[Retracted] Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Table 2

Treatment of 10 experiments.

Experiment number	Processing situation
1	Baseline segmentation results without any ambiguity
2	Baseline segmentation results without any ambiguity
3	Freqword first uses the vocabulary to segment the test corpus, then counts the word frequency information in the test corpus after segmentation, and finally uses the new statistical word frequency information to segment the test corpus
4	Freq only uses string frequency to process ambiguous segmentation results
5	Freq + zi_havg uses a single-word boundary entropy judgment when MOAS consists of 3 characters, and more than 3 characters use Freq judgment
6	mi uses only mutual information
7	mi_zi + havg uses single-word boundary entropy when MOAS is composed of 3 characters, and mutual information is used for more than 3 characters
8	word_havg segmentation results use string boundary entropy to determine the likelihood of word formation
9	word_hseparate uses only string boundary entropy to judge string separation results
10	word_havg + zi_havg uses the boundary entropy of a single word when MOAS is composed of 3 characters and uses the boundary entropy of a string to determine more than 3 characters