Text error correction method, device and equipment and storage medium

Abstract

Embodiments of the invention disclose a text error correction method, device and equipment, and a storage medium, which are applied to the field of digital medical treatment, and the method comprisesthe steps of obtaining text features of initial text information, and determining a to-be-retrieved text according to the text features; determining target domain knowledge from a domain think librarybased on the to-be-retrieved text; and generating a to-be-corrected text sequence according to the to-be-retrieved text and the target domain knowledge, and performing error correction on the to-be-retrieved text based on the to-be-corrected text sequence to realize error correction of the initial text information. By adopting the embodiment of the invention, domain knowledge can be associated toperform error correction on the text, and the text error correction accuracy is improved.

Description

technical field [0001] The present application relates to the field of natural language processing, and in particular to a text error correction method, device, equipment and storage medium. Background technique [0002] Text error correction is the process in which the retrieval system perceives the errors in the text to be retrieved entered by the user, and corrects it into the correct text to be retrieved according to the user's search intention. As an indispensable part of the retrieval system, text error correction is mainly composed of text error detection and error correction in the field of language processing. The existing technology mainly uses rule-based algorithms for error correction or deep learning The model is corrected. However, the rule-based algorithm performs error correction only based on the literal features of the text to be retrieved, and does not take into account the field of the text to be retrieved, which is likely to cause the semantic change of...

AI Technical Summary

Problems solved by technology

However, the error correction based on the rule-based algorithm is only based on the literal features of the text to be retrieved, and does not take into account the field of the text to be retrieved. It is easy to cause the problem that the semantics of the text to be retrieved after text error correction will change, and the applicability is poor.

The error correction algorithm based on the deep learning model requires a lot of training, but it is difficult to perform effective text error correction when the text to be retrieved is too short or the training corpus is insufficient

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