English text processing method and system, readable storage medium and computer

Abstract

The invention provides an English text processing method and system, a readable storage medium and a computer. The method comprises the steps of performing text splitting on a to-be-processed English text to obtain a plurality of to-be-processed statements; respectively calculating the statement coincidence degree and the vector similarity of the statement vectors converted by the statement coincidence degree by utilizing the statement analysis model; the word frequency dictionary and the word frequencies of all the words in the English text to be processed are used for calculating the word occurrence rate; performing text screening on the to-be-processed English text according to the statement coincidence degree, the vector similarity and the vocabulary occurrence rate to obtain an English marked text and an English screened text; and performing text processing on the English marked text by using the text processing model, and performing text combination on the English marked text after text processing and the English screened text to obtain a correct English text. According to the method, the abnormal vocabularies in the English text to be processed are eliminated according to the statement coincidence degree, the vector similarity and the vocabulary occurrence rate, and the text processing efficiency and accuracy are improved through multiple indexes.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an English text processing method, system, readable storage medium, and computer. Background Technology

[0002] With the continuous advancement of education reform, people's attention to education is also rising. As the cornerstone of talent cultivation, the information technology processing of the education industry has become a trend.

[0003] English is one of the most commonly learned languages ​​in the current education industry. Listening, speaking, reading, and writing are the basic skills of English. Writing in English usually involves expressing specific meanings through words. Current methods for processing English text typically involve shallow feature extraction from a word semantic database and constructing regression analysis using human scoring. The constructed regression model is then used to calculate the score of the English text. However, since different words in a text can have multiple meanings, and the meaning of a text can vary depending on the vocabulary used, simply extracting features from a word semantic database can lead to inconsistent recognition results. Furthermore, when there is a large amount of English text, human scoring requires extensive learning, resulting in low efficiency. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an English text processing method, system, readable storage medium, and computer to at least address the shortcomings of the aforementioned technologies.

[0005] This invention proposes an English text processing method, comprising: The process involves acquiring the English text to be processed uploaded by the user terminal and then decomposing the English text to obtain multiple sentences to be processed. Construct a statement analysis model and use the statement analysis model to calculate the statement overlap of each statement to be processed; Each of the statements to be processed is converted into a statement vector, and the vector similarity of each statement vector is calculated using the statement analysis model. The word frequencies of all words in the English text to be processed are statistically analyzed, and the word occurrence rate among the words is calculated using a word frequency dictionary and the word frequency of each word. The English text to be processed is filtered based on the sentence overlap, vector similarity, and word occurrence rate to obtain the tagged English text and the filtered English text. The English marked text is processed using a preset text processing model, and the processed English marked text is combined with the English filtered text to obtain the correct English text.

[0006] Furthermore, the steps for constructing a statement analysis model include: Obtain the terminal type of the user terminal, and obtain the corresponding sample data based on the terminal type; The statement filtering rules corresponding to the terminal type are invoked, and the sample data is filtered based on the statement filtering rules to obtain the sample filtered data; The sample screening data is used to construct a statement analysis model corresponding to the terminal type.

[0007] Furthermore, the step of calculating the statement overlap of each of the statements to be processed using the statement analysis model includes: Extract the key characters of each statement to be processed, and obtain the analysis statement corresponding to each statement to be processed in the statement analysis model based on the key characters of the statement; The overlap degree of each statement to be processed and its corresponding analysis statement is calculated to obtain the statement overlap degree of each statement to be processed.

[0008] Furthermore, the formula for calculating the statement overlap is as follows: ; In the formula, Indicates the first One statement to be processed. Indicates the first The analysis statements corresponding to each statement to be processed. Indicates the first The length of the longest common character among the statements to be processed and their corresponding parsed statements. Indicates the first The total length of the characters in the statements to be processed. Indicates the first The total length of the analysis statements corresponding to each statement to be processed.

[0009] Furthermore, the step of converting each of the statements to be processed into a statement vector includes: Each of the statements to be processed and its corresponding analysis statements is vectorized to obtain the corresponding statement vector and its corresponding analysis statement vector. The vector of the statement to be processed and its corresponding analysis statement vector are generalized to obtain a multidimensional statement vector and its corresponding multidimensional analysis statement vector.

[0010] Furthermore, the formula for calculating the vector similarity of the statement vectors is as follows: ; In the formula, Indicates the first The statement to be processed undergoes vector transformation and vector generalization to obtain a multidimensional statement vector. ; express The corresponding multidimensional analysis statement vector, .

[0011] This invention also proposes an English text processing system, comprising: The text decomposition module is used to acquire the English text to be processed uploaded by the user terminal and decompose the English text to be processed into multiple sentences to be processed. The overlap calculation module is used to construct a statement analysis model and use the statement analysis model to calculate the overlap of each statement to be processed. The similarity calculation module is used to convert each of the statements to be processed into statement vectors, and to calculate the vector similarity of each statement vector using the statement analysis model; The word frequency calculation module is used to count the word frequency of all words in the English text to be processed, and to calculate the word occurrence rate between each word using a word frequency dictionary and the word frequency of each word. The text filtering module is used to filter the English text to be processed based on the sentence overlap, the vector similarity, and the word occurrence rate, so as to obtain the English tagged text and the English filtered text. The text processing module is used to process the English marked text using a preset text processing model, and then combine the processed English marked text with the English filtered text to obtain the correct English text.

[0012] Furthermore, the overlap calculation module includes: The sample data acquisition unit is used to acquire the terminal type of the user terminal and acquire corresponding sample data based on the terminal type. The statement filtering unit is used to call the statement filtering rules corresponding to the terminal type, and to filter the sample data based on the statement filtering rules to obtain the sample filtered data. The model building unit is used to construct a statement analysis model corresponding to the terminal type using the sample screening data.

[0013] Furthermore, the overlap calculation module includes: The character extraction unit is used to extract the key characters of each of the statements to be processed, and to obtain the analysis statement corresponding to each of the statements to be processed in the statement analysis model based on the key characters of the statements. The overlap calculation unit is used to calculate the overlap between each of the statements to be processed and its corresponding analysis statement to obtain the statement overlap of each of the statements to be processed.

[0014] Furthermore, the similarity calculation module includes: The vector conversion unit is used to perform vector conversion on each of the statements to be processed and their corresponding analysis statements to obtain the corresponding statement vector and the corresponding analysis statement vector. The vector generalization unit is used to perform vector generalization on the statement vector to be processed and its corresponding analysis statement vector to obtain a multidimensional statement vector and its corresponding multidimensional analysis statement vector.

[0015] The present invention also proposes a readable storage medium on which a computer program is stored, which, when executed by a processor, implements the above-described English text processing method.

[0016] The present invention also proposes a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described English text processing method.

[0017] The English text processing method, system, readable storage medium, and computer of this invention utilize a sentence analysis model to calculate the sentence overlap of the sentences to be processed, convert each sentence into a sentence vector, calculate the vector similarity of each sentence through the sentence analysis model, calculate the word occurrence rate between each word using word frequency, and perform text filtering on the English text to be processed based on sentence overlap, vector similarity, and word occurrence rate to eliminate abnormal words in the English text to be processed. Multiple indicators are used to improve the efficiency and accuracy of text processing. Attached Figure Description

[0018] Figure 1 This is a flowchart of the English text processing method in the first embodiment of the present invention; Figure 2 for Figure 1 Detailed flowchart of step S102; Figure 3 for Figure 1 A detailed flowchart of another embodiment of step S102; Figure 4 for Figure 1 Detailed flowchart of step S103; Figure 5This is a structural block diagram of the English text processing system in the second embodiment of the present invention; Figure 6 This is a structural block diagram of the computer in the third embodiment of the present invention.

[0019] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0020] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0021] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] Example 1 Please see Figure 1 The figure shows an English text processing method in the first embodiment of the present invention, which specifically includes steps S101 to S105: S101, Obtain the English text to be processed uploaded by the user terminal, and decompose the English text to be processed to obtain multiple sentences to be processed; In practice, users upload the English text to be processed through a user terminal with communication capabilities, such as a mobile phone or tablet. The English text is decomposed according to the sentence structure rules to obtain multiple sentences to be processed. Typically, English text is formed by combining multiple types of words, such as subject, predicate, and object. The decomposition of the English text according to these rules facilitates the judgment of the entire sentence.

[0024] S102, construct a statement analysis model, and use the statement analysis model to calculate the statement overlap of each statement to be processed; For further details, please refer to Figure 2 Step S102 specifically includes steps S1021 to S1023: S1021, Obtain the terminal type of the user terminal, and obtain the corresponding sample data based on the terminal type; S1022, Invoke the statement filtering rules corresponding to the terminal type, and perform statement filtering on the sample data based on the statement filtering rules to obtain sample filtered data; S1023, Construct a statement analysis model corresponding to the terminal type using the sample screening data.

[0025] In specific implementation, the terminal type corresponding to the user terminal is obtained. In this embodiment, the terminal types corresponding to the user terminals used by users of different grades are different. When a user uploads English text to be processed through the user terminal, the grade information of the user can be obtained by obtaining the terminal type of the device terminal. Based on the grade information, all corresponding English words (i.e., sample data) and the sentence filtering rules corresponding to the grade information are obtained. Different grades are exposed to different English words, so the sentence combination methods of different grades are also different. Therefore, by pre-constructing a sentence filtering rule library, the English words of different grades are filtered to obtain the sample database corresponding to different grades.

[0026] For example, when a student in a middle school uploads the corresponding English text to be processed through the school's equipment terminal, the student is identified as a second-year junior high school student based on the terminal type. Then, all English words that a second-year junior high school student can access and all combinations of those words are called up. Using these combinations, all English words are combined into sentences to obtain a sentence database. This sentence database is then used to construct a sentence analysis model corresponding to the terminal type of the equipment terminal.

[0027] In some alternative embodiments, please refer to Figure 3 Step S102 further includes steps S1121~S1122: S1121, Extract the key characters of each statement to be processed, and obtain the analysis statement corresponding to each statement to be processed in the statement analysis model based on the key characters of the statement; S1122, calculate the overlap between each statement to be processed and its corresponding analysis statement to obtain the statement overlap of each statement to be processed.

[0028] In practical implementation, the key characters of each statement to be processed obtained above are extracted, and the corresponding analysis statement is obtained in the constructed statement analysis model using the key characters. The overlap between the analysis statement and the corresponding statement to be processed is calculated to obtain the statement overlap of each statement to be processed. The formula for calculating the statement overlap is as follows: ; In the formula, Indicates the first One statement to be processed. Indicates the first The analysis statements corresponding to each statement to be processed. Indicates the first The length of the longest common character among the statements to be processed and their corresponding parsed statements. Indicates the first The total length of the characters in the statements to be processed. Indicates the first The total length of the analysis statements corresponding to each statement to be processed.

[0029] For example, if the statement to be processed is "1234567" with a character length of 7, the analysis statement obtained through the above method is "1234576" with a character length of 7. The longest common character between the two statements is "12345" with a character length of 5.

[0030] S103, convert each of the statements to be processed into statement vectors, and use the statement analysis model to calculate the vector similarity of each statement vector; For further details, please refer to Figure 4 Step S103 specifically includes steps S1031 to S1032: S1031, Perform vector transformation on each of the statements to be processed and their corresponding analysis statements to obtain the corresponding statement vector and their corresponding analysis statement vector; S1032, the statement vector to be processed and its corresponding analysis statement vector are vector generalized to obtain a multidimensional statement vector and its corresponding multidimensional analysis statement vector.

[0031] In practical implementation, a preset vector transformation algorithm is used to perform two-dimensional vector transformation on each statement to be processed and its corresponding analysis statement to obtain the statement vector to be processed and its corresponding analysis statement vector. The statement vector to be processed and its corresponding analysis statement vector are then generalized to obtain the corresponding multi-dimensional statement vector and its corresponding multi-dimensional analysis statement vector. Among these, the vector is used... Indicates the first The statement to be processed undergoes vector transformation and vector generalization to obtain a multidimensional statement vector. ; express The corresponding multidimensional analysis statement vector, ; Furthermore, the multidimensional sentence vectors obtained above and their corresponding multidimensional analysis sentence vectors are imported into the sentence analysis model described above to calculate the vector similarity of each sentence vector. The formula for calculating the vector similarity of the sentence vectors is as follows: .

[0032] S104, count the word frequency of all words in the English text to be processed, and calculate the word occurrence rate between each word using a word frequency dictionary and the word frequency of each word; In practice, the word frequencies of all words in the English text to be processed are statistically analyzed. The word occurrence rate of each word is calculated using a preset word frequency dictionary and the word frequencies. The word occurrence rate is represented by the presence of four words "A, B, C, D" in a certain sentence X. In order to calculate the probability of sentence X appearing in the English text to be processed, it is necessary to perform probability calculations on the word frequencies of all words and the entire English text to be processed. The resulting probability is the word occurrence rate of the four words "A, B, C, D".

[0033] S105, the English text to be processed is filtered according to the sentence overlap, the vector similarity and the word occurrence rate to obtain the English marked text and the English filtered text; In specific implementation, the data with a sentence overlap less than a preset overlap threshold (in this embodiment, the overlap threshold is 80%, when the sentence overlap is less than 80%, it means that the sentence to be processed has a lot of data that needs to be corrected) is extracted and marked as the first abnormal data; the data with a vector similarity less than a preset similarity threshold (in this embodiment, the similarity threshold is 60%, when the vector similarity is less than 60%, it means that there is a large difference between the sentence to be processed and its corresponding analysis sentence, and a large change is needed to fix it) is marked as the second abnormal data; the data with a word occurrence rate less than a preset occurrence rate threshold (in this embodiment, the occurrence rate threshold is 20%) is removed. The first and second abnormal data are the English labeled text, and the remaining data are the English filtered text.

[0034] S106, The English marked text is processed using a preset text processing model, and the processed English marked text is combined with the English filtered text to obtain the correct English text.

[0035] In practice, a text processing model is pre-built and used to process the aforementioned English tagged text. The text processing model contains various types of erroneous text and their corresponding modified texts. The erroneous text types include partial errors and global errors. Partial errors include part-of-speech errors and errors in various words (nouns, articles, adjectives, etc.). Global errors include grammatical errors and thought errors. The English tagged text is input into the text processing model for text comparison, and the corresponding modified text is used to replace it, thereby obtaining the processed English tagged text. The aforementioned English filtered text is then combined with it to obtain the correct English text.

[0036] In summary, the English text processing method in the above embodiments of the present invention uses a sentence analysis model to calculate the sentence overlap of the sentences to be processed, converts each sentence into a sentence vector, calculates the vector similarity of each sentence through the sentence analysis model, calculates the word occurrence rate between each word using word frequency, and performs text filtering on the English text to be processed based on sentence overlap, vector similarity, and word occurrence rate to eliminate abnormal words in the English text to be processed. Multiple indicators are used to improve the efficiency and accuracy of text processing.

[0037] Example 2 In another aspect, this invention also proposes an English text processing system, please refer to [link / reference needed]. Figure 5 The figure shows an English text processing system according to a second embodiment of the present invention, the system comprising: The text decomposition module 11 is used to acquire the English text to be processed uploaded by the user terminal and decompose the English text to be processed to obtain multiple sentences to be processed. The overlap calculation module 12 is used to construct a statement analysis model and use the statement analysis model to calculate the overlap of each statement to be processed. Furthermore, the overlap calculation module 12 includes: The sample data acquisition unit is used to acquire the terminal type of the user terminal and acquire corresponding sample data based on the terminal type. The statement filtering unit is used to call the statement filtering rules corresponding to the terminal type, and to filter the sample data based on the statement filtering rules to obtain the sample filtered data. The model building unit is used to construct a statement analysis model corresponding to the terminal type using the sample screening data.

[0038] In some optional embodiments, the overlap calculation module 12 includes: The character extraction unit is used to extract the key characters of each of the statements to be processed, and to obtain the analysis statement corresponding to each of the statements to be processed in the statement analysis model based on the key characters of the statements. The overlap calculation unit is used to calculate the overlap between each of the statements to be processed and its corresponding analysis statement to obtain the statement overlap of each of the statements to be processed.

[0039] The similarity calculation module 13 is used to convert each of the statements to be processed into statement vectors and to calculate the vector similarity of each statement vector using the statement analysis model. Furthermore, the similarity calculation module 13 includes: The vector conversion unit is used to perform vector conversion on each of the statements to be processed and their corresponding analysis statements to obtain the corresponding statement vector and the corresponding analysis statement vector. The vector generalization unit is used to perform vector generalization on the statement vector to be processed and its corresponding analysis statement vector to obtain a multidimensional statement vector and its corresponding multidimensional analysis statement vector.

[0040] The word frequency calculation module 14 is used to count the word frequency of all words in the English text to be processed, and to calculate the word occurrence rate between each word using a word frequency dictionary and the word frequency of each word. The text filtering module 15 is used to filter the English text to be processed based on the sentence overlap, the vector similarity and the word occurrence rate, so as to obtain the English marked text and the English filtered text. The text processing module 16 is used to process the English marked text using a preset text processing model, and to combine the processed English marked text with the English filtered text to obtain the correct English text.

[0041] The functions or operation steps implemented by the above modules and units are largely the same as those in the above method embodiments, and will not be repeated here.

[0042] The English text processing system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0043] Example 3 This invention also proposes a computer, please refer to [link / reference]. Figure 6The computer shown in the third embodiment of the present invention includes a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, it implements the above-described English text processing method.

[0044] The memory 10 includes at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10 can be an internal storage unit of a computer, such as the computer's hard disk. In other embodiments, the memory 10 can be an external storage device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Furthermore, the memory 10 can include both internal and external storage units of the computer. The memory 10 can be used not only to store application software and various types of data installed on the computer, but also to temporarily store data that has been output or will be output.

[0045] In some embodiments, the processor 20 may be an electronic control unit (ECU), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program code stored in the memory 10 or process data, such as executing access restriction programs.

[0046] It should be pointed out that, Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0047] This invention also proposes a readable storage medium storing a computer program that, when executed by a processor, implements the English text processing method described above.

[0048] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0049] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0050] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0051] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0052] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for processing English text, characterized in that, include: The process involves acquiring the English text to be processed uploaded by the user terminal and then decomposing the English text to obtain multiple sentences to be processed. Construct a statement analysis model and use the statement analysis model to calculate the statement overlap of each statement to be processed; Each of the statements to be processed is converted into a statement vector, and the vector similarity of each statement vector is calculated using the statement analysis model. The word frequencies of all words in the English text to be processed are statistically analyzed, and the word occurrence rate among the words is calculated using a word frequency dictionary and the word frequency of each word. The English text to be processed is filtered based on the sentence overlap, vector similarity, and word occurrence rate to obtain the tagged English text and the filtered English text. The English marked text is processed using a preset text processing model, and the processed English marked text is combined with the English filtered text to obtain the correct English text.

2. The English text processing method according to claim 1, characterized in that, The steps to build a statement analysis model include: Obtain the terminal type of the user terminal, and obtain the corresponding sample data based on the terminal type; The statement filtering rules corresponding to the terminal type are invoked, and the sample data is filtered based on the statement filtering rules to obtain the sample filtered data; The sample screening data is used to construct a statement analysis model corresponding to the terminal type.

3. The English text processing method according to claim 1, characterized in that, The steps for calculating the statement overlap of each statement to be processed using the statement analysis model include: Extract the key characters of each statement to be processed, and obtain the analysis statement corresponding to each statement to be processed in the statement analysis model based on the key characters of the statement; The overlap degree of each statement to be processed and its corresponding analysis statement is calculated to obtain the statement overlap degree of each statement to be processed.

4. The English text processing method according to claim 3, characterized in that, The formula for calculating the overlap of statements is: ； In the formula, Indicates the first One statement to be processed. Indicates the first The analysis statements corresponding to each statement to be processed. Indicates the first The length of the longest common character among the statements to be processed and their corresponding parsed statements. Indicates the first The total length of the characters in the statements to be processed. Indicates the first The total length of the analysis statements corresponding to each statement to be processed.

5. The English text processing method according to claim 3, characterized in that, The steps for converting each of the statements to be processed into statement vectors include: Each of the statements to be processed and its corresponding analysis statements is vectorized to obtain the corresponding statement vector and its corresponding analysis statement vector. The vector of the statement to be processed and its corresponding analysis statement vector are generalized to obtain a multidimensional statement vector and its corresponding multidimensional analysis statement vector.

6. The English text processing method according to claim 4, characterized in that, The formula for calculating the vector similarity of the statement vectors is: ； In the formula, Indicates the first The statement to be processed undergoes vector transformation and vector generalization to obtain a multidimensional statement vector. ; express The corresponding multidimensional analysis statement vector, .

7. An English text processing system, characterized in that, include: The text decomposition module is used to acquire the English text to be processed uploaded by the user terminal and decompose the English text to be processed into multiple sentences to be processed. The overlap calculation module is used to construct a statement analysis model and use the statement analysis model to calculate the overlap of each statement to be processed. The similarity calculation module is used to convert each of the statements to be processed into statement vectors, and to calculate the vector similarity of each statement vector using the statement analysis model; The word frequency calculation module is used to count the word frequency of all words in the English text to be processed, and to calculate the word occurrence rate between each word using a word frequency dictionary and the word frequency of each word. The text filtering module is used to filter the English text to be processed based on the sentence overlap, the vector similarity, and the word occurrence rate, so as to obtain the English tagged text and the English filtered text. The text processing module is used to process the English marked text using a preset text processing model, and to combine the processed English marked text with the English filtered text to obtain the correct English text.

8. The English text processing system according to claim 7, characterized in that, The overlap calculation module includes: The sample data acquisition unit is used to acquire the terminal type of the user terminal and acquire corresponding sample data based on the terminal type. The statement filtering unit is used to call the statement filtering rules corresponding to the terminal type, and to filter the sample data based on the statement filtering rules to obtain the sample filtered data. The model building unit is used to construct a statement analysis model corresponding to the terminal type using the sample screening data.

9. A readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the English text processing method as described in any one of claims 1 to 6.

10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the English text processing method as described in any one of claims 1 to 6.

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