Multi-language text fine-granularity accurate alignment method based on depth semanteme

Abstract

The invention discloses a multi-language text fine-granularity accurate alignment method based on depth semanteme, and belongs to the field of text alignment, the multi-language text fine-granularity accurate alignment method based on depth semanteme comprises the following steps: extracting word embedding features, gradually calculating words, statements, paragraphs and semantic similarity of the paragraphs, consequently, gradually improving accuracy of text fine-grained alignment. Meanwhile, in the alignment process, through construction of a fine-grained lamp array, after each step, an inflatable tube is expanded and stretched in the radial direction and the longitudinal direction through inflation, then fluorescent liquid enters a photomask, lamp balls corresponding to aligned word embedding characteristics are lightened, and when the light ball is lighted for the second time or the last several times, the inflation time is gradually prolonged, the downward extension amount of the lighted light ball is increased, and the brightness is increased, so that the improvement of the accuracy of fine-grained alignment is more obvious in visual representation after each step, and the students are further assisted in accelerating the understanding speed of the content.

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

Images