Multiple strategies and fuzzy comprehensive evaluation were used to recognize term similarity relation types. First, a variety of similarity algorithms were used to calculate the term similarities, and then the relations and intervals were identified by continuous attribute discretization algorithm. The sample distribution probability was used to determine the membership degree of the interval to the relations, and the weight of elements were determined by particle swarm algorithm and cross validation method. Then, all the calculation results were combined using a fuzzy comprehensive evaluation method to recognize the term similarity relation types. Finally, the precision, recall, and F value were used to evaluate the effect of the results, and the results were compared to the experimental results of the SVM to demonstrate the effectiveness of this method. This experiment regarded the Chinese scientific and technical vocabulary system (new energy vehicles) as the test set. The results showed that the method was able to recognize the term similarity relation types effectively.
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