Character string explaining method based on dictionary platform and dictionary platform

Abstract

The invention relates to a character string explaining method based on a dictionary platform and a dictionary platform. The character string explaining method comprises the following steps of: S1, receiving a character string input by a user; S2, matching the character string with at least one pre-input dictionary, obtaining at least one explaining information, and outputting by virtue of a primary display interface; and S3, operating the primary display interface, controlling the display or concealing of a secondary display interface, wherein the secondary display interface outputs a management operation key for the dictionary. By adopting the method, the character string of various languages can be explained, a self-learning function can be realized, multiple dictionaries can be input, and various languages can be translated; and the explaining information of the character string information and relevant information of the dictionary are displayed by virtue of a way of the primary display interface and the secondary display interface, so that all or partial information can be checked by a user, and the character string is rapider to translate.

Description

technical field [0001] The invention relates to a character string interpretation method based on a dictionary platform and the dictionary platform. Background technique [0002] In the traditional method, the use of the dictionary is usually fixed, and the character string to be explained is obtained through user input, and then searched in the dictionary after importing. Automatic corrections and text predictions for user-entered strings are produced using a static language model, which can be open with test simulations and hard-coded on-device, and users can explicitly add words to the model or ignore words, but otherwise static language models may not be appropriate for specific users and interaction scenarios. Consequently, text prediction candidates provided using conventional techniques are often inappropriate or irrelevant to the user and / or context, which can lead to frustration and lack of trust in the predictions. Contents of the invention [0003] The technic...

AI Technical Summary

Problems solved by technology

Automatic corrections and text predictions for user-entered strings are produced using a static language model, which can be open with test simulations and hard-coded on-device, and users can explicitly add words to the model or ignore words, but otherwise static language models may not be appropriate for specific users and interaction scenarios

Consequently, text prediction candidates provided using traditional techniques are often inappropriate or irrelevant to the user and / or context, which can lead to frustration and lack of trust in the predictions

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