# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
## 3 Candidate Term Boundary Conflict Reduction Method Based on Information Entropy
### Boundary Conflict Reduction for Candidate Term  
In the actual process of word segmentation, word segmentation conflicts of candidate terms will be encountered. Through a large number of example analyses and induction, the conflict area is mainly the text character range covered by the conflict terms, which can be divided into two types of conflicts: inclusive conflict and intersecting conflict. Inclusive conflicts are conflicts in which one candidate term contains another candidate term, resulting in doubtful segmentation. As shown in Figure 3 below, inclusive conflict can be subdivided into same-starting-point inclusive conflict, same-ending-point inclusive conflict, and inner inclusive conflict. Among them, the conflict of the same starting point and the same ending point only causes doubt in the boundary of one side of the term, whilst the boundary of another side is certain. Since there are different segmentation requirements in different texts, inclusive conflict sometimes does not involve absolute errors, and appropriate term boundaries need to be selected according to granularity requirements. In the sentence example \"[PERSON] Zhou Gou Zu Chu Lu Yu Ce Qu Xi Bu Yang Er Zhuang (The Changzhougou Formation is exposed in Yangerzhuang in the west of the survey area)\" shown in the following figure, the line segment composed of the left and right information entropy represents the left and right boundary degree of freedom of each candidate term. According to the logic of information entropy to quantify the internal tightness and external degree of freedom, when an inclusive conflict occurs, the term will have high information entropy regarding the conflict boundary, that is, a high degree of freedom should be selected as the result of word segmentation, that is, \"[PERSON] Zhou Gou  
Figure 2: Flow chart of information entropy calculation. Zu (Changzhougou Formation)\", \"[PERSON] (exposed)\", \"[PERSON] Qu Xi Bu (in the west of the survey area)\" as a result of conflict resolution. In a large number of case verifications, this solution meets the expectation of word segmentation results. Intersecting conflicts are conflicts in which a candidate term is connected to another candidate term and the attribution of some characters on the related side is uncertain. Intersecting conflict usually involves absolute errors in the attribution of some characters, which must be solved. Observing the examples of the candidate term on the uncertain side, in the sentence \"Zhong Ceng Zhuang Tan Suan Yan Jiao Jie Xi Li Shi Ying Sha Yan Jia Zi Se Ye Yan (medium layered carbonate cemented fine-granularity quartz sandstone with purple shale)\" shown in Figure 4 below, there is an intersecting conflict between \"[PERSON] (fine-granularity)\" and \"[PERSON] (granularity stone)\", \"[PERSON] (sandstone)\" and \"[PERSON] (stone with)\". The resolution of the intersecting conflict is to solve the boundary problem of the conflict characters belonging to the left or to the right. As shown in the figure, the right-side information entropy of \"Xi Li (fine-granularity)\" is higher than the left-side information entropy of \"[PERSON] Shi (granularity stone)\", which shows that the character \"Li (granularity)\" has a higher internal tightness and external degree of freedom when it becomes the right boundary than the left boundary. It is better to choose \"Xi Li (fine-granularity)\" as the word segmentation result. Similarly, the right boundary of \"Sha Yan (sandstone)\" has a higher degree of freedom, which yields a better word segmentation result. There is no benchmark value for information entropy, and the numerical distribution intervals of specific fields and different texts are also different. It is difficult to artificially set a suitable threshold based on knowledge to adapt to different word segmentation requirements.