# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 4 Experimental Process
### Results and Analysis
#### 4.3.2 Error Analysis of Cases  
To conduct a detailed analysis of the errors, we randomly selected 100 test entry datasets, which included 978 entity instances and 489 relationship instances, respectively. We identified approximately 196 error cases, with 87 entity and 109 relationship errors, accounting for errors at about 44% and 56%, respectively. We categorized the entity and relationship data into several types based on different error causes. Figure 7 illustrates the specific distributions of these errors. As this shows, classification errors can generally be summarized as follows (for entity extraction, there are several types of errors):  
(1) The original entity is not extracted or just partially extracted: This is the most common type of error in entity extraction and is often encountered with more complex entities that have complete sentence structures, including punctuation marks or conjunctions, for example, \"and.\" The model tends to break down these entities into smaller units. Due to the low absolute number of such complex entities and the difficulty in finding similarities among them, the learning performance of features in the model is suboptimal. (2) Misclassification: The entity is correctly extracted but is assigned to the wrong category. This error often occurs between two-entity categories that have some similarities, such as \"Symptom\" and \"Pathological Staging,\" which both describe parts of a disease, leading to misclassification when processing the data. However, clarifying the definition of each category during annotation should help reduce these occurrences. (3) Extraction of superfluous entity information: Technically, an error has occurred. Yet, when the errors are reviewed, some of these are acceptable. For example, the model extracts \"liver function\" as \"liver function test,\" which does not change the information contained in the entity and does not affect the extraction process of the subsequent relationship. Therefore, it may be possible to incorporate them into the knowledge graph through some semantic similarity calculations, since such similarities are difficult to define under a category. For relationship extraction, there are several types of errors:  
(1) Relationship errors due to entity errors: This is the most common type of error in relationship extraction. Since relationship extraction is performed after entity extraction, the accuracy of entity extraction directly affects the outcome of relationship extraction. If there are errors or omissions in entity extraction, it will directly affect the process of relationship extraction. In other words, relationship extraction errors are caused by the relationship categories, for instance, \"Synonyms\" and \"Correlation.\" To illustrate, after two entities A and B are connected with the relationship \"Synonyms\" or \"Correlation,\" the model tends to consider these two entities as equivalent and attribute the other relationship information contained in entity A equally to entity B. This approach is generally acceptable when it comes to the \"Synonyms\" category, but it can cause significant problems in the \"Correlation\" category. Unlike \"Synonyms,\" the two entities associated with the \"Correlation\" category cannot replace each other; instead, they may just be two different types that are more likely  
Figure 7: Error types for entity and relationship. to appear together. Therefore, we believe that the definition of the \"Correlation\" category needs further refinement. (2) Misclassification: The entities are correctly extracted but are assigned to the wrong relationship category. This error often occurs between two relationship categories that have some similarities, such as \"Epidemiological\" and \"Sociological,\" which both describe parts of the causes of disease outbreaks, leading to misclassification when processing the data. (3) Relationships not extracted: The entities are correctly extracted, but the corresponding relationships are not identified based on the entities.