In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based ...In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based on association rules, English vocabulary data storage model is constructed, a two element linguistic feature function is constructed for describing the directionality of English lexical retrieval scheduling, English vocabulary classification decision making model is constructed based on contextual relations of English vocabulary, the features of the association rules of English vocabulary are extracted, the adaptive learning method is used to realize the automatic classification of English vocabulary. The simulation results show that the method of English vocabulary classification has good performance, the classification error rate is low, the retrieval precision is high, and the computational overhead is small.展开更多
文摘In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based on association rules, English vocabulary data storage model is constructed, a two element linguistic feature function is constructed for describing the directionality of English lexical retrieval scheduling, English vocabulary classification decision making model is constructed based on contextual relations of English vocabulary, the features of the association rules of English vocabulary are extracted, the adaptive learning method is used to realize the automatic classification of English vocabulary. The simulation results show that the method of English vocabulary classification has good performance, the classification error rate is low, the retrieval precision is high, and the computational overhead is small.
