The Application Of Deep Semantic Feature Extraction Based On Bert In The Analysis Of Hierachical Structure Of Complex Sentences

Nowadays,the society is stepping into a big data era.With the rapid development of the Internet industry,all walks of life are directly or indirectly enjoying the convenience brought by big data,which is the result of technological innovation and the crystallization of human continuous pursuit of progress.In this era,the urgent demand for information and the active exploration in the field of artificial intelligence make the field of natural language processing develop rapidly,and more and more applications are recognized by users,such as voice recognition,text translation,information retrieval and question answering system.Natural language processing,also known as natural language understanding,is an important branch of artificial intelligence,and Chinese information processing is an important branch of natural language processing,which plays an important role.At present,the research of Chinese information processing in "word" and "word" has been more mature,but there is still a need for further research in sentence text.The main research object is complex sentence,which is composed of multiple clauses.Compared with single sentence,complex sentence has more complex structure and various meanings.Therefore,the research on complex sentence has great challenges and high research significance.Because of the complexity of complex sentence structure,the analysis of hierarchical Chinese complex sentence structure has great significance in understanding the semantics of Chinese complex sentence.In the aspect of recognition of Chinese complex sentence hierarchy,based on the current research results and experience at home and abroad,this paper mainly describes the recognition method of complex sentence hierarchy using Bert neural network method and attention mechanism.The main content of this paper includes the following parts:first,it describes the method of dividing complex sentences accurately;it obtains the syntactic features of complex sentences through dependency syntax,and then obtains the sentence vector through model training to obtain the semantic features of sentences.Here,Bert is used to replace word2vec,because word2vec has huge disadvantages in capturing context information,which is lost There's too much information,and Bert can solve this problem very well.At the same time,because of the diversity of complex sentence semantics,word meaning disambiguation in feature extraction is also an essential means.Due to Bert's good generalization ability,the data volume is relatively not too large.By using Bert and CNN to carry out comparative experiments,the accuracy of hierarchy analysis has reached 85.6%,basically reaching the expected effect of this experiment.It reflects the validity of the deep semantic feature extraction method based on Bert in the analysis of complex sentence hierarchy.