Research on Natural Language Processing Technology Based on Deep Learning
In order to ensure the quality of natural language mining processing for English vocabulary and Chinese corpus text,we use word vector model and improved Apriori model algorithm to map the same type of words into an independent vector space according to the correlation between different word features,make lexical annotation,lexical chunking,and onomastic identification of words or phrases,and then use improved Apriori dynamic association rule algorithm to evaluate the candidate items of English or Chinese corpus,and then use the improved Apriori dynamic association rule algorithm to evaluate the candidate items of different types of words and phrases.Subsequently,using the improved Apriori dynamic association rule algorithm,different types of datasets are preprocessed,analyzed in terms of support and confidence,and words or characters with frequency greater than the minimum support are added to the corresponding frequent itemsets,so as to derive the association relationship between different itemsets in the network database,and the mined textual data in natural language are cleaned(screened),extracted and categorized and stored to ensure that the English or Chinese participle retrieval,lexical characterization,and lexical identity of words or phrases can be achieved in an efficient and effective way.We also clean(screen),extract and categorize the mined natural language text data to ensure the accuracy of English or Chinese word segmentation search,lexical meaning recognition,picture recognition and categorization storage.
deep learningword vector modelingimproved Apriori modeling algorithmnatural language processing
万方数据
