Contract review method and device, equipment, storage medium and product

Abstract

The invention discloses a contract review method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the contract review method comprises the steps: obtaining a target contract text in response to a review request; and carrying out fragmentation processing on the target contract text through a preset contract review model, and carrying out deep semantic analysis on review fragments obtained through processing to obtain a contract review result. According to the method, deep semantic analysis is carried out on the obtained target contract text through the preset contract review model, and the method is not simply limited to keyword matching or simple semantic understanding, so that the contract review result is obtained, and the contract review accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a contract review method, device, equipment, storage medium and product. Background Art

[0002] In the banking industry, contract review is a crucial task that involves multiple aspects such as legal compliance, risk control and customer rights protection.

[0003] In the related art, contract review is performed through a contract review system based on rules and machine learning. However, the related art is often limited to keyword matching or simple semantic understanding, which makes it difficult to deal with the complex logical relationships and implied terms in the contract text, thereby reducing the accuracy of the contract review. Summary of the invention

[0004] The main purpose of this application is to provide a contract review method, device, equipment, storage medium and product, aiming to solve the technical problems in related technologies that are often limited to keyword matching or simple semantic understanding, and have difficulty in dealing with complex logical relationships and implied terms in contract texts, thereby reducing the accuracy of contract review.

[0005] To achieve the above objectives, the present application proposes a contract review method, which includes:

[0006] Responding to the review request, obtaining the target contract text;

[0007] The target contract text is segmented through a preset contract review model, and deep semantic analysis is performed on the processed review segments to obtain the contract review results.

[0008] In one embodiment, the step of performing segmentation processing on the target contract text through a preset contract review model, and performing deep semantic analysis on the processed review segments to obtain the contract review result includes:

[0009] Preprocessing the target contract text to obtain processed contract segments and segment vectors corresponding to the contract segments, wherein the contract segments include titles and keywords;

[0010] Performing recall processing on the slice vector to obtain the first review slice;

[0011] Based on the preset review point, full text processing is performed through a preset reading processing sub-model in the preset contract review model to obtain the second review segment;

[0012] The first review segment and the second review segment are input into the preset contract review sub-model in the preset contract review model for deep semantic analysis to obtain the contract review result.

[0013] In one embodiment, the step of performing full text processing based on the preset review point through a preset reading processing sub-model in the preset contract review model to obtain the second review segment includes:

[0014] Inputting the contract segments into the preset contract review model, reading each of the contract segments paragraph by paragraph, extracting the fields related to the preset review points, and generating target contract segments based on the extracted target fields;

[0015] The target contract segments are spliced ​​together to obtain the spliced ​​second review segments.

[0016] In one embodiment, the step of performing recall processing on the slice vector to obtain the first review slice includes:

[0017] Establishing an index based on the text vector and the contract fragment corresponding to the text vector;

[0018] Preliminarily screening the contract shards through the preset review points and the index to obtain initial contract shards;

[0019] The initial contract segment is accurately located to obtain the excerpt field related to the preset review point, and a first review segment is generated based on the excerpt field.

[0020] In one embodiment, the step of segmenting the target contract text by a preset contract review model, and performing deep semantic analysis on the processed review segments to obtain the contract review results includes:

[0021] Based on the contract review result, a contract review report is generated, and the contract review report is sent to the target user, so that the target user can review the contract review report and provide feedback on the review opinion;

[0022] Based on the review opinions, a target review report is generated.

[0023] In one embodiment, the step of preprocessing the target contract text to obtain processed contract fragments and fragment vectors corresponding to the contract fragments includes:

[0024] Based on a preset word count range, the target contract text is segmented to obtain initial contract segments;

[0025] Extracting titles and keywords from the initial contract fragments to obtain contract fragments including keywords and titles;

[0026] Convert the contract shards into shard vectors.

[0027] In addition, to achieve the above purpose, the present application also proposes a contract review method device, the contract review device comprising:

[0028] A response module, used for responding to the review request and obtaining the target contract text;

[0029] The review module is used to segment the target contract text through a preset contract review model, and perform deep semantic analysis on the processed review segments to obtain the contract review results.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a contract review method device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the contract review method described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the contract review method described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the contract review method described above.

[0033] One or more technical solutions proposed in this application have at least the following technical effects:

[0034] Compared with the related art, which is often limited to keyword matching or simple semantic understanding, and has difficulty in dealing with the complex logical relationships and implied terms in the contract text, thereby reducing the accuracy of the contract review, the present application obtains the target contract text by responding to the review request; segments the target contract text through a preset contract review model, and performs deep semantic analysis on the processed review segments to obtain the contract review results. It can be understood that the present application performs deep semantic analysis on the obtained target contract text through a preset contract review model, and is not simply limited to keyword matching or simple semantic understanding, thereby obtaining the contract review results and improving the accuracy of the contract review. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 A flowchart of the first embodiment of the contract review method of this application is provided;

[0038] Figure 2 The overall flow chart of the contract review method for this application is provided;

[0039] Figure 3 A brief flowchart of the contract review method provided in Example 2 of the present application;

[0040] Figure 4 This is a schematic diagram of the module structure of the contract review device in the embodiment of the present application;

[0041] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the contract review method in the embodiment of the present application.

[0042] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0044] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0045] The main solution of the embodiment of the present application is: in response to a review request, obtain the target contract text; through a preset contract review model, segment the target contract text, and perform deep semantic analysis on the processed review segments to obtain the contract review results.

[0046] In the related art, contract review is performed through a contract review system based on rules and machine learning. However, the related art is often limited to keyword matching or simple semantic understanding, which makes it difficult to deal with the complex logical relationships and implied terms in the contract text, thereby reducing the accuracy of the contract review.

[0047] This application uses a preset contract review model to perform in-depth semantic analysis on the acquired target contract text, which is not simply limited to keyword matching or simple semantic understanding. In turn, the contract review results are obtained, thereby improving the accuracy of the contract review.

[0048] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the contract review device as an example to illustrate this embodiment and the following embodiments.

[0049] Based on this, the present application embodiment provides a contract review method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the contract review method of this application.

[0050] In this embodiment, refer to Figure 2 , Figure 2 An overall flow chart is provided, wherein the contract review method comprises steps S100 to S200:

[0051] Step S100, in response to the review request, obtaining the target contract text;

[0052] It should be noted that the execution subject of this embodiment is a contract review device, which is provided with a client, and the client is used to upload the contract text and trigger the review request. The target contract text is the contract text for review. After receiving the review request triggered by the client, the contract review device will obtain the target contract text for review based on the review request.

[0053] Step S200, segmenting the target contract text through a preset contract review model, and performing deep semantic analysis on the processed review segments to obtain the contract review results.

[0054] It is understandable that the preset contract review model is a pre-trained large-scale language model, which can perform deep semantic analysis and identify key terms, professional terms, and logical relationships in the contract. After obtaining the target contract text, the contract review device will perform deep semantic analysis on the target contract text through the preset contract review model, identify key terms, professional terms, and logical relationships in the contract, and then obtain the contract review result.

[0055] In a feasible implementation, the target contract text is segmented by a preset contract review model, and deep semantic analysis is performed on the processed review segments to obtain the contract review result, including the following steps:

[0056] Preprocessing the target contract text to obtain processed contract segments and segment vectors corresponding to the contract segments, wherein the contract segments include titles and keywords;

[0057] It should be noted that the title is used to help the model understand the contract structure. Keywords are used to assist in quickly locating key content. With the assistance of keywords and titles, the model can quickly perform in-depth semantic analysis. Contract segments are segments obtained based on the segmentation of the target contract text, and each contract segment has a corresponding title and keyword. The segment vector is the segment vector obtained after the contract segment is vectorized. After obtaining the target contract text, the contract review device will pre-process the target contract text to obtain the contract segments obtained after the target contract text is segmented, and each contract segment has a corresponding title and keyword. At the same time, the segment vector is obtained after the contract segment is vectorized.

[0058] Performing recall processing on the slice vector to obtain the first review slice;

[0059] It is understandable that the preset recall review model can recall and screen the contract shards, and then obtain the first review shards. The first review point includes review points such as compliance of contract terms and the use of specific legal terms. The contract review device obtains the shard vector after vectorizing the contract shards based on the review points such as compliance of contract terms and the use of specific legal terms, and the contract shards, and according to the association between the shard vector and the contract shards, the preset recall review model can be used to recall and screen the contract shards to obtain the first review shard for in-depth review.

[0060] Based on the preset review point, full text processing is performed through a preset reading processing sub-model in the preset contract review model to obtain the second review segment;

[0061] It should be noted that the preset segment review model can screen the contract segments to obtain the contract segments for review. After the contract segments are input into the preset segment review model, the contract review device will screen the contract segments based on the preset review points to obtain the contract segments for review.

[0062] The first review segment and the second review segment are input into the preset contract review sub-model in the preset contract review model for deep semantic analysis to obtain the contract review result.

[0063] It is understandable that the preset contract review model has been trained on a large amount of text and has strong language understanding and generation capabilities. It can deeply understand the semantics of the contract text, identify implicit terms, and perform logical reasoning to improve the accuracy and comprehensiveness of the review. The model has a certain logical reasoning ability and can identify the logical relationship between contract terms to ensure the consistency and integrity of the contract. The contract review device uses the preset contract review model to perform in-depth semantic analysis on the first review segment and the second review segment obtained by screening the preset recall review model and the preset segment review model, understand the semantics of the contract text, identify implicit terms, and perform logical reasoning, and then obtain the contract review result.

[0064] Furthermore, the contract review model preset by the contract review device can accurately identify and understand the meaning of professional terms and specific conditions in banking business, avoid review errors caused by misunderstanding of terminology, and through automated processing, greatly reduce the workload of manual review, improve review efficiency, and reduce the risk of human error.

[0065] In particular, the contract review device is also provided with a feedback mechanism for receiving user feedback. Through user feedback, the deficiencies of the model in actual application can be discovered and timely corrections and optimizations can be made.

[0066] Specifically, the contract review device is provided with a feedback entrance. After entering the feedback entrance, the user can submit the problems and suggestions encountered during use in an online form, and use natural language processing technology to automatically classify user feedback, such as "model error", "user interface problem" and "function suggestion", etc. After automatic classification, the processing priority is determined according to the severity and frequency of the feedback, and after processing the feedback, the project management tool (such as JIRA) is used to record and track the processing progress of each feedback. After the problem is solved, the corresponding user of the feedback will be notified, and feedback reports will be generated regularly to analyze common problems and trends in user feedback, and formulate specific improvement plans based on the feedback reports, including technical optimization and function enhancement.

[0067] In particular, as banking operations and laws and regulations are constantly changing, regular updates can ensure that the preset contract review model always meets the latest requirements, and by regularly updating the model and data, the accuracy and performance of the system can be continuously improved.

[0068] The specific steps are as follows:

[0069] Data update:

[0070] New data: Regularly collect new contract samples and legal and regulatory documents to expand the training data set.

[0071] Data cleaning: Clean and label new data to ensure data quality and consistency.

[0072] Model Update:

[0073] Fine-tune the model: Use new data to fine-tune the existing model to improve the model's generalization ability and accuracy.

[0074] Model evaluation: Evaluate the performance of the updated model on the test set to ensure that it achieves the expected results.

[0075] Version control: Use a version control system (such as Git) to manage different versions of models for easy backtracking and comparison.

[0076] Legal and regulatory tracking:

[0077] Regular review: Regularly review the latest laws, regulations and industry standards to ensure that the system meets the latest requirements.

[0078] Expert Consulting: Work with experts in the legal and financial fields to get professional advice and recommendations.

[0079] User Training:

[0080] Training Materials: User manuals and training materials are regularly updated to reflect new features and improvements to the system.

[0081] Training courses: Organize online or offline training courses to help users become familiar with the latest version of the system.

[0082] System Testing:

[0083] Regression testing: Comprehensive regression testing is performed after each update to ensure system stability and compatibility.

[0084] Performance testing: Testing the system's response time and processing power to ensure it can still operate normally under high load.

[0085] In a feasible implementation, the target contract text is segmented by a preset contract review model, and deep semantic analysis is performed on the processed review segments to obtain the contract review results, and the following steps are included:

[0086] Based on the contract review result, a contract review report is generated, and the contract review report is sent to the target user, so that the target user can review the contract review report and provide feedback on the review opinion;

[0087] It should be noted that the contract review report lists in detail the key information, potential risk points and suggested modifications of the contract. The review opinions include the professionals' amendments or supplements to the contract review report. The contract review device will automatically generate a contract review report based on the analysis results of the preset contract review model. The report lists in detail the key information, potential risk points and suggested modifications of the contract, and submits the automatically generated review report to professionals for review. Professionals can revise or supplement the contract review report generated by the analysis results of the model, and feedback the revised or supplemented opinions to the contract review device.

[0088] Based on the review opinions, a target review report is generated.

[0089] It is understandable that the contract review equipment will form a final target review report based on the corrections or additions provided by professionals, and utilize the review of professionals to improve the accuracy and reliability of the review and reduce the possibility of human error.

[0090] In a feasible implementation manner, the step of preprocessing the target contract text to obtain processed contract fragments and fragment vectors corresponding to the contract fragments includes the following steps:

[0091] Based on a preset word count range, the target contract text is segmented to obtain initial contract segments;

[0092] It should be noted that the preset word count range is within 1,000 words per segment so that the semantic continuity between segments is not affected during segmentation. The contract review device will divide the contract text into multiple segments according to logical paragraphs or natural breakpoints, and the length of each segment is controlled within 1,000 words. When segmenting, care should be taken to avoid cutting off sentences or paragraphs to maintain the coherence of the context, and then obtain the initial contract segmentation.

[0093] Extracting titles and keywords from the initial contract fragments to obtain contract fragments including keywords and titles;

[0094] It is understandable that the contract review device will use regular expressions to match common title formats. For example, the title is usually located at the beginning of the document or the beginning of a new paragraph, and the font may be larger or bold. It also identifies key terms and phrases in the contract text to help quickly locate key content. For example, keywords are extracted using rules. The frequency of occurrence of each word can be counted based on word frequency statistics, part-of-speech tagging and other methods, and words with higher frequency can be selected as candidate keywords.

[0095] Convert the contract shards into shard vectors.

[0096] It should be noted that the contract review device encodes each contract segment through a vector model, which is an efficient text embedding model that can capture the deep semantic information of the text, and then obtain the vector representation of each contract segment.

[0097] In this embodiment, the contract review device uses the deep thinking ability of the preset contract review model to perform deep semantic analysis and logical reasoning on banking contracts, thereby improving the accuracy and efficiency of contract review and providing a smarter and more efficient contract review solution for banking business.

[0098] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 Based on the preset review point, the full text is processed by the preset reading processing sub-model in the preset contract review model to obtain the second review segment. The contract review method also includes steps A100 to A200:

[0099] Step A100, inputting the contract segments into the preset contract review model, reading each of the contract segments paragraph by paragraph, extracting the fields related to the preset review points, and generating a target contract segment based on the extracted target fields;

[0100] It should be noted that the target contract segment is a new contract segment generated based on the excerpted fields. The preset contract review model can read each contract segment through multi-threaded concurrency. During the reading process, the large model is required to determine whether the current contract segment is related to the audit points. After the contract segment is input into the preset contract review model, the contract review device will use multi-threaded concurrency to read each contract segment through once, that is, full-text reading and review. During the reading process, the large model is required to determine whether the current contract segment is related to the audit points. If relevant, the relevant content will be summarized. If not, it will be ignored. Based on the content of the summary, a new target contract segment is generated.

[0101] Specifically, the contract review equipment is equipped with multi-core processor capabilities, which can analyze multiple contract fragments at the same time, significantly speeding up the processing speed, and making a preliminary judgment on each contract fragment through a preset model to determine whether the fragment is related to the audit points. This process can help exclude a large amount of irrelevant information, making subsequent analysis more efficient. For fragments that are considered to be related to the audit points, key information is extracted, such as specific legal terms and clause contents, etc., which can grasp the logical structure of the contract as a whole, ensure that no important details or clauses are omitted, and help discover the logical relationship between different clauses to ensure the consistency and integrity of the contract.

[0102] Step A200, splicing the target contract segments to obtain spliced ​​second review segments.

[0103] It is understandable that the second review segment is a segment obtained by screening the preset contract review model. The contract review device will merge the extracted new target contract segments to obtain the second review segment obtained by screening the preset contract review model.

[0104] Specifically, the contract review device aggregates all fragments marked as relevant to form a second review segment. Through automated tools and multi-threading technology, the contract review time is greatly shortened, especially when dealing with a large number of contracts.

[0105] In a feasible implementation manner, the step of performing recall processing on the slice vector to obtain the first review slice includes the following steps:

[0106] Establishing an index based on the text vector and the contract fragment corresponding to the text vector;

[0107] It should be noted that the contract review device constructs an index between the text vector and its corresponding text vector in Elasticsearch based on the vectorized text vector.

[0108] Preliminarily screening the contract shards through the preset review points and the index to obtain initial contract shards;

[0109] It is understandable that the contract review device will calculate the match between the preset review point and the text vector, and sort the match from high to low, and select a preset number of top-ranked text vectors, and quickly find them through Elasticsearch. The contract fragments corresponding to the selected text vectors are used as the preliminary screening results. Among them, Elasticsearch is an efficient full-text search engine used to store and query the fragmented contract text. ES supports complex query conditions and can quickly return contract fragments that are highly relevant to the audit requirements.

[0110] The initial contract segment is accurately located to obtain the excerpt field related to the preset review point, and a first review segment is generated based on the excerpt field.

[0111] It should be noted that the contract review device performs further analysis based on the contract segments obtained through preliminary screening, accurately locates the fields related to the preset review points, and generates a first review segment for further in-depth analysis based on the obtained fields.

[0112] Specifically, the contract review device uses pre-built indexes and pre-converted vector texts to convert audit requirements or keywords into vector form, and matches similar or related paragraphs in the contract by calculating the similarity between vectors. The screening solution adopted by the contract review device is the segmented recall method, which refers to the use of specific technical means to quickly locate text fragments related to specific requirements or audit points when processing large amounts of text data (such as contract review).

[0113] Furthermore, the contract review equipment uses both segmented retrieval recall review and full-text reading review. These two methods reduce irrelevant information and shorten the length of prompt words to improve the review capabilities of the large model.

[0114] In this embodiment, by searching the recall method and the large model summary, the interference information in the prompt words for the large model is reduced, and the effect of the model review is improved.

[0115] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the contract review method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0116] This application also provides a contract review device, please refer to Figure 4 , the contract review device comprises:

[0117] A response module 10, for obtaining a target contract text in response to a review request;

[0118] The review module 20 is used to segment the target contract text through a preset contract review model, and perform deep semantic analysis on the processed review segments to obtain the contract review results.

[0119] Optionally, the review module includes:

[0120] A screening submodule is used to pre-process the target contract text to obtain a processed contract segment and a segment vector corresponding to the contract segment, wherein the contract segment includes a title and keywords; recall the segment vector to obtain the first review segment; based on the preset review point, perform full-text processing through a preset reading processing sub-model in the preset contract review model to obtain the second review segment; input the first review segment and the second review segment into a preset contract review sub-model in the preset contract review model for deep semantic analysis to obtain a contract review result.

[0121] The feedback submodule is used to generate a contract review report based on the contract review result, and send the contract review report to the target user so that the target user can review the contract review report and provide feedback on the review opinion; based on the review opinion, a target review report is generated.

[0122] Optionally, the screening submodule includes:

[0123] A splicing unit is used to input the contract segments into the preset contract review model, read each of the contract segments paragraph by paragraph, extract the fields related to the preset review points, and generate a target contract segment based on the extracted target fields; and splice the target contract segments to obtain a spliced ​​second review segment.

[0124] A positioning unit is used to establish an index based on the text vector and the contract segment corresponding to the text vector; preliminarily screen the contract segment through a preset review point and the index to obtain an initial contract segment; accurately locate the initial contract segment to obtain an excerpt field related to the preset review point, and generate a first review segment based on the excerpt field.

[0125] The segmentation unit is used to segment the target contract text based on a preset word count range to obtain initial contract segments; extract titles and keywords from the initial contract segments to obtain contract segments including keywords and titles; and convert the contract segments into segment vectors.

[0126] The contract review device provided by the present application adopts the contract review method in the above embodiment to solve the technical problems of contract review. Compared with the prior art, the beneficial effects of the contract review device provided by the present application are the same as the beneficial effects of the contract review method provided by the above embodiment, and the other technical features in the contract review device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0127] The present application provides a contract review device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the contract review method in the above-mentioned embodiment one.

[0128] Reference below Figure 5, which shows a schematic diagram of the structure of a contract review device suitable for implementing the embodiment of the present application. The contract review device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, tablet computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The contract review device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0129] like Figure 5 As shown, the contract review device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the contract review device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the contract review device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a contract review device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0130] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0131] The contract review device provided by this application adopts the contract review method in the above embodiment to solve the technical problems of contract review. Compared with the prior art, the beneficial effects of the contract review device provided by this application are the same as the beneficial effects of the contract review method provided by the above embodiment, and the other technical features in the contract review device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0132] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0133] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0134] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the contract review method in the above-mentioned embodiment.

[0135] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0136] The above-mentioned computer-readable storage medium may be included in the contract review device; or it may exist independently without being assembled into the contract review device.

[0137] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the contract review device, the contract review device enables the contract review device to: obtain the target contract text in response to the review request; segment the target contract text through a preset contract review model, and perform deep semantic analysis on the processed review segments to obtain the contract review results.

[0138] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0140] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0141] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned contract review method, and can solve the technical problems of contract review. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the contract review method provided in the above-mentioned embodiment, and will not be elaborated here.

[0142] The present application also provides a computer program product, including a computer program, which implements the steps of the contract review method as described above when executed by a processor.

[0143] The computer program product provided by this application can solve the technical problems of contract review. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the contract review method provided by the above embodiment, which will not be repeated here.

[0144] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A contract review method, characterized in that: The contract review method includes: Responding to the review request, obtaining the target contract text; The target contract text is segmented through a preset contract review model, and deep semantic analysis is performed on the processed review segments to obtain the contract review results.

2. The contract review method according to claim 1, characterized in that: The review segments include a first review segment and a second review segment. The steps of segmenting the target contract text using a preset contract review model, and performing deep semantic analysis on the processed review segments to obtain the contract review result include: Preprocessing the target contract text to obtain processed contract segments and segment vectors corresponding to the contract segments, wherein the contract segments include titles and keywords; Based on a preset review point, the segment vector is recalled by a preset recall processing sub-model in a preset contract review model to obtain the first review segment; Based on the preset review point, full text processing is performed through a preset reading processing sub-model in the preset contract review model to obtain the second review segment; The first review segment and the second review segment are input into the preset contract review sub-model in the preset contract review model for deep semantic analysis to obtain the contract review result.

3. The contract review method according to claim 2, characterized in that: The step of performing full text processing based on the preset review point through the preset reading processing sub-model in the preset contract review model to obtain the second review segment includes: Inputting the contract segments into the preset contract review model, reading each of the contract segments paragraph by paragraph, extracting the fields related to the preset review points, and generating target contract segments based on the extracted target fields; The target contract segments are spliced ​​together to obtain the spliced ​​second review segments.

4. The contract review method according to claim 2, characterized in that: The step of recalling the slice vector to obtain the first review slice includes: Establishing an index based on the text vector and the contract fragment corresponding to the text vector; Preliminarily screening the contract shards through the preset review points and the index to obtain initial contract shards; The initial contract segment is accurately located to obtain the excerpt field related to the preset review point, and a first review segment is generated based on the excerpt field.

5. The contract review method according to claim 1, characterized in that: The step of segmenting the target contract text by using a preset contract review model, and performing deep semantic analysis on the processed review segments to obtain the contract review results includes: Based on the contract review result, a contract review report is generated, and the contract review report is sent to the target user, so that the target user can review the contract review report and provide feedback on the review opinion; Based on the review opinions, a target review report is generated.

6. The contract review method according to claim 1, characterized in that: The step of preprocessing the target contract text to obtain processed contract fragments and fragment vectors corresponding to the contract fragments includes: Based on a preset word count range, the target contract text is segmented to obtain initial contract segments; Extracting titles and keywords from the initial contract fragments to obtain contract fragments including keywords and titles; Convert the contract shards into shard vectors.

7. A contract review device, characterized in that: The device comprises: A response module, used for responding to the review request and obtaining the target contract text; The review module is used to segment the target contract text through a preset contract review model, and perform deep semantic analysis on the processed review segments to obtain the contract review results.

8. A contract review device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the contract review method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the contract review method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the contract review method according to any one of claims 1 to 6 are implemented.

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