Semantic intelligent analysis method and system for English text

Abstract

The invention relates to the technical field of natural language processing, and discloses a semantic intelligent analysis method and system for an English text, and the system comprises a data module which is used for comparing an analyzed grammar with a database, mining the meaning of each sentence segment of the text, carrying out the role marking of the semantics, completing the scene simulation of the dialogue of the sentence segment, and obtaining a semantic analysis result; and semantic meanings are further mined. According to the method, multiple advanced semantic analysis technologies are integrated, character and scene simulation is substituted, the tagging sequence is calculated on the basis of part-of-speech and word vectors, features are defined to measure tagging rationality, and weights are calculated for optimization, so that the prediction accuracy of the semantic role tagging standard sequence is remarkably improved; according to the method and the system, the semantic role of each component in the text, such as an executor and a receiver, can be more accurately understood by the system, so that the semantic connotation of the text can be more deeply and accurately mastered, and compared with the prior art, the semantic understanding depth and precision are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of natural language processing, and specifically to a method and system for realizing semantic intelligent analysis of English texts. Background Art

[0002] The semantic intelligent analysis of English texts aims to enable a computer to understand the inherent meaning conveyed by English texts like a human being with the help of computer technology and natural language processing methods, rather than just staying at the level of identifying words and grammatical structures. It involves analyzing aspects such as the lexical semantics in the text, the logical relationships between sentences, and the influence of context on semantics, and finally realizes a comprehensive, accurate, and in-depth semantic interpretation of the entire English text.

[0003] Most traditional English text analysis methods are mostly limited to relatively simple means such as grammatical structure analysis and keyword matching, and it is difficult to deeply dig out the true semantics behind the text. In the face of complex semantic situations such as polysemy, metaphor, and context dependence, there are often many limitations and it is impossible to effectively understand. For example, the accuracy of semantic analysis is still insufficient, the adaptability to complex texts is poor, and the analysis efficiency also needs to be further improved. These problems make it difficult to well meet the diverse needs presented by the semantic intelligent analysis of English texts in practical applications. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for realizing semantic intelligent analysis of English texts to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A semantic intelligent system for realizing English texts, the system includes:

[0006] A human-computer interaction module for uploading the text to be analyzed by using text uploading, pasting, and scanning methods at the input port;

[0007] An encoding module for performing format standardization and lexical analysis processing on the received English text, and constructing a grammar array for the input words according to the sequence to sort out the text grammar structure;

[0008] A data module for comparing the analyzed grammar with the database, mining the meanings of each sentence segment of the text, and performing role annotation on the semantics to complete the scenario simulation of the sentence dialogue of this paragraph and further dig out the semantic meaning;

[0009] A data integration module for finally analyzing the semantics for quantitative representation, forming a complete analysis result according to the context, characters, and semantic connection relationship, and enabling the semantic information to be more convenient for subsequent processing by the computer system and external application use through the quantitative representation method.

[0010] Preferably, the human-computer interaction module includes:

[0011] A voice recognition unit that processes the voice recognition of the user, converts the collected voice signal into an electrical signal through a voice box, and further analyzes it into text understood by a computer;

[0012] A keyboard input unit that uses an external type and an analog touch screen, encodes by inputting characters and numbers, and is used for computer recognition processing;

[0013] A scanning recognition unit that uses an infrared camera to scan and input the required text.

[0014] Preferably, the encoding module recognizes English characters. For English characters, they are represented as a five-by-six matrix in a digital way. It is stipulated that 1 represents that there is a character element filled in the grid position, and 0 represents empty, regardless of uppercase and lowercase;

[0015]

[0016]

[0017] where A = [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];

[0018] B = [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];

[0019] C = [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];

[0020] to construct the digital representations of multiple English letters such as "A", "B", "C", and so on.

[0021] Preferably, the data module analyzes the language structure rules using language rules in its own composition, compares similar sentences and their corresponding semantic explanations with a database, and substitutes semantic roles. It consists of an executor A, a recipient P, a tool I, a location L, and a time T. Define the roles and formulas:

[0022] Executor formula: For the verb phrase V in a sentence, if the noun phrase N satisfies being in the subject position in the syntactic structure and having a logically active initiating relationship with V, and is judged based on the rules connecting the context between grammars, then N is labeled as the executor role;

[0023] Recipient P: For the verb phrase V and the noun phrase N, if N has a direct action-receiving relationship with V in the syntactic structure and forms an object-predicate state with each other, then it means that N is the recipient role;

[0024] Substitute the semantic roles defined above into the formula, and label the components in each sentence segment of the text one by one.

[0025] Preferably, the scene setting formula is:

[0026] Based on the semantic role annotation results and scene characteristics, assign corresponding roles to the dialogue simulation through the role assignment formula. In the shopping state, the executor can be set as the customer, the recipient is set as the product role, and the place corresponding to the location is set as the store. The formula can be expressed as P = agent → role = customer;

[0027] Among them, based on the part-of-speech and word vectors in the sentence, calculate the annotation sequence, and by defining features to measure the rationality of the annotation, the features combined with the grammatical and semantic information in the text indicate that the probability of the corresponding semantic role standard increases.

[0028] Preferably, the data integration module performs text analysis based on the scene, characters, and semantic context connection relationship, and the logical analysis is as follows:

[0029] If the verb (Buy) in the purchase behavior appears in the text, and the subsequent mentions are related to the product and the place (place) part-of-speech, then it is judged that the scene is a shopping scene;

[0030] If the verb of having a meeting (Meeting) appears in the text, and the subsequent mentions are the participants (Participants) and the location (Location), then it is determined that the place is a meeting venue.

[0031] Preferably, after processing the semantics of the English text, the data integration module performs quantitative processing on the data and stores it in the system to provide data comparison support for the next-step analysis of the device.

[0032] Preferably, a method for realizing semantic intelligent analysis of English text, the analysis method includes:

[0033] S1: The user can select a file containing English text stored locally through the file upload function provided by the human-computer interaction module, upload it to the system, and the system will perform a read operation on the uploaded file to obtain the text content therein;

[0034] S2: Subsequently, perform format standardization and lexical analysis on the received English text, output the preprocessed word sequence and part-of-speech tagging information, and transfer it to the syntactic analysis module;

[0035] S3: According to the built-in grammar rules and syntactic analysis model, construct a syntactic tree for the input word sequence, output it to the semantic role labeling module, achieve the sorting of the text grammar structure, and explore the interactions between roles in the scenario, the tone of the discourse, and the potential implied meaning, and dig out the meaning not directly expressed in the text;

[0036] S4: Combine grammar and the system's built-in semantic knowledge base to complete the semantic role labeling of each component in the text, and then transfer the result with semantic role labeling to the semantic dependency analysis module to comprehensively sort out the constructed dialogue simulation scenario, clarify the type of the scenario, the background setting, and the overall logic and development context of the scenario, and summarize and further deeply explore the semantic information of these scenario-related information;

[0037] S5: The system provides multiple output interface methods to meet the access and usage requirements of different external devices. For example, the analysis results can be transmitted to other application systems through a network interface, enabling them to obtain and utilize these semantic analysis results.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] In the present invention, by integrating a variety of advanced semantic analysis technologies, making full use of the rich semantic knowledge base, substituting the simulation of characters and scenarios, calculating the annotation sequence based on part-of-speech and word vectors, and defining feature metrics to measure the rationality of annotations and calculate weights for optimization, the prediction accuracy of the standard sequence of semantic role labeling is significantly improved, enabling the system to more accurately understand the semantic roles of each component in the text, such as the executor and the bearer, so as to more deeply and accurately grasp the semantic connotation of the text, and there is a significant improvement in the depth and accuracy of semantic understanding compared with the prior art.

[0040] In the present invention, the algorithms and processes of each module of the system are optimized and designed. While ensuring the quality of semantic analysis, it can quickly process the input English text, improve the overall analysis efficiency, and meet the requirements of timely processing of a large number of English texts in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a step diagram of a method and system for realizing semantic intelligent analysis of English text;

[0042] Figure 2 It is a construction flow diagram of a method and system for realizing semantic intelligent analysis of English text. Detailed implementation manners

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 - Figure 2 , the present invention provides a technical solution: a semantic intelligent system for realizing English text, and the system includes:

[0045] A human-computer interaction module, which is used to upload the text to be analyzed by using text upload, paste, and scanning methods at the input port;

[0046] An encoding module, which performs format standardization and lexical analysis processing on the received English text, constructs a grammar array for the input words according to the sequence, and realizes the sorting of the text grammar structure;

[0047] A data module, which compares the analyzed grammar with the database, excavates the meanings of each sentence segment of the text, performs role annotation on the semantics, completes the scenario simulation of the segment of statement dialogue, and further excavates the semantic meaning;

[0048] A data integration module, finally analyzes the semantics for quantitative representation, forms a complete analysis result according to the scene, characters, and semantic context connection relationship, and enables the semantic information to be more convenient for subsequent processing by the computer system and external application use through the quantitative representation method.

[0049] The following further illustrates this solution in conjunction with Embodiments 1 to 3:

[0050] Embodiment 1:

[0051] The human-computer interaction module of the present invention includes:

[0052] A voice recognition unit, which converts the collected voice signal into an electrical signal through voice recognition processing of the user, and further parses it into text understood by the computer;

[0053] A keyboard input unit, which adopts an external type and an analog touch screen, encodes the input characters and numbers for computer recognition processing;

[0054] A scanning recognition unit, which adopts an infrared camera to scan and input the required text;

[0055] The encoding module recognizes English characters. For English characters, they are represented as a five-by-six matrix in a digitalized way. It is stipulated that 1 represents that there is a character element filled in the grid position, and 0 represents an empty position, regardless of case.

[0056]

[0057]

[0058] Among them, A = [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];

[0059] B = [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];

[0060] C = [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];

[0061] And so on to construct the digital representations of multiple English letters such as "A", "B", "C".

[0062] Example 2:

[0063] The data module of the present invention analyzes the language structure rules through its own composition using language rules, compares similar sentences and their corresponding semantic interpretations with a database, and substitutes semantic roles. It consists of an executor A, a recipient P, a tool I, a location L, and a time T, and defines roles and related formulas:

[0064] Executor formula: For the verb phrase V in a sentence, if the noun phrase N satisfies being in the subject position in the syntactic structure and having a logically active initiating relationship with V, and is judged by the rules between grammars to connect the context, then N is marked as the executor role; Recipient P: For the verb phrase V and the noun phrase N, if N has a direct action-receiving relationship with V in the syntactic structure and forms an object-verb state with each other, then N represents the recipient role; According to the above-defined semantic role substitution formula, each component in each sentence segment of the text is marked with a role one by one.

[0065] The scenario setting formula is:

[0066] According to the semantic role annotation results and scene characteristics, corresponding roles are assigned to the dialogue simulation through the role assignment formula. In the shopping state, the executor can be set as the customer, the recipient as the commodity role, and the place corresponding to the location as the store. The formula can be expressed as P = agent → role = customer;

[0067] Among them, based on the part of speech and word vectors in the sentence, the annotation sequence is calculated. By defining features to measure the rationality of the annotation, and combining the syntactic and semantic information in the text, the probability corresponding to the semantic role standard increases. The calculation formula is;

[0068]

[0069] Among them, Q represents the probability of the calculation condition, y represents the annotation sequence, x is the observation sequence, and λ y is the weight of g y (x, y). By optimizing and calculating this weight, the prediction accuracy of the standard sequence can be improved.

[0070] Example 3:

[0071] The data integration module performs text analysis based on the scene, characters, and semantic context connection relationship. The logical analysis is as follows:

[0072] If the verb (Buy) of the purchase behavior (BuyAction) appears in the text, and at the same time the subsequent mentions of product-related and venue-related parts of speech, then it is determined that the scene is a shopping scene;

[0073] If the verb (MeetingAction) appears in the text, and the subsequent mention of the venue (Location), then it is determined that the venue is a meeting place;

[0074] The data integration module, after processing the semantics of the English text, quantifies the data and stores it in the system to provide data comparison support for the next-step analysis of the device.

[0075] In the present invention, after a user uploads a file through human-computer interaction, obtains the text content, and conducts lexical analysis on the standardized English text, the text is segmented into individual words, and corresponding parts of speech (nouns, verbs, adjectives) are labeled for each word. Finally, a word sequence and part-of-speech tagging information are output, and this information is transmitted to the syntactic analysis module to provide basic data for syntactic analysis. After receiving the word sequence and part-of-speech tagging information output by the lexical analysis, the syntactic analysis module combines its own semantic knowledge base, based on the previously tagged part-of-speech information, to complete the semantic role tagging process for each component in the text. Then, based on the part of speech and word vectors in the sentence, the tagging sequence is calculated, the rationality of the tagging is measured by defining features, and the weight is calculated. By optimizing this weight, the accuracy of predicting the standard sequence can be improved. Subsequently, according to the theme of the text, key verbs, and main object elements involved, a corresponding dialogue simulation scenario is constructed. The person corresponding to the executor role in the semantic role tagging is set as the customer role, and the item corresponding to the recipient role is set as the commodity role, presenting and applying the text content in a more vivid and intuitive manner.

[0076] The present invention further includes a method for realizing semantic intelligent analysis of English text. The analysis method includes:

[0077] Step 1: The user can select a file containing English text stored locally through the file upload function provided by the human-computer interaction module and upload it to the system. The system will read the uploaded file and obtain the text content therein.

[0078] Step 2: Subsequently, perform format standardization and lexical analysis processing on the received English text, output the preprocessed word sequence and part-of-speech tagging information, and transmit it to the syntactic analysis module.

[0079] Step 3: According to the built-in grammar rules and syntactic analysis model, construct a syntactic tree for the input word sequence, output it to the semantic role tagging module, sort out the grammatical structure of the text, and explore the unexpressed meanings in the text, such as the interaction between roles in the scenario, the tone of the discourse, and the potential implied meaning.

[0080] Step 4: Combine the grammar and the system's own semantic knowledge base to complete the semantic role tagging work for each component in the text. Then, transmit the result with semantic role tagging to the semantic dependency analysis module to comprehensively sort out the constructed dialogue simulation scenario, clarify the type of the scenario, background setting, overall logic, and development context of the scenario, and organize and summarize these scenario-related information to further deeply explore semantic information.

[0081] Step Five: The system provides multiple output interface methods to meet the access and usage requirements of different external devices. For example, the analysis results can be transmitted to other application systems through a network interface, enabling them to obtain and utilize these semantic analysis results.

[0082] The implementation method of this method refers to the above embodiments, which will not be elaborated in the description.

[0083] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A semantic intelligent system for implementing English text, characterized in that: The system includes: A human-computer interaction module, which is used to upload the text to be analyzed by using text upload, paste, and scanning methods at the input port. An encoding module, which performs format standardization and lexical analysis on the received English text, constructs a syntax array for the input words in sequence, and realizes the sorting of the text syntax structure. A data module, which compares the analyzed syntax with the database, excavates the meanings of each sentence segment of the text, performs role annotation on the semantics, completes the scenario simulation of the segment statement dialogue, and further excavates the semantic meaning. A data integration module, which finally analyzes the semantics for quantitative representation, forms a complete analysis result according to the scenario, characters, and semantic context connection relationship, and enables the semantic information to be more convenient for subsequent processing by the computer system and external application use through the quantitative representation method.

2. The semantic intelligent system for realizing English text according to claim 1, wherein: The human-computer interaction module includes: A voice recognition unit, which processes the voice of the user through voice recognition, converts the collected voice signal into an electrical signal through a voice box, and further analyzes it into text understood by the computer. A keyboard input unit, which adopts an external and simulated touch screen, encodes the input characters and numbers for computer recognition and processing. A scanning recognition unit, which uses an infrared camera to scan and input the required text.

3. A semantic intelligent system for realizing English text according to claim 1, characterized in that: The encoding module recognizes English characters. For English characters, they are represented as a five-by-six matrix in a digitalized way. It is stipulated that 1 represents that there is a character element filled in the square position, and 0 represents empty. Regardless of case, where A = [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 B＝[0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 00]； C＝[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 00]； ​ 4. A semantic intelligent system for implementing English text according to claim 1, characterized in that: ​ ​ ​ ​ 5. A semantic intelligent system for realizing English text according to claim 1, characterized in that, ​ ​ Based on the part-of-speech and word vectors in the sentence, the annotation sequence is calculated, and by defining features to measure the rationality of the annotation, the combination of features with the syntactic and semantic information in the text indicates an increase in the probability of the corresponding semantic role standard.

6. A semantic intelligent system for realizing English text according to claim 1, characterized in that, The data integration module performs text analysis based on the scenario, characters, and semantic context connection relationship, and the logical analysis is as follows: If the verb (Buy) in the purchase behavior appears in the text, and the subsequent text mentions the product and the relevant part-of-speech of the place, it is determined that the scenario is a shopping scenario; If the verb of having a meeting (Meeting) appears in the text, and the subsequent text mentions the participants and the location, it is determined that the location is a meeting place.

7. A semantic intelligent system for realizing English text according to claim 1, characterized in that, After processing the semantics of the English text, the data integration module quantifies the data, which is stored by the system to provide data comparison support for the next-step analysis of the device.

8. A method for realizing semantic intelligent analysis of English text, applicable to a semantic intelligent system for English text described in any one of claims 1-7, and the analysis method includes: S1: The user can select a file containing English text stored locally through the file upload function provided by the human-computer interaction module and upload it to the system. The system will read the uploaded file to obtain the text content therein; S2: Subsequently, perform format standardization and lexical analysis processing on the received English text, output the preprocessed word sequence and part-of-speech annotation information, and transfer it to the syntactic analysis module; S3: According to the built-in grammar rules and syntactic analysis model, construct a syntactic tree for the input word sequence and output it to the semantic role annotation module to sort out the text grammar structure, and explore the unexpressed meanings in the text, such as the interaction between roles in the scenario, the tone of the discourse, and the potential implied meaning; S4: Combine grammar and the system's built-in semantic knowledge base to complete the semantic role annotation work for each component in the text, and then transfer the result with semantic role annotation to the semantic dependency analysis module to comprehensively sort out the constructed dialogue simulation scenario, clarify the type of the scenario, the background setting, and the overall logic and development context of the scenario, and organize and summarize these scenario-related information to further deeply explore semantic information; S5: The system provides multiple output interface methods to meet the access and usage requirements of different external devices.

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