Fine-grained and precise alignment method for multilingual texts based on deep semantics

Abstract

The invention discloses a fine-grained and precise alignment method for multilingual texts based on deep semantics, which belongs to the field of text alignment. The fine-grained and precise alignment method for multilingual texts based on deep semantics gradually calculates words, sentences, paragraphs and chapters by extracting word embedding features Semantic similarity, so as to gradually improve the accuracy of text fine-grained alignment. At the same time, in the alignment process, through the construction of fine-grained light arrays, after each step, the air expansion tube is inflated in the radial direction and vertical direction respectively by inflation. and elongation, so that the fluorescent liquid enters the mask, lights up the lamp ball corresponding to the aligned word embedding feature, and when it is lit for the second time or the last few times, because the inflation time gradually becomes longer, the lit lamp The downward extension of the ball is getting longer and longer, and the brightness is getting brighter, so that after each step, the improvement of the accuracy of fine-grained alignment is more visually reflected, further assisting students to speed up their understanding of this content speed.

Description

technical field [0001] The invention relates to the field of text alignment, and more specifically, to a method for fine-grained and precise alignment of multilingual texts based on deep semantics. Background technique [0002] Entity linking is the process of mapping entity references in natural language to correct candidate entities in a knowledge base. In layman's terms, the fine-grained model is to subdivide the objects in the business model to obtain a more scientific and reasonable object model. Intuitively speaking, it is to divide many objects. [0003] The memory space in modern computers is divided by byte. In theory, it seems that access to any type of variable can start from any address, but the actual situation is that when accessing a specific variable, it is often accessed at a specific memory address, which is All types of data need to be arranged in space according to certain rules, rather than sequentially arranged one by one, which is alignment. [0004]...

AI Technical Summary

Problems solved by technology

[0006] When performing fine-grained alignment between two or more texts in multiple languages, due to the differences in grammar and expression habits between languages, there are certain deviations in the calculation of semantics after mutual translation, and then alignment, resulting in The accuracy of fine-grained alignment is affected. When performing fine-grained alignment between two or more texts in the same language, due to differences in polysemous words or emotional words with strong subjectivity, when word embedding features are used for semantic calculation, Some of the results of fine-grained alignment are difficult to align, which also affects the accuracy of fine-grained alignment. In addition, in the prior art, the results of fine-grained alignment are usually only displayed through data. In special occasions, especially in colleges and universities, When teaching this aspect, the results are relatively abstract, resulting in low efficiency for students to understand

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