A legal application document intelligent review system based on natural language processing
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN INST OF TECH
- Filing Date
- 2026-03-06
- Publication Date
- 2026-06-26
Smart Images
Figure CN122287609A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an intelligent review system for legal application documents based on natural language processing, belonging to the field of semantic recognition technology. Background Technology
[0002] Current legal document review uses natural language processing technology to analyze text semantics, extract legal subjects, factual statements and rights and obligations elements, and assist in compliance verification. Legal language has high logical density and mutually exclusive rights and obligations relationships. Mainstream technologies mostly focus on shallow entity extraction or word probability generation. When faced with long documents, due to the lack of modeling the logical polarity between semantic entities, the parsing process is difficult to establish logical constraint boundaries in unstructured texts.
[0003] Existing technologies focus on improving the standardization of document generation, neglecting the proactive detection of logical loopholes. For example, Chinese invention patent CN104090863A discloses an intelligent legal document generation method and system, which belongs to a static question-and-answer template matching mechanism. It focuses on the logical stacking and semantic combination of clauses. Due to the lack of atomized polarity quantitative analysis of legal propositions, it cannot perceive semantic drift caused by contextual shifts and excessive referential distances. It is difficult to identify long-distance logical conflicts across paragraphs that are hidden under restrictive conditions. Based on static rule matching of unstructured legal texts, the lack of logical closed-loop detection capability creates blind spots in review and cannot solve the legal risks of execution boundaries. This limitation leads to an increase in semantic logical entropy, causing implicit semantic breaks between the document's arguments and evidence, facts and conclusions. Increasing the training sample size or improving model parameters only improves the local text expression coherence and cannot solve the problem of inconsistent semantic topology. The probability distribution determination method is ambiguous, and the review results rely on manual secondary verification, which restricts the depth of automation of legal review.
[0004] Therefore, how to establish logical constraint boundaries in unstructured legal texts and identify long-distance semantic logical conflicts across paragraphs is the technical problem that this invention aims to solve. Summary of the Invention
[0005] To address the problems raised in the background art, the technical solution of the present invention is as follows: A legal application document intelligent review system based on natural language processing, comprising: The semantic proposition unit extraction module is used to acquire the legal document to be tested and extract semantic proposition units, which include legal subjects, behavioral predicates, and logical polarity labels. The semantic association graph construction module is used to perform the following steps to generate a semantic association graph: Step 101, map semantic propositional units to topological nodes; Step 102, establish association edges between topological nodes based on the connecting words and dependency relationships in the legal document to be tested. The semantic polarity transfer module is used to identify the polarity source node corresponding to the core obligation clause in the semantic association graph. The polarity source node is a specific node in the topological node, and an initial weight score is set for the polarity source node according to the legal hierarchy. The semantic polarity transfer module uses a linear superposition operator to calculate the attenuation propagation of the initial weight score along the associated edge. Each associated edge is based on an attenuation coefficient that represents the logical loss of context transformation. The semantic polarity transfer module identifies the broken nodes with weight scores lower than the preset threshold and logical polarity label conflicts by calculating the distribution of the initial weight scores in the semantic association graph. The logical consistency verification module is used to parse broken nodes and construct cross-paragraph association edges by combining semantic feature vectors. Based on the attenuation coefficient of the cross-paragraph association edges, the logical consistency verification module performs weighted mapping on the final weight score of the broken nodes and outputs the review results.
[0006] Preferably, when the semantic polarity transfer module performs weight decay calculation, the calculation rule for the node weight value is as follows: the semantic polarity transfer module obtains all upstream paths of the target node, calculates the total weight transmission of each path, and linearly superimposes the multi-path transmission results to determine the final weight value of the target node. The decay coefficient is dynamically weighted according to the context transformation depth and referential distance corresponding to the associated edge.
[0007] Preferably, it also includes: a logic conflict injection module, used to inject virtual conflict data into the core nodes of the semantic association graph to simulate the association reaction of the legal logic chain under extreme conditions; and a propagation path analysis module, used to capture the polarity fluctuation points generated by the virtual conflict data in the transmission path to locate the logical discontinuity of the legal document under test at the execution boundary.
[0008] Preferably, when the semantic proposition unit extraction module extracts logical polarity tags, the logical polarity tags include four preset polarity types: allow, prohibit, obligation, and right. The semantic proposition unit extraction module identifies the polarity attribute of the behavior predicate through the semantic parser and performs atomic tagging.
[0009] Preferably, the semantic association graph construction module performs semantic completion on the topology nodes through a legal knowledge graph library. When a logical jump is detected in the legal document to be tested, virtual semantic nodes are inserted between the topology nodes.
[0010] Preferably, when the logical consistency verification module constructs cross-paragraph association edges, it includes the following steps: Step 601, extracting legal entities with the same semantic features from different paragraphs of the legal document to be tested; Step 602, calculating the logical association density between legal entities; Step 603, when the logical association density exceeds a preset threshold, establishing cross-paragraph association edges between legal entities.
[0011] Preferably, the semantic polarity transfer module calculates the weight decay. The formula is as follows: ,in, This represents the initial weight score for the polarity source node. For the first in the conduction path The decay coefficient of the associated edges, This represents the total number of associated edges between the source node and the target node.
[0012] Preferably, the logical consistency verification module is also used to identify the mutually exclusive rights and obligations between topology nodes. When two paths with opposite logical polarity labels point to the same legal entity, a logical conflict warning is triggered.
[0013] Preferably, when the semantic polarity transfer module calculates the weight score, the initial weight score is quantified according to the legal level to which the legal document to be tested belongs, and the initial weight score corresponding to mandatory norms is higher than that corresponding to conventional norms.
[0014] Preferably, the logical consistency verification module further includes an adaptive adjustment unit, which performs the following operations: when the character size of the legal document to be tested exceeds a preset limit... When the character threshold is reached, the adaptive adjustment unit switches the recursive parsing depth of the broken node from the first level to the second level to achieve a balance between review accuracy and computational overhead.
[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In natural language processing, the semantic proposition deconstruction module and the topology graph construction module transform unstructured legal documents into semantic topology graphs that carry logical polarity. By using tension weights to represent the hard logical constraints between nodes, legal documents are transformed from simple word perception to semantic logical deduction, suppressing the phenomenon of semantic logical entropy increase caused by long narratives, and providing clear logical deduction basis for review conclusions.
[0016] 2. The implicit node completion module, based on prior knowledge of legal logic, projects corresponding virtual obligation nodes onto the semantic space for rights-related semantic nodes. It searches for semantic support paths to identify missing logical loops in documents, perceives semantics that should exist but are not present in the document, and recalls implicit legal risk points formed by concealing restrictive conditions, eliminating blind spots in review caused by explicit matching. The logic incentive injection module and stress transmission analysis module construct a dynamic logic elasticity testing mechanism, injecting virtual disturbance events into the core nodes of the semantic topology map, simulating the cascading reaction of the legal logic chain under extreme conditions, capturing the polar oscillation points generated by the transmission path, locating logical breaks in the document at the execution boundary, and completing the transformation from static form comparison to dynamic logic robustness detection, so that legal documents maintain the legal logic loop in complex execution environments.
[0017] 3. The logical consistency verification module recursively decomposes logical gray zone nodes, constructs equivalent edges of virtual anchor points across paragraphs by combining semantic fingerprint vectors, calibrates the topological offset at the micro scale, and performs global consistency compensation by using polarity attenuation coefficient. This reduces the recognition deviation caused by semantic drift of legal entities or ambiguity of abstract terms, and enables the review accuracy to adaptively adjust with the logical complexity of the document, thereby improving the system's anti-interference ability and recognition accuracy in handling ultra-large-scale complex documents. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the entire process of the intelligent review system for semantic association and logical verification of the present invention. Figure 2 This is a performance response diagram of the weight attenuation and recognition accuracy of the system of the present invention under different character spans; Figure 3 This is a diagram showing the physical hardware deployment and software / hardware interaction topology of the intelligent review system of this invention. Detailed Implementation
[0019] The following embodiments are used to explain and illustrate the present invention, and do not constitute a limitation on the scope of protection of the present invention. Those skilled in the art should understand that any modifications or equivalent substitutions made to the technical solutions of the present invention without departing from the essence of the present invention shall fall within the scope of protection of the present invention.
[0020] This invention provides an intelligent review system for legal application documents based on natural language processing. It comprises a semantic proposition unit extraction module, a semantic association graph construction module, a semantic polarity transfer module, and a logical consistency verification module. The semantic proposition unit extraction module acquires the legal document to be tested and deconstructs it into topological nodes carrying logical polarities. The semantic association graph construction module establishes association edges between nodes using dependency relationships. The semantic polarity transfer module identifies weight decay breakpoints in the graph by simulating polarity potential energy transmission. The logical consistency verification module combines cross-paragraph association edges and semantic feature vectors to complete the calibration of logical self-consistency and output the review results. This system addresses the semantic and logical entropy increase phenomenon that exists in lengthy legal documents, leading to the loss of rights and obligations in unstructured text. Since it is difficult to establish stable constraint boundaries for the relationship between obligations, the semantic proposition unit extraction module uses a semantic parsing operator to perform atomic representation of the legal document under test. This module uses a preset legal knowledge dictionary to process the text paragraphs into sentences, identify the core legal subject and behavioral predicate in each sentence, and label the corresponding logical polarity based on the semantic attributes of the behavioral predicate. The logical polarity labels include four preset polarity types: permission, prohibition, obligation, and right. For example, when processing a statement containing "the lessee should pay rent on time", the system identifies the legal subject as the lessee, the behavioral predicate as "pay rent", and marks its logical polarity label as "obligation" according to the mandatory component. Through this procedure, the system converts unstructured text into semantic proposition units with definite logical orientation.
[0021] After obtaining the semantic propositional units, the semantic association graph construction module maps them as topological nodes. Based on the connectors and syntactic dependency relationships in the document, this module establishes directional association edges between the topological nodes. Each association edge is based on a decay coefficient representing the logical loss of context transformation. attenuation coefficient The calculation method is as follows: ,in, The attenuation coefficient; This represents the physical span between two topological nodes in the text space, i.e., the character span. The number of semantic units between two nodes; and The preset weighting adjustment factor is used to adjust the influence weights of physical distance and semantic depth. This process constructs a global topological map that carries semantic tension, transforming legal documents from simple lexical perception into a logical deductive structure with spatial constraints; regarding the attenuation coefficient... Engineering calibration and multi-path integration: The system retrieves fifty pre-annotated legal document benchmark sample sets and statistically analyzes different physical spans. The logical conflict recall distribution will be adjusted by the weighting factor. Set as Weighting adjustment factor Set as When multiple independent logical paths point to the same target node, the semantic polarity transfer module obtains... Candidate paths, using the formula Calculate the attenuation score for each path, where Indicates the first Path attenuation score, This represents the initial weight score of the polarity source node. Indicates the first in the path The attenuation coefficient of each associated edge This represents the total number of edges between the source node and the target node; the semantic polarity transitive module performs a linear superposition operation to determine the final weight value of the target node. ,in This represents the final weight value of the target node. This represents the normalized contribution coefficient determined based on the proportion of mandatory specification nodes in the path, eliminating the bias in the assessment of logical constraint strength caused by single-path calculation.
[0022] To address the logical jumps caused by the omission of necessary legal premises in the legal documents under test, the semantic association graph construction module executes a virtual node injection procedure. When establishing connections between topological nodes, the system retrieves the shortest path distance between adjacent nodes in the legal ontology space by calling the legal knowledge graph database. When two logically dependent nodes lack direct connecting words in the physical text, and the path span of their semantic feature vectors exceeds a preset step size threshold, a logical jump is identified. The system extracts the missing intermediate legal entities from the legal knowledge graph database as virtual semantic nodes and inserts them between the topological nodes. Based on preset weights in the ontology database, a transit attenuation coefficient is assigned to the virtual nodes, thus completing the logical loop in the semantic space. Since legal language experiences intensity attenuation during transmission, the semantic polarity transfer module identifies the polarity source nodes corresponding to core obligation clauses. The system sets initial weight scores for the polarity source nodes based on the legal level to which the legal document under test belongs. The initial weight score corresponding to the standard is set as follows: The initial weight score corresponding to the convention specification is set as follows: The semantic polarity propagation module uses a linear superposition operator to calculate the decay propagation of the initial weight scores along the associated edges, and the weight decay amount. The calculation logic is as follows: ,in, This represents the weight decay. This represents the initial weight score for the polarity source node; For the first in the conduction path The attenuation coefficient of the associated edges, whose value range is: ; The system obtains all upstream paths of the target node and performs linear superposition of the multi-path propagation results. When the final weight score of the target node is found to be lower than the preset logic robustness threshold, and the logic polarity actually carried by the target node is opposite to the inductive polarity transmitted from it, the system determines the node as a broken node.
[0023] Regarding the procedures for virtual semantic node injection and cross-paragraph logical anchoring, the semantic association graph construction module extracts semantic feature vectors between adjacent topological nodes. The cosine distance algorithm is used to calculate the similarity between nodes. ,in This represents the node vector representation output by the predefined legal parsing operator. Represents the similarity score; identified Below the preset threshold Furthermore, when the shortest logical step number of a topological node in the legal knowledge graph B1 is greater than or equal to two, a semantic gap is determined to exist. The legal entity located at the midpoint of the shortest logical step path in the legal knowledge graph B1 is automatically retrieved as a virtual semantic node and inserted into the semantic association graph. When the logical consistency verification module constructs cross-paragraph association edges, it extracts the contextual dependency features of legal entities from different paragraphs to generate semantic fingerprints and calculates the Euclidean distance within the fingerprint space. This distance is less than the preset clustering radius. At that time, cross-paragraph association edges are established, and attenuation coefficients are dynamically allocated based on the character scale spanned by the edge. Establish constraint boundaries that possess engineering stability and logical consistency during the review process of lengthy documents.
[0024] To correct logical recognition biases caused by contextual changes, the logical consistency verification module extracts semantic fingerprint vectors of legal entities in different paragraphs, including the contextual dependency features of the entities. When the fingerprint vector distance between two cross-paragraph nodes meets the consistency criterion, the system adds cross-paragraph association edges, i.e., virtual anchor equivalent edges, to the topology graph. The system performs a weighted mapping on the final weight score of the broken node based on the attenuation coefficient of the cross-paragraph association edges. When it is detected that the factual statement at the beginning of the document and the liability clause at the end of the document are mutually exclusive in polarity on the logical path, and the path potential energy attenuation is significant... Exceeding the preset When a critical value is reached, the system triggers a logical conflict warning and locks the corresponding text space coordinates. The logical consistency verification module has an adaptive adjustment unit to balance the computational overhead during large-scale document processing. When the character size of the legal document under test exceeds a certain threshold... When the character threshold is reached, the adaptive adjustment unit switches the recursive parsing depth of the broken node from the first level to the second level. The depth parsing operator uses the legal ontology feature vector to decompose the abstract proposition node into a group of micro-sub-nodes containing spatiotemporal constraints, magnitude modifications, and preconditions. The system reconstructs the logical edges between sub-nodes at the micro scale and calibrates the topological offset of the output based on the micro polarity response results to ensure the recognition accuracy of the system when processing complex long documents.
[0025] Example 1: In a partnership agreement review scenario involving multiple cross-border investment entities, the total length of the legal document under test exceeds 150 pages and has a high logical density. The partnership agreement establishes the partner's right to withdraw under specific triggering conditions in Chapter 5, but in Chapter 88, regarding risk compensation, the exercise of the aforementioned right is implicitly restricted through asset preservation clauses. Due to the semantic logical entropy increase phenomenon in the long-distance narrative of such documents, the rights and obligations relationships in unstructured texts are difficult to identify through static matching. The system's semantic proposition unit extraction module performs atomic deconstruction on the partnership agreement, uses legal semantic parsing operators to identify legal subjects and behavioral predicates in the text, and identifies statements mentioning the right to withdraw as topological nodes. Based on the semantic attributes of behavioral predicates and corresponding logical polarity labels, the semantic association graph construction module establishes directional association edges between topological nodes based on the connectors and dependency paths in the document.
[0026] The system identified the polarity source node corresponding to the partner withdrawal clause. Since the partnership agreement is a contractual regulation, the system assigned an initial weight score to this polarity source node. for The semantic polarity transfer module uses a linear superposition operator to calculate the distribution of weight scores along the path. This process uses the following formula to calculate the weight decay. : ,in, This represents the weight decay. This represents the initial weight score for the polarity source node; This represents the total number of associated edges; For the first in the conduction path The decay coefficient of a related edge increases with the depth of contextual transformation and the distance of reference in a path with a large span. The dynamic weight allocation resulted in the weight score falling below the preset logical robustness threshold when it was passed to Chapter 88. At this point, the logical consistency verification module extracts the semantic fingerprint vector of the partner entity and calculates the logical association density between entities across paragraphs. When the fingerprint vector distance meets the consistency criterion, the system adds cross-paragraph association edges, i.e., virtual anchor equivalent edges, to the topology graph. The system detects that the same partner entity is based on opposite logical states on different legal paths, i.e., the right polarity corresponding to the withdrawal path and the restriction polarity corresponding to the asset preservation path are mutually exclusive. The logical consistency verification module identifies paths with opposite logical polarity labels that point to the same legal entity, triggers a logical conflict warning, and locks the corresponding text space coordinates. By introducing a semantic polarity potential field transmission mechanism, the system transforms the implicit semantic breakpoints that were originally in a logically silent state into definite topological residuals, thereby achieving logical convergence of the global semantics of the legal document.
[0027] Example 2: In the experiment verifying the efficiency and accuracy of logical consistency verification in legal documents, the experiment focused on calibrating semantic recognition bias under long-distance contextual transformation. The experimental platform was built based on a computing unit with floating-point processing capabilities and was equipped with... The random access memory, the operating environment simulates real review conditions by deploying legal semantic parsing operators, and the experimental data comes from sources including A closed dataset of commercial contracts and legal application documents was used to simulate data noise in a real-world application environment. To achieve this, the system actively superimposed a signal-to-noise ratio of [value missing] during the semantic recognition stage. The system uses Gaussian white noise and a hash mapping algorithm to de-identify the names and contact information of natural persons in documents, ensuring that the data processing is at the level of physical parameters and de-identified data. The decision logic chain for setting core parameters is based on the initial weight score. and attenuation coefficient As the core, the initial weight score The initial weight score is chosen to balance the strength of logical transmission with the risk of computational saturation. When the legal document under test belongs to a mandatory legal level, the initial weight score is set to ensure that the core obligation clauses still have logical binding force after transmission. Set as attenuation coefficient Subject to the physical span of topology nodes With the number of semantic units Constraints, the system is based on the formula Real-time calibration is performed, including weight adjustment factors. for , for .
[0028] The experimental group operated according to the specific implementation procedures, capturing legal subjects and behavioral predicates in the text through the semantic proposition unit extraction module, and calculating the weight decay using the linear superposition operator. When processing a complex text involving asset mortgage rights and limitations on liability for breach of contract, the system identifies the initial mortgage node at the beginning of the text and the liability exemption node at the end. It also observes the response trends of weight scores under different configurations. Table 1 shows the verification data for the accuracy of identifying logical conflicts in legal application documents. The control group uses conventional natural language processing methods, while the partially missing control group removes cross-paragraph association edges and logical conflict injection modules. The out-of-range control group tests the initial weight score. Cases deviating from the preset range and fixed resolution depth.
[0029] Table 1: System performance in identifying logical conflicts in legal documents under different configurations and operating conditions. in This represents the initial weight score for the polarity source node. The weight decay amount is calculated by the system, and the recognition accuracy is the matching ratio between the identified logical conflict nodes and the pre-set manually labeled conflict tags. According to the data in Table 1, after introducing semantic association graphs and polarity transfer mechanisms, the recognition accuracy increased from... Upgraded to After removing cross-paragraph associative edges, for characters spanning more than [a certain length], The logical dependency resulted in a decrease in recognition accuracy, confirming the role of virtual anchor point equivalent edges in calibrating semantic drift. Data from the out-of-range control group showed that when the initial weight score... Increase to At that time, the system experienced a saturation effect, and the recognition accuracy dropped to [a certain value]. For characters spanning more than The document, exceeding the range of the second control group, showed an accuracy of [percentage missing] when the first level of recursion depth was [percentage missing]. However, after switching to the second level of depth resolution, the accuracy of the experimental group rebounded to [percentage missing]. .
[0030] Example 3: This example combines Figures 1 to 3 This describes an intelligent review system for legal application documents based on natural language processing, such as... Figure 1 As shown, the processing flow of this intelligent legal application document review system begins with the user or applicant submitting the legal document to be tested. The semantic proposition unit extraction module receives the document and outputs semantic proposition units containing legal subjects, behavioral predicates, and logical polarity labels. The semantic association graph construction module combines the semantic completion and virtual node data provided by the B1 legal knowledge graph library to generate a semantic association graph composed of topological nodes and related edges. The semantic polarity transfer module performs calculations based on the initial weight score ES set by the B2 legal hierarchy weight rules, identifies polarity break nodes with weights below the threshold, and finally, the logical consistency verification module completes the verification by combining cross-paragraph related edges and weighted mapping data, and outputs the logical self-consistency review results to the examiner or client.
[0031] like Figure 2 As shown, the horizontal axis represents the character span D, and the vertical axis represents the numerical value. The bar chart distinguishes between the weight attenuation L for filled diagonal lines and the percentage of recognition accuracy for filled grids. The data presents multiple sets of comparative scenarios. For example, when the character span D is 1528, the weight attenuation L is 32.6, and the recognition accuracy reaches its highest value. When the character span D increases to 12500, although the weight attenuation L increases, the recognition accuracy remains at a high level. The graph clearly shows the data distribution of multiple discrete test points, including 1245, 1528, 8562, 4231, 4567, and 12500. Figure 3As shown, the core of the system's hardware architecture is an intelligent review computing server, which is equipped with a floating-point processing unit and 16GB of random access memory. Internally, it deploys a legal semantic parsing runtime environment, including a semantic proposition unit extraction component, a semantic association graph construction component, a semantic polarity transfer calculation component, and a logical consistency verification component. This server is connected to the examiner's terminal device on the left, including a PC workstation or mobile terminal, through a secure network transmission link. Its interface includes a review interaction interface, a document upload interface, and a large screen for visualizing review results. At the same time, it is connected to the knowledge graph and data storage server through a high-speed data read and write channel below. The storage server contains a legal knowledge graph library that stores entity and relation triples, as well as a legal hierarchy weight rule library that stores mandatory and contractual norms.
[0032] Example 4: In the intelligent review scenario of intellectual property licensing contracts for high-tech enterprises, the legal document to be tested contains multiple abstract clauses involving necessary security measures and reasonable usage rates. Since the logical polarity of such expressions is neutral biased in the initial atomization representation, and its semantic features cannot trigger logical conflicts under the first level of parsing, the system determines it as a logical gray zone node. In order to determine whether such nodes have legal risks, the system initiates a recursive semantic resolution trigger calibration procedure. The legal subject and behavioral predicate of the node are obtained through the semantic proposition unit extraction module and input into the legal knowledge graph library. It is organized according to the triple structure of entity, relation and attribute. The system extracts the semantic feature vector of the behavioral predicate and performs a nearest neighbor search in the ontology library to identify its corresponding subdivision attributes.
[0033] To achieve logical reconstruction at the local scale, the deep parsing operator decomposes a single proposition node into a micro-level sub-node group consisting of spatiotemporal constraint sub-nodes, magnitude modification sub-nodes, and conditional precondition sub-nodes. The specific decomposition steps include: retrieving a pre-defined legal ontology database, extracting spatiotemporal constraint operators related to necessary security measures, and generating constraint nodes containing performance periods and implementation regions; extracting magnitude modification operators to generate magnitude nodes representing technical standard levels; establishing subordinate association edges between the micro-level sub-node groups; recalculating local tension based on the logical polarity of each sub-node; and determining the existence of high-level logical conflicts when the polarity orientation of the deconstructed micro-subgraph deviates non-linearly from the core obligation clauses. The determination of logical gray zone nodes is based on a calibration procedure based on topological offset and information entropy. The system calculates the subgraph mapping residual. ,when satisfy When the interval is defined, the region is determined to be a logical gray area, and the lower limit of this interval is... It is a random semantic noise baseline value obtained by sampling and calculating known compliant documents, with an upper limit. This is the critical damping value that triggers the global consistency check failure; during the recursive parsing process, the system introduces a deep parsing gain coefficient. It is determined by the following linear relationship: ,in, For depth-resolution gain coefficients, The total character size of the legal document to be tested; The preset computational cost adjustment factor has a fixed value. The system according to The system dynamically adjusts the tension contribution value between child nodes to compensate for recognition biases caused by contextual changes. Through this procedure, the system reconstructs the logical edges between child node groups at a local scale, locking in implicit legal risks hidden in abstract modifiers. Experimental results show that after applying this recursive calibration procedure, the system's recall rate for implicit semantic breakpoints in long documents containing ambiguous legal terms has increased to [percentage missing]. .
[0034] Example 5: In deploying the review system in the scenario of reviewing foreign-related maritime legal documents, the maritime legal system has different attributes from general commercial contracts in terms of legal hierarchy and semantic relevance density. Before the system runs, a standardized pre-calibration procedure is performed by importing a sample set containing 50 pre-annotated maritime cases, and statistical distribution is used to calculate different character spans. The logical polarity decay benchmark is set below, and the weight adjustment factor is adjusted according to the calculation results. as well as The system matches the weight decay amount. Given the response value at the known conflict point, The value is determined by Adjust to meet the detection sensitivity requirements. And simultaneously, the initial weight scores corresponding to the agreed-upon specifications will be applied. from Calibrated to meet the constraint strength of this field This system achieves the matching of logical potential energy transmission models with the syntactic features of maritime texts at the physical parameter level. The internal legal knowledge graph is constructed through an offline data filling procedure, extracting legal subjects and core behavioral predicates in the maritime field. Based on the direction of influence of these behavioral predicates on legal relationships, they are converted into polar feature vectors containing atomic markers. The system performs triple mapping on the injected semantic proposition units, calculating the cosine similarity between candidate predicates and the existing vector library, and setting a value no lower than... The matching threshold is used to classify eligible behavioral predicates into four preset polarity labels: allow, prohibit, obligation, or right. This process uses the reviewed corpus to support the semantic association graph construction module to generate association edges, eliminate logical recognition bias caused by ambiguity in the definition within the knowledge base, and enable the system to output traceable topological offsets when processing unstructured text.
[0035] During the deployment of intelligent review of contracts involving mega-scale infrastructure construction projects, the system executes procedures to determine logical stability thresholds. The pre-calibration procedure involves retrieving 100 reviewed compliance documents as a baseline sample set; using the semantic polarity transfer module, the weight score of each topological node in the baseline sample set at the end of the transmission path is calculated, and the expected value and variance of the weight distribution are statistically analyzed. Based on the lower margin confidence interval of the score distribution, the calibration is completed. The calibration ensures the initial weight scores of the polarity source node outputs. The fluctuations attenuated along the associated edges and propagated to the target node are within the normal range. This process provides a physical reference benchmark for identifying semantic breaks in documents caused by contextual shifts. The system executes a semantic feature fingerprint consistency judgment procedure for the construction of cross-paragraph associated edges. It defines that the deviation between the semantic fingerprint vectors of legal entities in different paragraphs must meet the preset consistency limit. The determination of the vector bias threshold is based on the cluster analysis of legal entities in the multi-dimensional feature space. In the initial state, entity samples with the same legal features are extracted. The vector bias is calibrated by calculating the cluster center and dispersion of the fingerprint vector in the semantic space. When the path with the same semantic features points to the same legal subject and the vector distance is within the calibration range, the logical consistency verification module adds a virtual anchor point equivalent edge in the topology graph. The attenuation coefficient carried by the equivalent edge is used to perform a weighted mapping on the final weight score of the broken node, thereby completing the global consistency verification of the legal document.
[0036] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A natural language processing-based legal application document intelligent review system, characterized in that, include: The semantic proposition unit extraction module is used to acquire the legal document to be tested and extract semantic proposition units, which include legal subjects, behavioral predicates, and logical polarity labels. The semantic association graph construction module is used to perform the following steps to generate a semantic association graph: Step 101, map semantic propositional units to topological nodes; Step 102, establish association edges between topological nodes based on the connecting words and dependency relationships in the legal document to be tested. The semantic polarity transfer module is used to identify the polarity source node corresponding to the core obligation clause in the semantic association graph. The polarity source node is a specific node in the topological node, and an initial weight score is set for the polarity source node according to the legal hierarchy. The semantic polarity transfer module uses a linear superposition operator to calculate the attenuation propagation of the initial weight score along the associated edge. Each associated edge is based on an attenuation coefficient that represents the logical loss of context transformation. The semantic polarity transfer module identifies the broken nodes with weight scores lower than the preset threshold and logical polarity label conflicts by calculating the distribution of the initial weight scores in the semantic association graph. The logical consistency verification module is used to parse broken nodes and construct cross-paragraph association edges by combining semantic feature vectors. Based on the attenuation coefficient of the cross-paragraph association edges, the logical consistency verification module performs weighted mapping on the final weight score of the broken nodes and outputs the review results.
2. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, When the semantic polarity propagation module performs weight decay calculation, the calculation rules for node weight values are as follows: The semantic polarity propagation module obtains all upstream paths of the target node, calculates the total weight propagation of each path, and linearly superimposes the multi-path propagation results to determine the final weight value of the target node. The decay coefficient is dynamically weighted according to the context transformation depth and referential distance corresponding to the associated edge.
3. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, It also includes: a logic conflict injection module, used to inject virtual conflict data into the core nodes of the semantic association graph to simulate the association reaction of the legal logic chain under extreme conditions; and a propagation path analysis module, used to capture the polarity fluctuation points generated by the virtual conflict data in the transmission path to locate the logical discontinuity of the legal document under test at the execution boundary.
4. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, When the semantic proposition unit extraction module extracts logical polarity tags, the logical polarity tags include four preset polarity types: allow, prohibit, obligation, and right. The semantic proposition unit extraction module identifies the polarity attribute of the behavior predicate through the semantic parser and performs atomic tagging.
5. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, The semantic association graph construction module performs semantic completion on the topology nodes through the legal knowledge graph library. When a logical jump is detected in the legal document to be tested, virtual semantic nodes are inserted between the topology nodes. 6.The natural language processing-based legal application document intelligent review system according to claim 1, wherein, When constructing cross-paragraph association edges, the logical consistency verification module includes the following steps: Step 601, extract legal entities with the same semantic features from different paragraphs of the legal document to be tested; Step 602, calculate the logical association density between legal entities; Step 603, when the logical association density exceeds a preset threshold, establish cross-paragraph association edges between legal entities.
7. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, The semantic polarity transfer module calculates the weight decay. The formula is as follows: ,in, This represents the initial weight score for the polarity source node. For the first in the conduction path The decay coefficient of the associated edges, This represents the total number of associated edges between the source node and the target node.
8. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, The logical consistency verification module is also used to identify mutually exclusive rights and obligations between topology nodes. When two paths with opposite logical polarity labels point to the same legal entity, a logical conflict warning is triggered.
9. The intelligent review system for legal application documents based on natural language processing according to claim 1, characterized in that, When the semantic polarity transfer module calculates the weight score, the initial weight score is quantified according to the legal level to which the legal document under test belongs. The initial weight score corresponding to mandatory norms is higher than that corresponding to conventional norms. 10.The natural language processing based legal application document intelligent review system according to claim 1, wherein, The logical consistency verification module also includes an adaptive adjustment unit, which performs the following operations: when the character size of the legal document under test exceeds a preset limit... When the character threshold is reached, the adaptive adjustment unit switches the recursive parsing depth of the broken node from the first level to the second level.