The fuzziness and ambiguity of natural language increase the difficulty of feature extraction.To accu-rately extract natural language features,a method of extracting natural language features based on fuzzy association op-timization was proposed.Initially,uncertain natural language information was described by using a ternary language representation model.Then,an initial membership function(MF)was provided.Next,maximizing the support of fuzzy item sets and semantic interpretability were set as the fitness function.Moreover,the Group Search Optimization(GSO)algorithm was adopted to obtain the optimal MF.Furthermore,natural language information was mined by the optimized fuzzy association rule algorithm.After that,the generation function and restriction function were added to the attention mechanism.Meanwhile,the traditional attention mechanism was improved.On this basis,the extraction of natural language features was completed.Simulation results show that the proposed method can achieve high-preci-sion and high-coverage natural language feature extraction.
维普
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