_{j}\) is located, as shown in (13). \[Sim(X,Y)=\frac{\sum_{i=1}^{n}(X_{i},Y_{i})}{\sqrt{\sum_{i=1}^{n}X_{i}^{2}}\cdot \sqrt{\sum_{i=1}^{n}Y_{i}^{2}}} \tag{12}\]  
\[Smw(t_{i})=max\big{(}Sim\big{(}t_{i},t_{j}\big{)}\big{)} \tag{13}\]  
Then, the classifier compares the semantic vectors \(\text{V}(Sen_{i})\) of the input sentences with the semantic vectors \(\text{V}(Sen_{j})\) of the class c of sensitive sentences in the sensitive sentences library C. \(Sim(V(sen_{i}),V(sen_{j}))\), the largest \(Sim(V(sen_{i}),V(sen_{j}))\) is assigned to \(Sms(sen_{i}i)\), as shown in (14):  
\[Sms(sen_{i})=max\big{(}Sim(V(sen_{i}),V(sen_{j}))\big{)} \tag{14}\]  
The final assessment _Score_ of the input text is calculated by attaching weights to the statements of different categories \((\alpha_{1},\cdots,\alpha_{m})\) is the weight of each category of sensitive statements, which is set to 1 in this paper and can be adjusted according to the requirements, as shown in (15). If the final assessment _Score_ exceeds the set threshold, the input text is determined to be sensitive text. \[ \begin{array}{c}Score=\alpha_{1}*Sms(C_{1})+\alpha_{2}*Sms(C_{2})\\ \cdots+\alpha_{m}*Sms(C_{m})\end{array}  \tag{15}\]  
Therefore, the TSIIP algorithm comes from this. The operation mechanism of the algorithm is shown in Algorithm 1. We use an example to illustrate our algorithm. The input text \"US-Malaysian navy assembles in South China Sea for joint military exercises\" is split into \"US-Malaysian navy\", \"South China Sea\", \"assembly\", and \"joint military exercises\". First, its segmentations are compared with the sensitive words in the sensitive vocabulary, T, and the similarity score, smw, is calculated for each word to classify it as a military sentence. Then it is compared with the sensitive sentences in the sensitive sentence library C and the similarity score, sms, is calculated for the whole sentence. If the similarity  
Figure 5: The flow of the whole classifier. score, sms, of the sentence exceeds the threshold, S, it is classified as a sensitive military sentence; otherwise, it is a military non-sensitive sensitive sentence. ```
0: Pending articles Text, Sensitive vocabulary T, Sensitive Threshold S, Sensitive sentences library C
0: Categories of Text and The sensitivity judgment of Text
1: There are m types of sensitive categories in the Sensitive vocabulary T and Sensitive sentences library C.
2: Split the pending article text into sentences, split sentences into words, and remove the stop words to get n statements.