# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
## 3 Candidate Term Boundary Conflict Reduction Method Based on Information Entropy
### Information Entropy of Single Geological Text Candidate Terms  
Entropy quantifies chaotic relationships of molecular states in thermodynamics; information entropy represents uncertainty theory in information theory. In the field of word segmentation, it can be used to quantify the internal tightness and external degree of freedom of terms. Its mathematical expression is as follows:  
\[H=-\sum P(x)\log P(x) \tag{1}\]  
Based on the character connection relationships and meaning of the formula, the mathematical expression of the left and right information entropy converted to a string is as follows:  
\[H_{l}(s)=-\sum_{a\in A}P(s_{la}/s)\log(P(s_{la}/s)) \tag{2}\]  
where, for the candidate term \(s\), \(H_{l}(s)\) is the information entropy on the left side of the term \(s\). \(A\) is the set of all Chinese characters on the left side of \(s\). \(s_{la}\) represents combinations formed of the Chinese character \(a\) on the left side and \(s\). \(P(s_{la}/s)\) represents the conditional probability that the Chinese character \(a\) appears on the left side of \(s\) under the premise that the term \(s\) appears in the text.  
\(H_{l}(s)\) reflects the average uncertainty of Chinese characters appearing on the left side of string \(s\). The larger \(H_{l}(s)\) is, the more uncertain the Chinese character collocation on the left side of \(s\) is, that is, the higher the left degree of freedom of the term \(s\) is and the greater the probability of inferring that the string combination \(s\) should be disconnected on the left side as the term boundary. Taking \"Di Zhi (Geology)\" and \"Suan Yan (-ate, such as a suffix of carbonate)\" as examples, the approximate calculation process of their information entropy is shown in Figure 2 below. The left information entropy of \"Di Zhi (Geology)\" is 4.8414, while the left information entropy of \"Suan Yan (-ate)\" is only 0.1274.  
Figure 1: Technical route of Chinese geological text segmentation based on information entropy.  
Similarly, the mathematical expression of information entropy on the right side of candidate term \(s\) can be obtained:  
\[H_{r}(s)=-\sum_{\alpha\in A}P(s_{\pi a}/s)\text{log}(P(s_{\pi a}/s)) \tag{3}\]  
Information entropy is a quantification of the internal tightness and external degree of freedom of a character combination. From the numerical results, the information entropy of a certain character combination can be zero, which means that the left and right connections of the character combination in the current calculation space are fixed without uncertainty and a degree of freedom. It can be non-zero with uncertainty and a degree of freedom, indicating that the character combination basically constitutes a term boundary. The information entropy can be very high, indicating that when the character combination appears as a whole, there are many uncertain possibilities for the left and right connections, that is, the degree of freedom is large. Then, we believe that the combination appears in the text as a whole in enough occurrences and the probability of it being a correct term is high. It may be very low, indicating that the uncertainty is small, the string group has fewer external character collocations, or the string itself is less; then, the probability of being a correct term is small.