Text analysis method and device, equipment, storage medium and computer program product

Abstract

The invention discloses a text analysis method and device, equipment, a storage medium and a computer program product, and relates to the technical field of large models, and the text analysis method comprises the steps that in response to a text analysis instruction, keyword extraction is conducted on a target text through a large language model, and text keywords are obtained; searching at least one associated text related to the target text based on the text keyword; and performing conjoint analysis on the target text, the associated text and a local text library through a large language model to obtain an analysis result of the target text. Through the mode, keyword extraction is performed in the target text based on the text analysis instruction extracted by the user, then comprehensive analysis is performed based on the retrieved associated text and the local text library, and the comprehensiveness of paper text analysis is improved.

Description

Technical Field

[0001] The present application relates to the field of large model technology, and in particular to text analysis methods, devices, equipment, storage media and computer program products. Background Art

[0002] With the continuous development of intelligent AI, it is now possible to conduct easy-to-understand analysis of papers with more profound and difficult-to-understand knowledge, thereby facilitating users to acquire knowledge more simply and quickly. However, current paper analysis can only perform content analysis on the paper itself, and cannot conduct a more comprehensive analysis of the research content and research prospects of the paper.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a text analysis method, device, equipment, storage medium and computer program product, aiming to solve the technical problem that existing paper analysis is often only based on the paper body to generate an abstract for analysis.

[0005] To achieve the above purpose, the present application proposes a text analysis method, which includes:

[0006] In response to the text analysis instruction, extract keywords from the target text using a large language model to obtain text keywords;

[0007] Searching for at least one associated text related to the target text based on the text keyword;

[0008] The target text, the associated text and the local text library are jointly analyzed through a large language model to obtain an analysis result of the target text.

[0009] Optionally, in response to the text analysis instruction, extracting keywords from the target text by using a large language model to obtain text keywords comprises:

[0010] When receiving a text analysis instruction, determining a target text according to the text analysis instruction;

[0011] The target text is imported into the large language model to extract keywords to obtain text keywords.

[0012] Optionally, the step of importing the target text into the large language model to extract keywords to obtain text keywords includes:

[0013] Importing the target text into the large language model to obtain text keywords corresponding to the target text;

[0014] The text keywords of the target text are determined according to the text keywords.

[0015] Optionally, the step of searching for at least one associated text related to the target text based on the text keyword includes:

[0016] Searching a target database according to the text keyword to obtain at least one preliminary screening text associated with the text keyword;

[0017] At least one associated text related to the target text is selected from the primary screening texts.

[0018] Optionally, the step of selecting at least one associated text related to the target text from the primary screening text includes:

[0019] Obtaining text subject information of each preliminary screening text;

[0020] Select related texts from each initially screened text based on the text subject information.

[0021] Optionally, the step of selecting related texts from each pre-screened text according to the text subject information and the research direction information includes:

[0022] Acquiring analysis topic information and analysis viewpoint information of the target text;

[0023] Determine the same viewpoint text and the opposite viewpoint text in the preliminary screening text according to the text topic information, analysis topic information and analysis viewpoint information;

[0024] The associated texts are determined according to the same viewpoint texts and the opposite viewpoint texts.

[0025] Optionally, the step of jointly analyzing the target text, the associated text and the local text library by using a large language model to obtain an analysis result of the target text includes:

[0026] Compare the target text with the local text library through a large language model to obtain a local duplicate checking result;

[0027] Analyze the target text and the associated text by using the large language model to obtain a content analysis result;

[0028] The analysis result of the target text is determined according to the local duplicate checking result and the content analysis result.

[0029] Optionally, the step of comparing the target text with the local text library by using a large language model to obtain a local duplicate checking result includes:

[0030] Determine the duplicate checking keywords of the target text through a large language model;

[0031] Search the local text library according to the duplicate checking keyword to obtain duplicate text content;

[0032] The local duplicate checking result is determined based on the repeated text content.

[0033] Optionally, the step of analyzing the target text and the associated text by using the large language model to obtain a content analysis result includes:

[0034] Determining the target research content of the target text and the associated research content of the associated text by using the large language model;

[0035] Compare the target research content with the related research content to determine the research advancement information and direction duplication information of the target text;

[0036] The content analysis results are determined based on the research advancement information and the direction repeatability information.

[0037] Optionally, the step of comparing the target research content with the related research content to determine the research advancement information and direction repetitive information of the target text includes:

[0038] Comparing the target research content with the related research content through the large language model to determine the professional knowledge difference information between the research content of the target text and the research content of the related text;

[0039] Determine the research advancement information and direction repetitive information of the target text according to the professional knowledge difference information

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a text analysis device, which includes:

[0041] A keyword extraction module, for extracting keywords from a target text through a large language model in response to a text analysis instruction, to obtain text keywords;

[0042] An associated query module, used for searching at least one associated text related to the target text based on the text keyword;

[0043] The joint analysis module is used to perform a joint analysis on the target text, the associated text and the local text library through a large language model to obtain an analysis result of the target text.

[0044] Optionally, the keyword extraction module is further used to determine the target text according to the text analysis instruction when receiving the text analysis instruction; import the target text into the large language model to extract keywords to obtain text keywords.

[0045] Optionally, the keyword extraction module is further used to import the target text into the large language model to obtain text keywords corresponding to the target text; and determine text keywords of the target text according to the text keywords.

[0046] Optionally, the associated query module is further used to search the target database according to the text keyword to obtain at least one preliminary screening text associated with the text keyword; and select at least one associated text related to the target text from the preliminary screening text.

[0047] Optionally, the associated query module is further used to obtain text subject information of each initially screened text; and select associated texts from each initially screened text according to the text subject information.

[0048] Optionally, the associated query module is also used to obtain analysis topic information and analysis viewpoint information of the target text; determine the same viewpoint text and opposite viewpoint text in the preliminary screening text based on the text topic information, analysis topic information and analysis viewpoint information; and determine associated text based on the same viewpoint text and opposite viewpoint text.

[0049] Optionally, the joint analysis module is also used to compare the target text with the local text library through a large language model to obtain a local duplicate checking result; analyze the target text and the associated text through the large language model to obtain a content analysis result; and determine the analysis result of the target text based on the local duplicate checking result and the content analysis result.

[0050] In addition, to achieve the above-mentioned purpose, the present application also proposes a text analysis device, which includes: 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 text analysis method described above.

[0051] 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 a processor, the steps of the text analysis method described above are implemented.

[0052] 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, the steps of the text analysis method described above are implemented.

[0053] One or more technical solutions proposed in this application have at least the following technical effects:

[0054] This application extracts keywords from the target text through a large language model in response to a text analysis instruction to obtain text keywords; searches for at least one associated text related to the target text based on the text keywords; and performs a joint analysis of the target text, the associated text, and the local text library through a large language model to obtain the analysis result of the target text. In this way, keyword extraction is achieved in the target text based on the text analysis instruction raised by the user, and then a comprehensive analysis is performed based on the retrieved associated text and the local text library, thereby improving the comprehensiveness of the paper text analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] 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.

[0056] 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.

[0057] Figure 1 A flowchart of the first embodiment of the text analysis method of the present application is provided;

[0058] Figure 2 A flowchart of the second embodiment of the text analysis method of the present application is provided;

[0059] Figure 3 This is a schematic diagram of the module structure of the text analysis device according to an embodiment of the present application;

[0060] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the text analysis method in the embodiment of the present application.

[0061] 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

[0062] 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.

[0063] 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.

[0064] The main solution of the embodiment of the present application is: in response to a text analysis instruction, a large language model is used to extract keywords from a target text to obtain text keywords; based on the text keywords, at least one associated text related to the target text is searched; and a large language model is used to jointly analyze the target text, the associated text, and a local text library to obtain an analysis result of the target text.

[0065] In this embodiment, for the convenience of description, the following description is made by taking the identification computer as the execution subject.

[0066] As existing technologies continue to develop with intelligent AI, it is now possible to perform easy-to-understand analysis on papers that are more profound and difficult to understand, making it easier and faster for users to acquire knowledge. However, current paper analysis can only perform content analysis on the paper itself, and cannot perform a more comprehensive analysis of the research content and research prospects of the paper.

[0067] The present application provides a solution, through which keyword extraction is implemented in the target text based on the text analysis instructions raised by the user, and then a comprehensive analysis is performed based on the retrieved related texts and the local text library, thereby improving the comprehensiveness of the paper text analysis.

[0068] 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, a server, etc. that can realize the above functions. The following takes a computer device as an example to illustrate this embodiment and the following embodiments.

[0069] Based on this, the present application embodiment provides a text analysis method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the text analysis method of the present application.

[0070] In this embodiment, the text analysis method includes steps S10 to S30:

[0071] Step S10, in response to the text analysis instruction, extracting keywords from the target text through a large language model to obtain text keywords;

[0072] It should be noted that when a text analysis instruction is received, the target text specified by the text analysis instruction is firstly extracted using a large language model, so that the keywords corresponding to the target text can be obtained for subsequent retrieval of associated texts and analysis.

[0073] In a feasible implementation, in order to accurately obtain text keywords, step S10 includes: when receiving a text analysis instruction, determining a target text according to the text analysis instruction; importing the target text into the large language model to extract keywords to obtain text keywords.

[0074] It should be understood that when a text analysis instruction is received, the target text is determined through the text analysis instruction. The target text may be the text uploaded by the user following the instruction, or the text under a URL specified by the user, or the text of other specified paths. This embodiment does not limit this.

[0075] In a specific implementation, after determining the target text, the target text is imported into a large language model, so as to extract keywords related to or corresponding to the subject of the target text through the large language model.

[0076] In a feasible implementation, in order to accurately determine text keywords, the target text is imported into the large language model for keyword extraction, and the step of obtaining text keywords includes: importing the target text into the large language model to obtain text keywords corresponding to the target text; determining the text keywords of the target text based on the text keywords.

[0077] It should be noted that the target text is first imported into the large language model, and then the target text can be semantically analyzed by the large language model to obtain multiple text keywords, and then each text keyword is screened, and the text keywords with lower criticality rankings are eliminated to obtain at least one text keyword.

[0078] Step S20, searching for at least one associated text related to the target text based on the text keyword;

[0079] It should be understood that after obtaining the text keywords, a network search is performed based on the text keywords, so as to obtain at least one associated text related to the target text for subsequent analysis of research directions, research contents and other dimensions.

[0080] In a feasible implementation, in order to accurately retrieve related texts, step S20 includes: searching the target database according to the text keywords to obtain at least one preliminary screening text associated with the text keywords; selecting at least one related text related to the target text from the preliminary screening texts.

[0081] In the specific implementation, the target database is first searched according to the text keywords, and the searched results are used as the primary screening text. The target database is a pre-set network database, which may include any paper database, scientific journal database, etc.

[0082] It should be noted that after obtaining the initial screening text, the initial screening text is screened again to obtain the associated text.

[0083] In a feasible implementation, in order to obtain related texts that are more helpful for analyzing the target text, the step of selecting at least one related text related to the target text from the preliminary screening texts includes: obtaining text subject information of each preliminary screening text; and selecting related texts from each preliminary screening text based on the text subject information.

[0084] It should be understood that the text subject information of each preliminary screening text is obtained first. The text subject information refers to the paper theme of each preliminary screening text, which can be summarized and extracted from the paper abstract.

[0085] In a specific implementation, after obtaining the text subject information, related texts with a more auxiliary analysis function are screened out from all the preliminarily screened texts based on the text subject information.

[0086] In a feasible implementation manner, in order to ultimately select related texts with greater reference value, the step of selecting related texts from each preliminary screening text based on the text subject information and the research direction information includes: obtaining the analysis subject information and analysis viewpoint information of the target text; determining the same viewpoint text and opposite viewpoint text in the preliminary screening text based on the text subject information, analysis subject information and analysis viewpoint information; and determining related texts based on the same viewpoint text and opposite viewpoint text.

[0087] It should be noted that first, the analysis topic information and analysis viewpoint information of the target text are obtained, specifically, the topic-related information obtained after semantic analysis and understanding of the target text, as well as the viewpoints therein.

[0088] It should be understood that the text topic information is then combined with the analysis topic information and the analysis viewpoint information for comparative analysis, so as to determine the text with the same viewpoint as the target text and the text with the opposite viewpoint in the preliminary screening text, so that the paper analysis can be carried out more dialectically.

[0089] Step S30: jointly analyzing the target text, the associated text and the local text library by using a large language model to obtain an analysis result of the target text.

[0090] In the specific implementation, after obtaining the associated text, the target text is checked for duplicates locally, and content analysis is performed in combination with the associated text, thereby performing a joint analysis from both the aspects of duplicate checking and content.

[0091] This embodiment provides a text analysis method, which extracts keywords from a target text through a large language model in response to a text analysis instruction to obtain text keywords; searches for at least one associated text related to the target text based on the text keywords; and performs a joint analysis of the target text, the associated text, and a local text library through a large language model to obtain an analysis result of the target text. In this way, keyword extraction is achieved in the target text based on the text analysis instruction raised by the user, and then a comprehensive analysis is performed based on the retrieved associated text and the local text library, thereby improving the comprehensiveness of the paper text analysis.

[0092] 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 2 , step S30 includes steps S301 to S303:

[0093] Step S301, comparing the target text with the local text library through a large language model to obtain a local duplicate checking result;

[0094] It should be noted that the target text is first compared with the local text library through a large language model, that is, a local duplicate check is performed, so that the local duplicate check result of the target text can be obtained.

[0095] In a feasible implementation, in order to accurately obtain local duplicate checking results, step S301 includes: determining the duplicate checking keywords of the target text through a large language model; searching the local text library according to the duplicate checking keywords to obtain repeated text content; and determining the local duplicate checking results based on the repeated text content.

[0096] It should be understood that the duplicate checking keywords of the target text are first determined through a large language model, that is, a keyword combination that can represent the main idea and conclusion of the target text. The duplicate checking keywords are a combination of multiple words.

[0097] In the specific implementation, after obtaining the duplicate check keywords, the local text library is searched through the duplicate check keywords to obtain the duplicate text content, that is, the duplication of the text associated with the duplicate check keywords in the local text library. Finally, the local duplicate check result is determined based on the duplicate text content, that is, the degree of duplication of the target text compared with the already published technologies and papers.

[0098] Step S302, analyzing the target text and the associated text by using the large language model to obtain a content analysis result;

[0099] It should be noted that by importing the target text and related text into the large language model for analysis and processing at the same time, the results of content analysis between the target text and published technologies and papers can be obtained, including but not limited to the repetitiveness, advancement and future development of the research content.

[0100] In a feasible implementation, in order to accurately obtain content analysis results, step S302 includes: determining the target research content of the target text and the related research content of the related text through the large language model; comparing the target research content with the related research content to determine the research advancement information and direction repetitiveness information of the target text; determining the content analysis results based on the research advancement information and the direction repetitiveness information.

[0101] It should be understood that the target text is first analyzed through a large language model to obtain the target research content of the target text, and then the research content of the related text is analyzed. After obtaining the target research content and the related research content, the two are compared to determine whether the depth of the target text in research exceeds that of each related text, obtain research advancement information, and determine whether the research direction of the target text is largely repeated in existing research and development results.

[0102] In the specific implementation, after determining the research advancement information and direction repetitive information, the content analysis results of the target text are summarized.

[0103] In a feasible implementation, in order to accurately obtain research advancement information and directional repeatability information, the target research content is compared with the related research content, and the steps of determining the research advancement information and directional repeatability information of the target text include: comparing the target research content with the related research content through the large language model to determine the professional knowledge difference information between the research content of the target text and the research content of the related text; determining the research advancement information and directional repeatability information of the target text based on the professional knowledge difference information.

[0104] It should be noted that the target research content is first compared with the related research content through the large language model to determine the professional knowledge difference information, that is, the difference in the analysis of technology or the discussion of knowledge between the two. After obtaining the professional knowledge difference information, the large language model is used to analyze and obtain the research advancement information and direction repeatability information.

[0105] Step S303, determining the analysis result of the target text according to the local duplicate checking result and the content analysis result.

[0106] It should be understood that after obtaining the local duplicate check results and content analysis results, the two are combined for joint analysis to obtain the analysis results of the target text. In addition to issuing an analysis result report, submission suggestions can also be made based on the analysis of the target text, and submission suggestions to journals and magazines can be made based on the advanced nature of the target text.

[0107] This embodiment compares the target text with the local text library through a large language model to obtain a local duplicate check result; analyzes the target text and the associated text through the large language model to obtain a content analysis result; and determines the analysis result of the target text based on the local duplicate check result and the content analysis result. In this way, text localization processing is achieved. Because the browser can render locally, local tools can be used to directly analyze the paper to achieve local rendering, which can respond faster. At the same time, combined with text content analysis, the comprehensiveness of paper analysis is improved.

[0108] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the text analysis method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0109] This application also provides a text analysis device, please refer to Figure 3 , the text analysis device comprises:

[0110] The keyword extraction module 10 is used to extract keywords from the target text through a large language model in response to the text analysis instruction to obtain text keywords.

[0111] The associated query module 20 is used to search for at least one associated text related to the target text based on the text keyword.

[0112] The joint analysis module 30 is used to perform a joint analysis on the target text, the associated text and the local text library through a large language model to obtain an analysis result of the target text.

[0113] In this embodiment, in response to a text analysis instruction, a large language model is used to extract keywords from the target text to obtain text keywords; at least one associated text related to the target text is searched based on the text keywords; and the target text, the associated text and the local text library are jointly analyzed by the large language model to obtain the analysis result of the target text. In this way, keyword extraction is implemented in the target text based on the text analysis instruction raised by the user, and then a comprehensive analysis is performed based on the retrieved associated text and the local text library, thereby improving the comprehensiveness of the paper text analysis.

[0114] In one embodiment, the keyword extraction module 10 is further used to determine the target text according to the text analysis instruction when receiving the text analysis instruction; import the target text into the large language model to extract keywords to obtain text keywords.

[0115] In one embodiment, the keyword extraction module 10 is further used to import the target text into the large language model to obtain text keywords corresponding to the target text; and determine text keywords of the target text according to the text keywords.

[0116] In one embodiment, the keyword extraction module 10 is further used to search the target database according to the text keyword to obtain at least one preliminary screening text associated with the text keyword; and select at least one associated text related to the target text from the preliminary screening text.

[0117] In one embodiment, the associated query module 20 is further used to obtain text subject information of each initially screened text; and select associated texts from each initially screened text according to the text subject information.

[0118] In one embodiment, the associated query module 20 is also used to obtain analysis topic information and analysis viewpoint information of the target text; determine the same viewpoint text and opposite viewpoint text in the preliminary screening text based on the text topic information, analysis topic information and analysis viewpoint information; and determine associated text based on the same viewpoint text and opposite viewpoint text.

[0119] In one embodiment, the joint analysis module 30 is further used to compare the target text with the local text library through a large language model to obtain a local duplicate checking result; analyze the target text and the associated text through the large language model to obtain a content analysis result; and determine the analysis result of the target text based on the local duplicate checking result and the content analysis result.

[0120] In one embodiment, the joint analysis module 30 is also used to determine the duplicate checking keywords of the target text through a large language model; search the local text library according to the duplicate checking keywords to obtain repeated text content; and determine the local duplicate checking results according to the repeated text content.

[0121] In one embodiment, the joint analysis module 30 is also used to determine the target research content of the target text and the related research content of the related text through the large language model; compare the target research content with the related research content to determine the research advancement information and directional repetitive information of the target text; and determine the content analysis results based on the research advancement information and the directional repetitive information.

[0122] In one embodiment, the joint analysis module 30 is also used to compare the target research content with the related research content through the large language model to determine the professional knowledge difference information between the research content of the target text and the research content of the related text; and determine the research advancement information and direction repeatability information of the target text based on the professional knowledge difference information.

[0123] The text analysis device provided by the present application adopts the text analysis method in the above embodiment, which can solve the technical problem that the existing paper analysis is often only based on the paper body to generate the abstract for analysis. Compared with the prior art, the beneficial effects of the text analysis device provided by the present application are the same as the beneficial effects of the text analysis method provided by the above embodiment, and the other technical features in the text analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0124] The present application provides a text analysis 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 text analysis method in the above-mentioned embodiment 1.

[0125] Reference below Figure 4 , which shows a schematic diagram of the structure of a text analysis device suitable for implementing the embodiment of the present application. The text analysis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), 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 4 The text analysis device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0126] like Figure 4As shown, the text analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, 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 text analysis 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 text analysis device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a text analysis 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 have alternatively.

[0127] 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.

[0128] The text analysis device provided by the present application adopts the text analysis method in the above embodiment, which can solve the technical problem that the existing paper analysis is often only based on the paper body to generate the summary for analysis. Compared with the prior art, the beneficial effects of the text analysis device provided by the present application are the same as the beneficial effects of the text analysis method provided by the above embodiment, and the other technical features in the text analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0129] 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.

[0130] 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.

[0131] 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 text analysis method in the above-mentioned embodiment.

[0132] 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.

[0133] The computer-readable storage medium may be included in the text analysis device; or may exist independently without being assembled into the text analysis device.

[0134] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by a text analysis device, the text analysis device: responds to a text analysis instruction, extracts keywords from a target text through a large language model, and obtains text keywords; searches for at least one associated text related to the target text based on the text keywords; and jointly analyzes the target text, the associated text, and a local text library through a large language model to obtain an analysis result of the target text.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned text analysis method, and can solve the technical problem that the existing paper analysis is often only based on the paper body to generate an abstract for analysis. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the text analysis method provided by the above-mentioned embodiment, and will not be repeated here.

[0139] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned text analysis method when executed by a processor.

[0140] The computer program product provided by this application can solve the technical problem that the existing paper analysis is often only based on the paper body to generate the abstract for analysis. 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 text analysis method provided by the above embodiment, which will not be repeated here.

[0141] 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.

[0142] The present invention discloses A1. a text analysis method, the method comprising:

[0143] In response to the text analysis instruction, extract keywords from the target text using a large language model to obtain text keywords;

[0144] Searching for at least one associated text related to the target text based on the text keyword;

[0145] The target text, the associated text and the local text library are jointly analyzed through a large language model to obtain an analysis result of the target text.

[0146] A2. As described in the method of A1, in response to the text analysis instruction, the step of extracting keywords from the target text through the large language model to obtain text keywords includes:

[0147] When receiving a text analysis instruction, determining a target text according to the text analysis instruction;

[0148] The target text is imported into the large language model to extract keywords to obtain text keywords.

[0149] A3. As described in the method of A2, the step of importing the target text into the large language model for keyword extraction to obtain text keywords includes:

[0150] Importing the target text into the large language model to obtain text keywords corresponding to the target text;

[0151] The text keywords of the target text are determined according to the text keywords.

[0152] A4. As described in the method of A1, the step of searching for at least one associated text related to the target text based on the text keyword comprises:

[0153] Searching a target database according to the text keyword to obtain at least one preliminary screening text associated with the text keyword;

[0154] At least one associated text related to the target text is selected from the primary screening texts.

[0155] A5. As described in the method of A4, the step of selecting at least one associated text related to the target text from the primary screening text comprises:

[0156] Obtaining text subject information of each preliminary screening text;

[0157] Select related texts from each initially screened text based on the text subject information.

[0158] A6. As described in the method of A5, the step of selecting related texts from each preliminary screening text according to the text subject information and the research direction information comprises:

[0159] Acquiring analysis topic information and analysis viewpoint information of the target text;

[0160] Determine the same viewpoint text and the opposite viewpoint text in the preliminary screening text according to the text topic information, analysis topic information and analysis viewpoint information;

[0161] The associated texts are determined according to the same viewpoint texts and the opposite viewpoint texts.

[0162] A7. As described in the method of A1, the step of jointly analyzing the target text, the associated text and the local text library by a large language model to obtain the analysis result of the target text comprises:

[0163] Compare the target text with the local text library through a large language model to obtain a local duplicate checking result;

[0164] Analyze the target text and the associated text by using the large language model to obtain a content analysis result;

[0165] The analysis result of the target text is determined according to the local duplicate checking result and the content analysis result.

[0166] A8. As described in the method of A7, the step of comparing the target text with the local text library through a large language model to obtain a local duplicate checking result includes:

[0167] Determine the duplicate checking keywords of the target text through a large language model;

[0168] Search the local text library according to the duplicate checking keyword to obtain duplicate text content;

[0169] The local duplicate checking result is determined based on the repeated text content.

[0170] A9. As described in the method of A7, the step of analyzing the target text and the associated text by the large language model to obtain a content analysis result comprises:

[0171] Determining the target research content of the target text and the associated research content of the associated text by using the large language model;

[0172] Compare the target research content with the related research content to determine the research advancement information and direction duplication information of the target text;

[0173] The content analysis results are determined based on the research advancement information and the direction repeatability information.

[0174] A10. As described in the method of A9, the step of comparing the target research content with the related research content to determine the research advancement information and direction repetitive information of the target text includes:

[0175] Comparing the target research content with the related research content through the large language model to determine the professional knowledge difference information between the research content of the target text and the research content of the related text;

[0176] The research advancement information and direction repeatability information of the target text are determined based on the professional knowledge difference information.

[0177] The present invention also discloses B11. a text analysis device, the device comprising:

[0178] A keyword extraction module, for extracting keywords from a target text through a large language model in response to a text analysis instruction, to obtain text keywords;

[0179] An associated query module, used for searching at least one associated text related to the target text based on the text keyword;

[0180] The joint analysis module is used to perform a joint analysis on the target text, the associated text and the local text library through a large language model to obtain an analysis result of the target text.

[0181] B12. In the device as described in B11, the keyword extraction module is also used to determine the target text according to the text analysis instruction when receiving the text analysis instruction; import the target text into the large language model to extract keywords to obtain text keywords.

[0182] B13. In the device as described in B12, the keyword extraction module is also used to import the target text into the large language model to obtain text keywords corresponding to the target text; and determine the text keywords of the target text based on the text keywords.

[0183] B14. In the device as described in B11, the associated query module is also used to search the target database according to the text keyword to obtain at least one preliminary screening text associated with the text keyword; and select at least one associated text related to the target text from the preliminary screening text.

[0184] B15. In the device as described in B14, the associated query module is also used to obtain text subject information of each preliminary screening text; and select associated texts from each preliminary screening text according to the text subject information.

[0185] B16. In the device as described in B15, the associated query module is also used to obtain the analysis topic information and analysis viewpoint information of the target text; determine the same viewpoint text and opposite viewpoint text in the preliminary screening text according to the text topic information, analysis topic information and analysis viewpoint information; and determine the associated text according to the same viewpoint text and opposite viewpoint text.

[0186] B17. In the device as described in B11, the joint analysis module is further used to compare the target text with the local text library through a large language model to obtain a local duplicate checking result; analyze the target text and the associated text through the large language model to obtain a content analysis result; and determine the analysis result of the target text based on the local duplicate checking result and the content analysis result.

[0187] The present invention also discloses C18. A text analysis device, comprising: 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 text analysis method described above.

[0188] The present invention also discloses D19. a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the text analysis method as described above are implemented.

[0189] The present invention also discloses E20. a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the text analysis method described above are implemented.

Claims

1. A text analysis method, characterized in that: The method includes: In response to the text analysis instruction, extract keywords from the target text using a large language model to obtain text keywords; Searching for at least one associated text related to the target text based on the text keyword; The target text, the associated text and the local text library are jointly analyzed through a large language model to obtain an analysis result of the target text.

2. The method according to claim 1, characterized in that The step of extracting keywords from the target text by using a large language model in response to the text analysis instruction to obtain text keywords includes: When receiving a text analysis instruction, determining a target text according to the text analysis instruction; The target text is imported into the large language model to extract keywords to obtain text keywords.

3. The method according to claim 2, characterized in that The step of importing the target text into the large language model to extract keywords and obtain text keywords includes: Importing the target text into the large language model to obtain text keywords corresponding to the target text; The text keywords of the target text are determined according to the text keywords.

4. The method according to claim 1, characterized in that The step of searching for at least one associated text related to the target text based on the text keyword comprises: Searching a target database according to the text keyword to obtain at least one preliminary screening text associated with the text keyword; At least one associated text related to the target text is selected from the primary screening texts.

5. The method according to claim 4, characterized in that The step of selecting at least one associated text related to the target text from the primary screening text comprises: Obtaining text subject information of each preliminary screening text; Select related texts from each initially screened text based on the text subject information.

6. The method according to claim 5, characterized in that The step of selecting related texts from each pre-screened text according to the text subject information and the research direction information comprises: Acquiring analysis topic information and analysis viewpoint information of the target text; Determine the same viewpoint text and the opposite viewpoint text in the preliminary screening text according to the text topic information, analysis topic information and analysis viewpoint information; The associated texts are determined according to the same viewpoint texts and the opposite viewpoint texts.

7. A text analysis device, characterized in that: The device comprises: A keyword extraction module, for extracting keywords from a target text through a large language model in response to a text analysis instruction, to obtain text keywords; An associated query module, used for searching at least one associated text related to the target text based on the text keyword; The joint analysis module is used to perform a joint analysis on the target text, the associated text and the local text library through a large language model to obtain an analysis result of the target text.

8. A text analysis 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 text analysis 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, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the text analysis 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 text analysis method according to any one of claims 1 to 6 are implemented.

Images