Text correction method and user equipment

Abstract

An embodiment of the invention provides a text correction method and user equipment and relates to the language processing field. The text correction method and the user equipment can reduce correction mistakes and improve correction flexibility and correction accuracy. The text correction method comprises the steps of obtaining more than two text types of text to be corrected in the preset text classification standard, obtaining a sub-language-module to be combined, corresponding to each text type, of the text to be corrected in a correction knowledge base, mixing more than two obtained sub-language-modules to be combined into a hybrid language model, and correcting the text to be corrected according to the hybrid language model so as to obtain correction suggested text. The text correction method and the user equipment are used for correcting wrong text.

Description

technical field [0001] The present invention relates to the field of language processing, in particular to a text correction method and user equipment. Background technique [0002] With the advent of the digital age, the text correction technology to correct the wrong text to be corrected is more and more widely used. In the prior art, in the noise channel theory, it is considered that the errors of the text to be corrected mainly come from the input errors generated in the process of manual input, and the input errors generated in optical character recognition and speech recognition. In the noise channel theory, these texts to be corrected are regarded as real texts produced after passing through a channel mixed with noise. For example, W is the original string sequence <W 1 , W 2 ,...,W n >, that is, the completely correct text, produces noise text after passing through the noise channel <O 1 , O 2 ,...O 3 >, the method of text correction using the nois...

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Problems solved by technology

[0003] In the method of text correction using noise channel theory, it is necessary to obtain the string W' that maximizes the probability of P(W)P(O|W) according to the language model. However, when the language environment and theme background of the text to be corrected are different At the same time, the same words or character strings may have different meanings, so different correction options are required, but the language model in the prior art is relatively fixed, and only fixed correction options can be used for the text to be corrected, so correction errors are prone to occur, resulting in Calibration flexibility is poor and accuracy is low

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