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- W2919752502 abstract "When using traditional knowledge retrieval algorithms to analyze whether the feature input of words in multi-modal natural language library is symmetrical, the symmetry of words cannot be analyzed, resulting in inaccurate analysis results. A feature input symmetric algorithm of multi-modal natural language library based on BP (back propagation) neural network is proposed in this paper. A Chinese abstract generation method based on multi-modal neural network is used to extract Chinese abstracts from images in multi-modal natural language library. The Word Sense Disambiguation (WSD) Model is constructed by the BP neural network. After the word or text disambiguation is performed on the Chinese abstract in the multi-modal natural language library, the feature input symmetry problem in the multi-modal natural language library is analyzed according to the sentence similarity. The experimental results show that the proposed algorithm can effectively analyze the eigenvalue symmetry problem of the multi-modal natural language library. The maximum error rate of the analysis algorithm is 7%, the growth rate of the analysis speed is up to 50%, and the average analysis time is 540.56 s. It has the advantages of small error and high efficiency." @default.
- W2919752502 created "2019-03-11" @default.
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- W2919752502 date "2019-03-07" @default.
- W2919752502 modified "2023-09-27" @default.
- W2919752502 title "Feature Input Symmetry Algorithm of Multi-Modal Natural Language Library Based on BP Neural Network" @default.
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- W2919752502 doi "https://doi.org/10.3390/sym11030341" @default.
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