Synonymous Sentence Recognition Based on Machine Translation and BERT-Whitening
[Purpose/significance]Constructing a sentence synonym recognition model combining machine translation and BERT-Whitening can improve the effect of synonym sentence recognition and provide support for downstream information resource manage-ment and service applications.[Method/process]Firstly,the types and characteristics of synonymous sentences are analyzed,and on this basis,a synonymous sentence recognition model integrating machine translation and BERT-Whitening is constructed,and the ef-fect of the model is verified through experiments.The recognition model consists of four elements:sentence preprocessing,candidate synonym recognition,embedded text representation and synonym judgment based on similarity fusion.[Result/conclusion]The experi-mental results show that the accuracy,recall and F1 of the combined model of machine translation and BERT-Whitening are 0.840,0.859 and 0.849,respectively,significantly higher than the control Group.[Innovation/limitation]Lack of text verification in highly specialized fields,insufficient universality verification,and significant room for improvement in accuracy and recall.
NETL
NSTL
万方数据
