An artificial intelligence-based electronic publication content automatic proofreading and correction system

Abstract

This invention relates to the field of character recognition and grammatical analysis technology, and discloses an automatic proofreading and error correction system for electronic publications based on artificial intelligence. The system includes: a data acquisition module for acquiring a sequence stream of text to be proofread; a syntactic parsing module for constructing a dependency syntax tree and identifying multi-level nested clauses; a structure processing module for generating feature projection vectors using feature space projection mapping when the syntactic nesting depth exceeds a threshold, thereby compensating for semantic features and maintaining logical closure; a verification module for determining the structural deviation index between the local syntactic matrix and the global semantic vector; and an error correction decision module for outputting error correction signals. This invention utilizes a feature projection compensation mechanism to fill in semantic gaps caused by local truncation, ensures the connectivity of long-range grammatical dependencies, eliminates false positives caused by loss of contextual information, and effectively enhances the system's parsing accuracy for ultra-long and complex sentences.

Description

Technical Field

[0001] This invention belongs to the field of character recognition and syntax analysis technology, and in particular relates to an automatic proofreading and error correction system for electronic publications based on artificial intelligence. Background Technology

[0002] Currently, in digital publishing and document processing, natural language processing technology achieves text proofreading through dictionary comparison and sequence probability prediction. This method supports the identification of spelling errors and local semantic mismatches, and is the basic support for improving the efficiency of publication quality control. When processing academic monographs, legal compilations and other professional documents, the content presents complex syntactic nesting and strict logical dependencies. Existing technical solutions mostly adopt local window scanning or linear dependency path parsing. Due to the linear distance bias of the parser, the system usually actively ignores deep syntactic topology in order to maintain real-time response.

[0003] This physical truncation of the syntax tree leads to logical breaks in long-distance dependencies within the topological matrix, preventing the system from accurately capturing cross-level grammatical constraints and resulting in frequent false alarms. Increasing hardware resources or model parameter size cannot fundamentally solve the dimensionality explosion problem in syntactic parsing. When the parser attempts to traverse deep clauses, the computational overhead increases exponentially with nesting depth. Such linear improvement paths face the dual constraints of computational bottlenecks and processing latency. Software-controlled logic has shortcomings in handling long-range semantic relationships. For example, Chinese invention patent application CN121009885A discloses a... This method for correcting English text grammatical errors by annotating contextual features solves the problem of cross-sentence grammatical error identification by using attention mechanisms to fuse features based on contextual information. However, this approach relies on a fixed sliding sampling window and statistical probability distribution, with underlying logic anchoring semantic completion within the window. It does not address the recursive depth issue inherent in the syntactic topology itself. When faced with deep grammatical topologies of extremely long sentences in academic contexts, the parser exhibits physical truncation bias, causing logical breaks in long-distance dependencies within the topological matrix. It fails to capture cross-level grammatical constraints and, due to the lack of a feature compensation mechanism for syntactic tree truncation points, large-scale computing environments lead to computational redundancy and a cascading effect of logical false alarms.

[0004] Therefore, the technical problem to be solved by this invention is how to provide an automatic proofreading and error correction system for electronic publications based on artificial intelligence, which can achieve logical closure parsing of highly nested syntactic structures under the constraint of constant-level computational overhead. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: An automatic proofreading and error correction system for electronic publications based on artificial intelligence, comprising: The data acquisition module is used to acquire the text sequence stream to be proofread in electronic publications; The syntactic parsing module is used to parse the text sequence stream to be proofread into a dependency syntax tree through a self-attention encoding unit, and to identify the multi-level nested clause structure in the dependency syntax tree; The structure processing module is used to enable feature compensation at the syntactic truncation node position when the nesting depth of the dependency syntax tree exceeds the preset level threshold. The structure processing module maps the syntactic features of the truncated subtree to the syntactic truncation node through feature space projection mapping, generates feature projection vectors that represent the semantic closure state of the underlying clauses, and uses the feature projection vectors to compensate the semantic features after the syntactic truncation node, so that the dependency syntax tree maintains logical closure in the vector space. The verification module is used to determine the structural deviation index between the local syntactic matrix and the global semantic vector in the text sequence stream to be verified by calculating the syntactic structural distance of the current statement and the semantic tensor distance in the feature space. The error correction decision module is used to determine that there is a logical break in the text sequence stream to be corrected when the structural deviation index exceeds the preset deviation threshold, and outputs error correction instruction signals for distorted nodes in the local syntax matrix.

[0006] Preferably, the syntax parsing module reduces the weight score of the far-end node in the dependency syntax tree through a preset weight decay process, and matches it with a pre-stored syntax error correction graph to identify the subject-verb-object relationship in the multi-layer nested clause structure; wherein, the weight decay process allocates the node weight according to the topological span of the node in the dependency syntax tree in a non-linear proportion to ensure the parsing accuracy of the syntax parsing module.

[0007] Preferably, the text sequence stream to be proofread includes digitized text data of academic monographs, legal compilations, or industry reports, and the text sequence stream to be proofread carries linguistic logical features in the form of a character set with grammatical structure and semantic constraints.

[0008] Preferably, when generating feature projection vectors, the structure processing module converts deep clauses exceeding a preset level threshold into fixed-length feature groups through adaptive feature dimensionality reduction processing, so that the system's memory usage and computational load remain within the preset hardware processing boundaries.

[0009] Preferably, the verification module is connected to the multidimensional semantic vector space mapping module, which is used to perform vector alignment between the local grammatical features output by the syntactic parsing module and the pre-stored global terminology consistency features, so as to identify terminology reference deviations across chapters of the text.

[0010] Preferably, the verification module determines the structural deviation index by calculating the weighted Euclidean distance between the syntactic structure distance and the semantic tensor distance, and dynamically adjusts the weight ratio of the syntactic structure distance and the semantic tensor distance according to the context database of the relevant professional field, so as to lock the distorted nodes in the local syntactic matrix.

[0011] Preferably, when processing inverted sentences, the structure processing module repairs the topological breakpoints of the dependency syntax tree through feature space projection mapping, and reconstructs the feature connectivity of long-range dependency relations in the local syntax matrix, thereby suppressing false alarms caused by context information truncation.

[0012] Preferably, when the error correction decision module outputs the error correction instruction signal, it simultaneously generates error correction suggestion data, which includes grammatical nesting correction paths, long and difficult sentence structure reorganization paths, and semantic consistency verification paths in specific professional contexts.

[0013] Preferably, the system configures the subtree decoupling logic of the dependency syntax tree through the structure processing module. The system solves the problem of exhausting computing resources in the proofreading process of large-scale publications by performing dimensional compression on multi-level nested clauses, and ensures the consistency of text parsing.

[0014] Compared with existing technologies, the AI-based automatic proofreading and error correction system for electronic publications of this invention has the following advantages: 1. In the automatic proofreading of electronic publications, by introducing a cross-domain virtual terminal symbol projection mechanism into the local syntactic topology matrix, the system provides semantic anchor points for dangling dependency nodes that have reached the depth constraint threshold. This mechanism utilizes the dimensionality reduction feature array of high-dimensional global semantic vectors to directly instantiate macro-discourse features into virtual entity nodes at the edge of the graph structure. Since virtual nodes can act as terminals in the syntactic parsing process, the system can maintain the logical closure of the local syntactic tree in an algebraic sense without infinitely expanding the deep nested structure. This approach eliminates the information entropy discontinuity caused by traditional physical truncation strategies from a mechanism perspective, ensuring that the system can still accurately perceive long-distance syntactic dependencies across chapters when processing extremely long and complex sentences, and avoids false alarms caused by the loss of contextual information.

[0015] 2. By deeply coupling the local syntactic topology matrix with the high-dimensional global semantic vector, the system constructs a cross-verification logic based on the structural deviation index. This logic no longer analyzes the statistical probability of a single character sequence in isolation, but quantifies the compliance of the text logic by calculating the syntactic topology distance of the current sentence and the semantic tensor distance in the feature space. When a sentence has a correct syntactic structure but the terminology deviates from the global context, this dual-track parsing architecture can accurately lock the distorted nodes in the local topology matrix through dynamic adjustment of weights. This multi-dimensional collaborative processing method improves the system's ability to parse unfamiliar contexts and long and difficult sentences in publications, effectively blocking the error cascading effect caused by semantic mismatch.

[0016] 3. By organically combining adaptive topology truncation units and subtree decoupling operations, the system achieves highly stable text parsing under constant computing power boundaries. For complex nesting with graph depths exceeding the threshold, the system converts deep clauses into independent feature groups for dimensionality reduction through feature extraction and similarity allocation, and applies distance decay logic to suppress interference weights of distant nodes. This processing path ensures that memory usage and computational complexity are always kept within constant levels, solving the problem of dimensionality explosion caused by complex grammatical topology in the proofreading process of large-scale publications. This solution not only ensures the continuity of the proofreading process, but also provides stable structural support for grammatical error correction in high-noise text environments. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the principle of text parsing and automatic error correction based on artificial intelligence in this invention. Figure 2 This is a diagram showing the dual-track analysis and module interaction of the intelligent proofreading and error correction system of this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0019] An AI-based automatic proofreading and error correction system for electronic publications includes: The data acquisition module is used to acquire the text sequence stream to be proofread in electronic publications; The syntactic parsing module is used to parse the text sequence stream to be proofread into a dependency syntax tree through a self-attention encoding unit, and to identify the multi-level nested clause structure in the dependency syntax tree; The structure processing module is used to enable feature compensation at the syntactic truncation node position when the nesting depth of the dependency syntax tree exceeds the preset level threshold. The structure processing module maps the syntactic features of the truncated subtree to the syntactic truncation node through feature space projection mapping, generates feature projection vectors that represent the semantic closure state of the underlying clauses, and uses the feature projection vectors to compensate the semantic features after the syntactic truncation node, so that the dependency syntax tree maintains logical closure in the vector space. The verification module is used to determine the structural deviation index between the local syntactic matrix and the global semantic vector in the text sequence stream to be verified by calculating the syntactic structural distance of the current statement and the semantic tensor distance in the feature space. The error correction decision module is used to determine that there is a logical break in the text sequence stream to be corrected when the structural deviation index exceeds the preset deviation threshold, and outputs error correction instruction signals for distorted nodes in the local syntax matrix.

[0020] Preferably, the syntax parsing module reduces the weight score of the far-end node in the dependency syntax tree through a preset weight decay process, and matches it with a pre-stored syntax error correction graph to identify the subject-verb-object relationship in the multi-layer nested clause structure; wherein, the weight decay process allocates the node weight according to the topological span of the node in the dependency syntax tree in a non-linear proportion to ensure the parsing accuracy of the syntax parsing module.

[0021] Preferably, the text sequence stream to be proofread includes digitized text data of academic monographs, legal compilations, or industry reports, and the text sequence stream to be proofread carries linguistic logical features in the form of a character set with grammatical structure and semantic constraints.

[0022] Preferably, when generating feature projection vectors, the structure processing module converts deep clauses exceeding a preset level threshold into fixed-length feature groups through adaptive feature dimensionality reduction processing, so that the system's memory usage and computational load remain within the preset hardware processing boundaries.

[0023] Preferably, the verification module is connected to the multidimensional semantic vector space mapping module, which is used to perform vector alignment between the local grammatical features output by the syntactic parsing module and the pre-stored global terminology consistency features, so as to identify terminology reference deviations across chapters of the text.

[0024] Preferably, when the structure processing module calculates the feature projection vector, it determines the compensation features at the syntactic truncation nodes using the following formula: ,in, Let n be the feature projection vector, and n be the total number of feature dimensions of the truncated subtree. Let be the original value of the i-th feature dimension. is the projection weight coefficient of the i-th feature dimension in the feature space.

[0025] Preferably, the verification module determines the structural deviation index by calculating the weighted Euclidean distance between the syntactic structure distance and the semantic tensor distance, and dynamically adjusts the weight ratio of the syntactic structure distance and the semantic tensor distance according to the context database of the relevant professional field, so as to lock the distorted nodes in the local syntactic matrix.

[0026] Preferably, when processing inverted sentences, the structure processing module repairs the topological breakpoints of the dependency syntax tree through feature space projection mapping, and reconstructs the feature connectivity of long-range dependency relations in the local syntax matrix, thereby suppressing false alarms caused by context information truncation.

[0027] Preferably, when the error correction decision module outputs the error correction instruction signal, it simultaneously generates error correction suggestion data, which includes grammatical nesting correction paths, long and difficult sentence structure reorganization paths, and semantic consistency verification paths in specific professional contexts.

[0028] Preferably, the system configures the subtree decoupling logic of the dependency syntax tree through the structure processing module. The system solves the problem of exhausting computing resources in the proofreading process of large-scale publications by performing dimensional compression on multi-level nested clauses, and ensures the consistency of text parsing.

[0029] Example 1: Addressing the challenge of proofreading homophones and grammatical structural discrepancies in large-scale academic journal article editing tasks, the system faces the problem of insufficient extraction of high-dimensional semantic features from the text sequence to be processed. The processor receives the initial text stream, and the semantic feature mapping module converts the character entities in the text sequence into corresponding multi-dimensional vector features, establishing the distribution coordinates of character nodes in the semantic space. The system reconstructs the multi-dimensional vector features using a lexical dependency logic topology tree, capturing long-distance grammatical dependencies between character nodes. Within the syntactic parsing module, the self-attention encoding unit establishes compensation weights based on the scaling dot product attention rule, extracting the query vectors and key vectors of character nodes at each level within the truncated subtree, and calculating... The inner product of the two types of vectors is divided by the square root of the feature dimension, and an attention distribution matrix is ​​output based on the normalized exponential function. The system inputs this attention distribution matrix into a pre-set dual affine classifier. By performing a bilinear mapping on the query vector and key vector, the probability score of dependency arcs between character nodes is calculated. The maximum spanning tree decoding algorithm is then used to search for the directed acyclic graph with the highest global probability score in the scoring matrix, thereby generating a dependency syntax tree containing hierarchical topological relationships. When the local nesting level of the dependency syntax tree exceeds a preset level threshold, the structure processing module extracts the data row distribution vector of the corresponding syntactic truncation node in the attention distribution matrix, and directly uses the dimensionless normalized value in the distribution vector as the projection weight coefficient. Applied to feature compensation operations for syntactic truncation nodes; extracts the original values ​​of each feature dimension under the total number of feature dimensions n within the truncated subtree. Combined with projection weighting coefficients A fixed-length feature projection vector is generated through multiplication and addition operations. The underlying multidimensional semantic closure state is transformed into a specific feature array to fill the parameter gaps at the topological breakpoints. During the filling process, the system uses the fixed-length feature projection vector as the initial embedding representation of the virtual leaf node. Through graph aggregation operations, its features are concatenated into the adjacency matrix feature pool of the original syntactic truncated node. This allows the parser to directly read the underlying structural information contained in the continuous vector in subsequent topological calculations, thereby replacing the actual extension of the physical topological edge at the algebraic level of the algorithm and completing the logical closure.

[0030] The controller drives the semantic feature mapping module to calculate the semantic association matrix between each character node, establishing a feature benchmark to represent contextual consistency. Before assigning verification operation weights, the system extracts the sum of the topological side lengths of adjacent nodes in the dependency syntax tree as the syntactic distance, and calculates the cosine similarity scalar between the feature vector of the local syntax matrix and the feature benchmark used to represent contextual consistency, using it as the semantic tensor distance. The verification module quantifies the text context density based on information entropy theory to define the cross-verification operation weights, and calculates the distribution probability of the core terms covered by the current local syntax matrix in the pre-stored professional domain context library, calculating the corresponding cross-information entropy scalar. The system uses the conditional probability of the core terms under the current local syntax matrix as the true distribution and the distribution probability in the pre-stored context library as the reference distribution. By calculating the relative entropy, i.e., the KL divergence, between the two and integrating and summing over all extracted core term feature dimensions, the final exact value of the cross-information entropy scalar is obtained. The system pre-stores initial weight ratio parameters. When the calculated cross-information entropy scalar is higher than the set calibration entropy threshold, the semantic tensor distance multiplier factor is increased proportionally to the deviation difference, while the syntactic structure distance multiplier factor is reduced by an equal amount. Based on the updated multiplier factor, the weighted Euclidean distance of the current sequence is calculated, and a deviation index for the representation logic compliance structure is established. When the semantic coordinates of a specific character node deviate from the Euclidean distance of the feature benchmark by more than the preset error correction deviation threshold, the system determines that there is a content error at that position. The semantic feature mapping module retrieves the standardized language knowledge base stored locally and locks the candidate correction character set. The system extracts the local environment features of the current character node based on the context sliding sampling window, calculates the matching probability of each character in the candidate correction character set with the local environment features, and the controller selects the character with the highest matching probability as the final correction result and replaces the erroneous character in the original sequence. After the correction is completed, the semantic logic of the output corpus reaches the preset coherence index and meets the electronic publication entry standards.

[0031] Example 2: For high-concurrency proofreading scenarios involving a mix of historical document scans and modern typesetting documents as input sources, the input text from the optical character recognition engine contains structural gibberish and breaks in proprietary entity syntax. The experiment uses a Chinese grammatical error diagnosis dataset as the basic validation corpus. The operating environment employs a graphics processing unit (GPU) array with 128 tensor cores and a clock speed of 2.1 GHz. This array has a single-precision floating-point operation capability calibrated to 15 TFLOPS, supporting concurrent character matrix operations. A context-based sliding sampling window length parameter is set, the value of which is related to the semantic dependency span and the memory residency overhead of the computing nodes. When the average sentence length of the monitored corpus exceeds 50 characters, to avoid truncation of dependencies across syntactic components, the sampling window length tends towards the upper limit of the range. For the mixed-typesetting scenario of ancient books with an average sentence length of 65 characters, the sampling window length is determined to be a span of 128 characters based on the above rules.

[0032] Discrete optical character recognition noise with an error rate of 15% was injected into the test signal source, including misalignment of radicals and random replacement of similar-looking characters, to simulate interference in the optical scanning environment. The original input text sequence containing the aforementioned noise was extracted. This sequence contained erroneous entities. The lexical dependency topology distance value output by the grammatical feature mapping unit was extracted. The control group did not introduce a grammatical topology correction mechanism, and the output distance value was 0.85, which did not trigger a context break warning. The test group adopted this technical solution and extracted a dependency topology distance value of 4.25. Based on the fact that the lexical dependency topology distance value is greater than the preset topology distance threshold of 3.0, the system determined that the position was a context abnormal entity, and the system generated a corresponding correction and replacement instruction to eliminate the mismatch of similar-looking characters.

[0033] Multiple experimental groups with gradient variations were set up to determine the performance boundaries. A control group with a lower limit of 16 characters (contextual sliding sampling window length) exceeded the range, achieving a syntax correction accuracy of 41.2%, lower than the quality acceptance baseline. The present invention's sample group with a sliding sampling window length of 64 characters achieved a syntax correction accuracy of 89.5%. A control group with an upper limit of 256 characters (contextual sliding sampling window length) exceeded the range, achieving a syntax correction accuracy of 90.1%. The single-sentence logical reasoning latency increased from 12.5ms to 85.4ms. Experimental data showed that after the sampling window length exceeded 128 characters, the rate of increase in error correction accuracy decreased, memory addressing overhead increased, and local semantic feature extraction sites reached saturation. A partially missing control group with missing grammatical dependency tree reconstruction features was established. When processing corpora with a 15% error rate, the corresponding accuracy was 62.3%. The complete combination of the present invention's sample group produced an accuracy of 89.5%, greater than the sum of the accuracy of each sub-unit.

[0034] Example 3: For the proofreading of interdisciplinary professional electronic publications, a static grammar rule base generates an excessive context break warning when dealing with dense professional terms. The initial data stream for this scenario is a sequence of text to be proofread containing complex medical terms. The operating environment uses a neural network acceleration processor equipped with 32GB of video memory and supporting tensor parallel operations. The semantic feature mapping module extracts a 512-character sequence of text to be proofread. This mapping module includes 12 stacked self-attention feature transformation layers. The semantic feature mapping module converts the sequence of text to be proofread into a 512-dimensional word embedding feature matrix and inputs this feature matrix into a graph convolutional neural network. The graph convolutional neural network calculates the connectivity weights between each character node based on a preset lexical dependency logic topology tree. The connectivity weights are determined according to the following formula: ,in, The connectivity weight between character node i and character node j This is the path attenuation coefficient, with a value of 0.5; Let be the shortest path span between character node i and character node j in the lexical dependency logic topology tree. It is the basic bias constant, with a value of 0.1.

[0035] The graph convolutional neural network, combined with connectivity weights, outputs a semantic deviation scalar for each character node. The controller sets anomaly detection threshold based on dynamic evolution logic, and extracts the values ​​from the previous monitoring time. The semantic deviation scalar of all character nodes within a sliding sampling period is calculated, and the variance of this semantic deviation scalar is calculated. The controller calculates the reciprocal of this variance and sets the reciprocal as the dynamic judgment benchmark threshold. In this process, the variance represents the degree of uniformity of the text context within the current sliding window. When complex technical terms appear densely, the semantic deviation scalar clusters, leading to a decrease in dispersion, i.e., a reduction in variance, which increases the reciprocal value, thereby automatically raising the judgment benchmark threshold. This effectively suppresses the risk of high-frequency false alarms that the system is prone to in high-density obscure terminology environments. When the semantic deviation scalar of a specific character node is greater than the dynamic judgment benchmark threshold, the controller generates a syntax error correction mark instruction for that specific character node. The control group uses a single static threshold judgment mechanism to process the text sequence to be proofread, outputting 15 syntax error coordinate parameters. The sample group of this invention uses dynamic evolution logic to process the text sequence to be proofread, and the dynamic judgment benchmark threshold increases synchronously with the increase of technical terminology density, ultimately outputting 1 syntax error coordinate parameter.

[0036] Example 4: For the initial deployment of a brand-new professional corpus, the system drives an offline calibration procedure to construct a lexical dependency logic topology tree. The system extracts 10,000 unlabeled text sequences from a specific domain, segments the unlabeled text sequences using an unsupervised word formation algorithm, counts the co-occurrence frequency between character nodes, selects character pairs with a co-occurrence frequency greater than a preset lower limit as candidate dependency edges, filters candidate edges according to a predetermined rule set, and generates an initial logic topology tree. The graph convolutional neural network loads the initial logic topology tree, receives 1,000 labeled verification texts, and drives backpropagation training. The system updates the shortest path span parameter between character nodes according to the gradient direction of the loss function. The training termination condition is triggered when the change amplitude of the loss function value within 100 consecutive iterations is less than 0.001. The system outputs a standard lexical dependency logic topology tree and stores it in memory.

[0037] The controller drives the on-site pre-calibration procedure to set the initial baseline of the dynamic judgment benchmark threshold. Before accessing the calibration data stream, the semantic feature mapping module receives a clean sample set containing 500 normal sentence patterns. The graph convolutional neural network processes the clean sample set and outputs the initial semantic deviation scalar of all character nodes. The controller calculates the global statistical variance of the initial semantic deviation scalar and sets the reciprocal of the global statistical variance as the baseline threshold at the first monitoring time. The system switches to concurrent calibration mode. The controller fuses the baseline threshold with the real-time extracted reciprocal of the sliding sampling period variance and updates the dynamic judgment benchmark threshold. In the first sampling period of accessing the concurrent data stream, the system outputs the coordinate parameters of the syntax anomaly.

[0038] Example 5: For the parameter calibration of cross-domain corpus deployment, the test terminal extracts a benchmark verification text set containing known grammatical structures. The benchmark verification text set is input into the semantic feature mapping module to generate an initial word embedding feature matrix. The calibration module traverses the path attenuation coefficient by incrementing the preset step size. With the basic bias constant In a two-dimensional coordinate grid, the calibration module drives the graph convolutional neural network to perform forward propagation tensor operations at each coordinate node. It then extracts the sum of squared differences between the semantic deviation scalar output of the graph convolutional neural network and the expected annotation benchmark. Finally, the calibration module locks the path decay coefficient that minimizes this sum of squared differences. With the basic bias constant The calibration module writes the parameter combination into the controller's read-only memory as the underlying parameter boundary for system startup.

[0039] When the system encounters a situation where the technical field of the proofreading corpus changes, the controller extracts a 512-character text slice from the front end of the newly received data stream as a probe sample. The controller then calculates the distribution density of unregistered words within the probe sample. Based on this distribution density, the controller queries the pre-stored calibration database to extract the corresponding weight compensation ratio scalar. The controller then stores the path attenuation coefficient. The runtime attenuation coefficient corresponding to the current working condition is calculated by multiplying the weight compensation ratio scalar. The graph convolutional neural network is loaded with the runtime attenuation coefficient to replace the initial parameters, and the connectivity weights between each character node are recalculated based on the runtime attenuation coefficient. The system outputs the corresponding syntax anomaly coordinate parameters based on the updated connectivity weights.

[0040] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An automatic proofreading and error correction system for electronic publications based on artificial intelligence, characterized in that, include: The data acquisition module is used to acquire the text sequence stream to be proofread in electronic publications; The syntactic parsing module is used to parse the text sequence stream to be proofread into a dependency syntax tree through a self-attention encoding unit, and to identify the multi-level nested clause structure in the dependency syntax tree; The structure processing module is used to enable feature compensation at the syntactic truncation node position when the nesting depth of the dependency syntax tree exceeds the preset level threshold. The structure processing module maps the syntactic features of the truncated subtree to the syntactic truncation node through feature space projection mapping, generates feature projection vectors that represent the semantic closure state of the underlying clauses, and uses the feature projection vectors to compensate the semantic features after the syntactic truncation node, so that the dependency syntax tree maintains logical closure in the vector space. The verification module is used to determine the structural deviation index between the local syntactic matrix and the global semantic vector in the text sequence stream to be verified by calculating the syntactic structural distance of the current statement and the semantic tensor distance in the feature space. The error correction decision module is used to determine that there is a logical break in the text sequence stream to be corrected when the structural deviation index exceeds the preset deviation threshold, and outputs error correction instruction signals for distorted nodes in the local syntax matrix.

2. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, The syntactic parsing module reduces the weight score of far nodes in the dependency syntax tree through a preset weight decay process, and matches it with a pre-stored syntax error correction graph to identify the subject-verb-object relationship in multi-layer nested clause structures. The weight decay process allocates node weights according to the topological span of the node in the dependency syntax tree in a non-linear proportion to ensure the parsing accuracy of the syntactic parsing module.

3. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, The text sequence stream to be proofread includes digitized text data from academic monographs, legal compilations, or industry reports. The text sequence stream to be proofread carries linguistic logical features in the form of a set of characters with grammatical structure and semantic constraints.

4. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, When generating feature projection vectors, the structure processing module uses adaptive feature dimensionality reduction to convert deep clauses that exceed the preset level threshold into fixed-length feature groups, thus keeping the system's memory usage and computational load within the preset hardware processing boundaries.

5. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, The verification module is connected to the multidimensional semantic vector space mapping module, which is used to perform vector alignment between the local grammatical features output by the syntactic parsing module and the pre-stored global terminology consistency features in order to identify terminology reference deviations across chapters of the text.

6. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, The verification module determines the structural deviation index by calculating the weighted Euclidean distance between the syntactic structure distance and the semantic tensor distance, and dynamically adjusts the weight ratio of the syntactic structure distance and the semantic tensor distance according to the context database of the relevant professional field in order to lock the distorted nodes in the local syntactic matrix.

7. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, When processing inverted sentences, the structure processing module repairs the topological breakpoints of the dependency syntax tree through feature space projection mapping, and reconstructs the feature connectivity of long-range dependency relations in the local syntax matrix.

8. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, When the error correction decision module outputs error correction instruction signals, it simultaneously generates error correction suggestion data, which includes grammatical nesting correction paths, long and difficult sentence structure reorganization paths, and semantic consistency correction paths in specific professional contexts.

9. The automatic proofreading and error correction system for electronic publications based on artificial intelligence according to claim 1, characterized in that, The system configures the subtree decoupling logic of the dependency syntax tree through the structure processing module. The system solves the problem of exhausting computing resources in the proofreading process of large-scale publications by compressing the dimensions of multi-level nested clauses and ensures the consistency of text parsing.

Images