Chinese reading difficulty grading method and system based on machine learning

Abstract

The invention discloses a Chinese reading difficulty grading method and system based on machine learning. In the grading method, training samples can be updated in real time so that the feature that language changes along with times is taken into consideration and therefore the Chinese difficulty grading table and the word frequency table can be updated. The introduction of features such as semantic, sentences, texts and subjects makes it more objective by using the above features, sentence length and word length as the index of complexity and therefore structural property can be accurately reflected. By using feature set to make up for the lack of a few shallow local linguistic features, it can reflect the real process of reading comprehension and classify the reading difficulty level more accurately. By this method, the reading difficulty grading technique can be applied to Chinese, which accords with the language characteristics of Chinese. The grading system comprises a text obtaining unit, a constructing unit and a training and predicting unit, which realizes the same beneficial effects of the grading method for Chinese text reading difficulty.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a method and system for grading Chinese reading difficulty based on machine learning. Background technique [0002] Artificial intelligence technology refers to understanding the essence of intelligence and producing a new kind of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence often achieves text reading difficulty classification through the combination of technologies such as natural language processing and machine learning and linguistic research results. [0003] Grading of reading difficulty can essentially be summarized as a measure of text readability. By definition, readability refers to the degree to which a text is easy to read and understand. Usually, highly readable text content matches the reader's background knowledge, and will appropriately paraphrase the above content and provide relevant knowledge; more...

AI Technical Summary

Problems solved by technology

[0005] The disadvantage of the existing technology is that it does not consider the characteristics of language changes with the times, and thus cannot update the Chinese character difficulty classification table and word frequency table; only using sentence length or word length as an indicator of complexity is too intuitive and cannot accurately reflect its Structural nature; a small number of shallow-level local language features are not enough to reflect the real reading comprehension process; reading difficulty classification technology is only applicable to English, and English itself and Chinese are very different in language features

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