A kind of nlp recognition method, device and terminal for teaching system
A technology of teaching system and recognition method, which is applied in the field of NLP recognition method, device and terminal of the teaching system, to achieve the effect of promoting further development and improving efficiency
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Embodiment 1
[0022] refer to figure 1 , shows a flow chart of steps of an NLP recognition method used in a teaching system according to Embodiment 1 of the present application.
[0023] It is worth noting that the steps S101 to S102 described in this application do not represent the order in which they are executed.
[0024] The NLP recognition method for the teaching system of the present embodiment comprises the following steps:
[0025] Step S101: Perform an NLP recognition process on the acquired recognition object according to the pre-stored mathematical symbol table and error comparison table, and determine the location of the calculation error in the recognition object.
[0026] Specifically, the mathematical symbol table and the error comparison table described in the embodiment of the present application are collected manually or obtained by using a machine learning algorithm.
[0027] The identification objects include: test papers, homework, exercise questions, answers, and ot...
Embodiment 2
[0034] The NLP identification method used in the teaching system of this embodiment includes the above steps S101 to S102.
[0035] refer to Figure 2a , shows a flow chart of step S101 of an NLP recognition method used in a teaching system according to Embodiment 2 of the present application.
[0036] It is worth noting that the steps S1011 to S1014 described in this application do not represent the order in which they are executed.
[0037] Wherein, the step S101 includes at least one of the following:
[0038] Step S1011: Divide the sentence in the recognition object into multiple lexical units, and determine the illegal lexical units as locations where calculation errors occur.
[0039] In a specific implementation of the present application, the step S1011 described in the embodiment of the present application is specifically:
[0040] Splitting the sentence in the recognition object into multiple lexical units, detecting the correctness of the words or / and arithmetic ...
Embodiment 3
[0052] This embodiment includes the above steps S101 to S102. It is worth noting that the steps S1021 to S1022 described in this application do not represent the order in which they are executed.
[0053] see image 3 , the step S102 includes:
[0054] Step S1021: According to the calculation method of the position where the calculation error occurs in the recognition object, determine the type of possible calculation error.
[0055] The types of calculation errors vary depending on the calculation method, such as carry errors in addition, and borrow errors in subtraction. Therefore, the type of calculation error is related to the calculation method, and the type of calculation error can be obtained according to the calculation method that may occur.
[0056] In the specific implementation of this application, step S1021 described in the embodiment of this application includes:
[0057] According to the number of digits of the calculation formula at the position where the ...
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