Sentence Vectorization Modeling And Text Level Application

Sentence vectorization modeling of words or texts is an important research field in Natural Language Processing(NLP).Vector representation is a common processing tool in NLP applications.By using vector space model,word or text is mapped into vector and then applied to a specific natural language processing application.In recent decades,the research on natural language processing has made great progress.For text like words or phrases,word embedding makes it possible to express the meaning of words with vectors.The use of traditional inverse frequency of word frequency makes it possible to model the whole vector quantitatively.However,in the increasingly complex application scenarios,with the text vector modeling requirements gradually improved,a reasonable model of the sentence is becoming more and more important.However,the methods used in the present study have not yet yielded accurate results.The main reason is the complexity of the statement and difficulty of the corresponding semantic capture,more accurate and reasonable representation of the sentence has an important impact on the natural language processing research.In this paper,we analyze the sentence vectorization modeling and introduce a method of embedding sentence and segment information to improve the accuracy of sentence modeling through additional information.The motivation of this method is to enhance the expressive ability of word vectors by capturing more context information,and then combine the word vectors to obtain the corresponding sentence vectors.The enhancement of the expression capacity of word vectors also brings the improvement of the ability of sentence vector representation.In this paper,the sentence modeling method is used in the related application system,and the result of machine translation experiment proves that the sentence vector modeling method can capture more context information of the sentence and also achieve a better effect of sentence modeling.