# Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction
## 4 Approach
### Implementation of Post-Processing
#### 4.2.1 Features Set  
After analyzing the characteristics of Chinese Spelling Error Correction and human behavior when they perform these problems, we finally determined more than ten features, such as **sos** and **sop** for the classifier to screen the candidate character. Furthermore, we gave a schematic diagram of the replacement suggestion feature (\(\
ucrightharpoons\rightarrow\
ucrightharpoons\), provided in Figure 1). Probability and Rank  
In the Chinese Spelling Error Correction task, the model gives a candidate set (1) that contains all possible characters in the vocabulary whose size is **V** for each original character in the sentence. Based on a given context, the model obtains each character's probability in the candidate set, appearing here through the softmax function. The sum of all characters' possibilities is 1. In theory, characters with higher probability are more likely to become target characters. As we mentioned in Section 2, various BERT-based algorithms simply choose the character with the largest probability value as the correction output. We agreed that the character's probability should be one of the most important features, but it should not be the only one. Rank of probability does also matter. The different specific values of the probability affect the classification result. For example, there are two corrected candidate characters, which have the highest probability. Their corresponding possibilities  
Figure 1: An example of introducing post-processing operation. The base model (the upper part of the figure) made a unnecessary change \(\
ucrightharpoons\) to \(\
ucrightharpoons\) after correcting this sentence. However, for the PoP model (the lower part of the figure), we proposed avoided this change. are \(pro1=0.9\) and \(pro2=0.3\), respectively. At this time, the classification model tends to judge the former (\(pro1=0.9\)) as correct, but the latter (\(pro2=0.3\)) as an error because \(pro1\gg pro2\) in this type of attribute. To make the model no longer affected by the different quantization values of probabilities, we ranked each character in the candidate set (1) according to the probability value from largest to smallest (rank = 1, 2, 3, 4,...). In the previous example, the rank value corresponding to the two correct characters is \(rank=1\) because they have the highest probability, which satisfies the property that samples of the same category in the classification model have the same characteristics. Due to the Chinese text's errors providing helpful information for correction, we took the original character as a candidate character and look for the candidate set at that position to obtain its corresponding probability and rank, under the condition that the feature values of the candidate character are \(canPro=0.5\) and \(canRank=1\). While the original typo's features are \(originalPro=0.1\) and \(originalRank=89\), respectively, the model believes that this candidate character is a reasonable correction. While these two features are 0.48 and 2, the model must carefully consider whether the error correction is appropriate. We used the **pro** and **rank** features to represent the probability value and rank value of probability; Figure 2 shows an example of these two features' calculation. \[C=[c_{1},c_{2},c_{3},...],\quad len(C)=V;\quad\sum_{i=1}^{V}p(c_{i})=1 \tag{1}\]  
Phonetic and Morphological Similarity  
Chinese characters are one of the oldest writing languages and have been developed and changed over the last three thousand years, from the original pictographic characters to today's simplified characters. Each character's shape and pronunciation are more or less related to the meaning of itself. There are similar pronunciations or forms between characters with related meanings. Observing the errors in Chinese text, we can find that most of the cases are that the error character and the correct character belong to the category of near-phonetic characters or near-form characters.