Solving Text Transmission Errors with N-gram and Binary Tree Models

This paper explores solutions for addressing transmission errors in text copying by leveraging two suitable models based on the specific characteristics of the problem. For the first issue, we opted for the N-gram model due to its ability to enhance the efficiency and accuracy of text processing and natural language processing, making it a suitable fit for this scenario. Problem two, however, calls for a binary tree data structure-based text transmission model. While this model exhibits a higher time complexity, its exceptional accuracy makes it the preferred choice for solving problem two. To further refine the models, we systematically quantified the features of the four prevalent error types presented in the problem statement.

For problem one, we employed the N-gram model to perform Chinese text comparison. By incorporating the Markov assumption, we successfully addressed the issue of a large parameter space. Utilizing Python's 're' and 'collections' modules for comparing two Chinese texts, we demonstrated the N-gram model's ability to accurately identify discrepancies. This model stands out for its capability to capture language patterns and word order relationships, offering the advantages of simple computation and fast processing speed. However, its limitations lie in its inability to consider semantic relationships and its relatively weak performance when handling rare words and specialized terminology.

In addressing problem two, we implemented a text transmission model for contrasting Chinese texts. This model estimates text transmission times by calculating the least common ancestor (LCA), the longest common prefix (LCP) of two nodes, and the nodes on the path between them. Notably, this model is capable of calculating transmission times between any two nodes on any binary tree, achieving a time complexity of O(n). We further refined the algorithm by incorporating the characteristics of four common error types. The improved algorithm demonstrates superior performance in identifying '讹', '脱', and '衍' category errors. However, it exhibits relatively weaker performance in recognizing '倒' category errors. Testing on 20 articles, manually modified to include a total of 80 errors, yielded consistent results with a misjudgment count ranging from 0 to 4, achieving an accuracy rate of 95% to 100%.

Regarding problem three, we revised the models developed for problems one and two to ensure their practical applicability in addressing real-world scenarios. We conducted a thorough analysis of the principles, solution processes, application scenarios, advantages, and disadvantages of the two models. The paper provides revised code, running speed estimates, and practical examples. We are confident that the models constructed in this paper offer significant reference value and practical utility for corresponding problems encountered in real-world situations.