CN111859885A - Automatic generation method and system for legal decision book - Google Patents
Automatic generation method and system for legal decision book Download PDFInfo
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- CN111859885A CN111859885A CN202010571946.9A CN202010571946A CN111859885A CN 111859885 A CN111859885 A CN 111859885A CN 202010571946 A CN202010571946 A CN 202010571946A CN 111859885 A CN111859885 A CN 111859885A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/166—Editing, e.g. inserting or deleting
- G06F40/186—Templates
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/38—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services; Handling legal documents
Abstract
The invention discloses a legal decision book generation method and a system. The legal decision making method comprises the following steps: the method comprises the steps of obtaining a legal document uploaded by a user, extracting a text of the legal document when the legal document is a picture document, and storing the obtained text information; matching the text segment information in the text information with pre-stored category information to obtain a matching category, and storing the text segment information according to the matching category in a classified manner; and responding to a legal decision book generation request initiated by the user, writing the target information inquired by the user into a legal decision book template, and generating a legal decision book. The invention can extract information from the picture file and automatically generate a legal decision book.
Description
Technical Field
The invention relates to the technical field of legal document generation, in particular to a method and a system for automatically generating a legal decision document.
Background
With the rapid development of artificial intelligence, artificial intelligence is applied more and more in the legal field, such as legal knowledge mapping, legal dialogue system construction, criminal scene recognition and legal picture recognition, but the automatic generation of legal decisions is still a difficult point.
Traditional legal decision making processes rely primarily on manual work to collect case material, archive case material, and generate legal decisions from relevant material. In order to accelerate the generation process of legal decision, various related information extraction technologies have been proposed, such as a judicial decision scenario information structured processing system, a method for automatically generating a court view according to the description of a crime fact, and the like. However, the actual case materials include not only electronic documents, but also handwritten documents, picture documents, and the like, and the existing applications related to legal decisions are focused on simple retrieval of the decisions and extraction of partial data in the decisions, and the complexity of extracting information from manual materials such as pictures and the like and synthesizing information under real conditions is not considered, so that the key contents forming the information of the decision data cannot be extracted comprehensively and accurately, and automatic generation of the legal decisions is not realized.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for automatically generating legal judgment books, which can fuse effective information in a picture file and automatically generate the legal judgment books.
In order to solve the above technical problem, in a first aspect, an embodiment of the present invention provides a legal decision making method, including:
The method comprises the steps of obtaining a legal document uploaded by a user, extracting a text of the legal document when the legal document is a picture document, and storing the obtained text information;
matching the text segment information in the text information with pre-stored category information to obtain a matching category, and storing the text segment information according to the matching category in a classified manner;
and responding to a legal decision book generation request initiated by the user, writing target information inquired by the user from all the text information into a legal decision book template, and generating a legal decision book.
Further, when the legal document is a picture document, performing text extraction on the legal document, and storing the obtained text information, specifically:
and according to an optical character recognition technology, text extraction is carried out on the legal document to obtain the text information, and the text information is stored in a database in a text format.
Further, the matching of the text segment information in the text information with pre-stored category information to obtain matching categories, and storing the text segment information according to the matching categories in a classified manner specifically include:
according to a pre-stored regular expression, segment extraction is carried out on the text information to obtain the segment information;
Matching the segment information with the pre-stored category information according to a preset matching algorithm to obtain the matching category;
and storing the text segment information in a database in a text format according to the matching category.
Further, the generating a legal decision by responding to the legal decision making request initiated by the user, writing the target information queried by the user from all the text information into a legal decision template, and generating a legal decision, further includes:
and generating the legal decision template through a template engine.
Further, the generating a legal decision by responding to the legal decision making request initiated by the user, writing the target information queried by the user from all the text information into a legal decision template, and generating a legal decision, further includes:
responding to a target information query request initiated by the user, and sending the text segment information corresponding to the matching category input by the user to the user as the target information.
In a second aspect, an embodiment of the present invention provides a legal decision making system, including:
the text information extraction module is used for acquiring legal documents uploaded by a user, extracting texts of the legal documents when the legal documents are picture documents, and storing the obtained text information;
The text segment information classification module is used for matching the text segment information in the text information with pre-stored category information to obtain a matching category, and storing the text segment information according to the matching category;
and the legal decision making module is used for responding to a legal decision making request initiated by the user, writing target information inquired by the user from all the text information into a legal decision making template, and generating a legal decision making.
Further, when the legal document is a picture document, performing text extraction on the legal document, and storing the obtained text information, specifically:
and according to an optical character recognition technology, text extraction is carried out on the legal document to obtain the text information, and the text information is stored in a database in a text format.
Further, the matching of the text segment information in the text information with pre-stored category information to obtain matching categories, and storing the text segment information according to the matching categories in a classified manner specifically include:
according to a pre-stored regular expression, segment extraction is carried out on the text information to obtain the segment information;
matching the segment information with the pre-stored category information according to a preset matching algorithm to obtain the matching category;
And storing the text segment information in a database in a text format according to the matching category.
Further, the generating a legal decision by responding to the legal decision making request initiated by the user, writing the target information queried by the user from all the text information into a legal decision template, and generating a legal decision, further includes:
and generating the legal decision template through a template engine.
Further, the generating a legal decision by responding to the legal decision making request initiated by the user, writing the target information queried by the user from all the text information into a legal decision template, and generating a legal decision, further includes:
responding to a target information query request initiated by the user, and sending the text segment information corresponding to the matching category input by the user to the user as the target information.
The embodiment of the invention has the following beneficial effects:
when the legal document uploaded by a user is a picture document, text extraction is carried out on the legal document, the obtained text information is stored, then the text information in the text information is matched with the pre-stored category information, and the text information is stored according to the obtained matching category classification, so that a legal decision book generation request initiated by the user is responded, target information inquired by the user from all the text information is written into a legal decision book template, and a legal decision book is generated. Compared with the prior art, the embodiment of the invention stores the text information in the legal document uploaded in a picture form and the text information in a classified storage manner, and can write the target information inquired by the user from all the text information into the legal decision template when the user initiates a legal decision book generation request, so that the legal decision book is automatically generated, the effective information in the picture file is fused, and the legal decision book is automatically generated.
Drawings
Fig. 1 is a schematic flow chart of a method for automatically generating a legal decision book according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating text information extraction according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating the classification of segment information according to the first embodiment of the present invention;
FIG. 4 is a schematic flow chart of legal decision making in the first embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an automatic legal decision making system according to a second embodiment of the present invention;
fig. 6 is a schematic flow chart of an automatic legal decision making system according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, the step numbers in the text are only for convenience of explanation of the specific embodiments, and do not serve to limit the execution sequence of the steps.
Please refer to fig. 1-4.
As shown in fig. 1, the first embodiment provides a legal decision making method, including steps S1 to S3:
and S1, acquiring the legal document uploaded by the user, extracting the text of the legal document when the legal document is a picture document, and storing the obtained text information.
And S2, matching the text segment information in the text information with pre-stored category information to obtain a matching category, and storing the text segment information according to the matching category in a classified manner.
And S3, responding to a legal decision book generation request initiated by the user, writing target information inquired by the user from all the text information into a legal decision book template, and generating a legal decision book.
As an example, in step S1, when the legal document uploaded by the user is acquired, it is determined whether the legal document is a picture document, and if the legal document is a picture document, text extraction is performed on the legal document, and the obtained text information is stored, so that the text segment information in the text information is stored in a classified manner in the following step.
In step S2, after storing the text information, the text information is filtered into a plurality of text information, and each text information is matched with the pre-stored category information to obtain a corresponding matching category, so as to store the text information according to the matching category, so that a subsequent user can directly query the corresponding text information according to the matching category.
In step S3, when a legal decision making request initiated by the user is received, the legal decision template is called according to the user requirement, and the target information queried by the user from all the text information is written into the legal decision template to make a legal decision.
In the embodiment, when the legal document uploaded by the user is a picture document, text extraction is performed on the legal document, the obtained text information is stored, then the text information in the text information is matched with the pre-stored category information, and the text information is classified and stored according to the obtained matching category, so that a legal decision book generation request initiated by the user is responded, target information inquired by the user from all the text information is written into a legal decision book template, and a legal decision book is generated. According to the method and the device, the text information in the legal document uploaded in a picture form is stored, and the text information in the text information is stored in a classified manner, so that when a user initiates a legal decision book generation request, target information inquired by the user from all the text information can be written into a legal decision book template, a legal decision book is automatically generated, effective information in the picture document is fused, and the legal decision book is automatically generated.
In a preferred embodiment, when the legal document is a picture document, performing text extraction on the legal document, and storing the obtained text information specifically includes: according to the optical character recognition technology, text extraction is carried out on legal documents to obtain text information, and the text information is stored in a database in a text format.
Optical Character Recognition (OCR) refers to a technology in which an electronic device, such as a scanner or a digital camera, inspects characters printed on paper, determines the shape of the characters by detecting dark and light patterns, and then translates the shape into computer characters by a Character Recognition method, that is, for the characters in the printed form, characters in a paper document are optically converted into an image file of a black-and-white dot matrix, and characters in the image are converted into a text format by Recognition software for further editing and processing by word processing software.
As shown in fig. 2, as an example, when a legal document uploaded by a user is acquired, it is determined whether the legal document is a picture document, if the legal document is not the picture document, the importing fails, at which point the legal document may not be subjected to text extraction according to an optical character recognition technique, and if the legal document is the picture document, the importing succeeds, at which point the legal document may be subjected to text extraction according to the optical character recognition technique. In the process of extracting the text information, if the characters cannot be recognized from the legal document or the recognized characters cannot be translated into the computer characters, stopping extracting the text from the legal document, if the characters can be recognized from the legal document and the recognized characters can be translated into the computer characters, taking the translated computer characters as the text information, storing the text information in a database in a text format, and then establishing a json data packet to generate, process and record and store the json data packet in the database.
According to the method and the device, the text extraction is carried out on the legal document according to the optical character recognition technology, so that the text information can be extracted from the legal document uploaded in a picture form, the effective information in the picture document is fused, and the legal judgment book is automatically generated.
In a preferred embodiment, the matching the text segment information in the text information with the pre-stored category information to obtain a matching category, and the storing the text segment information according to the matching category by category specifically includes: according to a pre-stored regular expression, text segment extraction is carried out on the text information to obtain text segment information; matching the text segment information with pre-stored category information according to a preset matching algorithm to obtain a matching category; and storing the text segment information in a database in a text format according to the matching category.
The preset matching algorithm includes, but is not limited to, a forward maximum matching algorithm, a reverse maximum matching algorithm, and a bidirectional matching algorithm.
The regular expression is a logic formula for operating on character strings, namely, specific characters defined in advance and a combination of the specific characters are used for forming a 'regular character string', and the 'regular character string' is used for expressing a filtering logic for the character strings. For example, by predefining keywords at the beginning and the end of a text segment, a regular expression is used for segmenting a single word string from text information, then the single word string is compared with pre-stored category information, if the single word string is a word, the single word string is recorded, otherwise, a single word is added or reduced, and the comparison with the pre-stored category information is continued until only one single word is left.
As shown in fig. 3, as an example, segment information is extracted from text information according to a pre-stored regular expression, and the segment information is matched with pre-stored category information according to a preset matching algorithm, if matching fails, the next segment information is continuously matched, and if matching succeeds, the segment information is stored in a database in a text format according to the matching category. After the classified storage of the text information is finished, when a user inputs the matched type query text information, the text information under the matched type can be screened from the database for table display, so that the user can quickly acquire required information from the table.
In a preferred embodiment, the generating of the legal decision in response to a legal decision making request initiated by a user writes target information queried by the user from all the text information into a legal decision template to generate the legal decision, further includes: and generating a legal decision book template through a template engine.
The template engine includes, but is not limited to, freeMaeker.
As shown in fig. 4, as an example, according to the referee document in the database, a word document is generated by a freeMaeker template engine, so that the xml template is directly converted into the word document, then specific information is extracted from the database, the xml document is filled into the xml document and stored in the database, further the xml document is converted into an ftl format, then the content is modified, and finally the legal decision template is created. And after the legal decision template is created, writing target information inquired by the user from all the text information into the legal decision template to generate a legal decision.
According to the embodiment, the legal decision template is generated through the template engine, so that the automatic generation of the legal decision is facilitated.
In a preferred embodiment, the generating of the legal decision in response to a legal decision making request initiated by a user writes target information queried by the user from all the text information into a legal decision template to generate the legal decision, further includes: responding to a target information query request initiated by a user, and sending the segment information corresponding to the matching category input by the user to the user as target information.
Illustratively, when a target information query request initiated by a user is received, according to the matching category input by the user, the segment information under the matching category is screened from all stored segment information to serve as the target information, and the target information is issued to the user.
In the embodiment, the segment information corresponding to the matching category input by the user is issued to the user as the target information by responding to the target information query request initiated by the user, so that the user can quickly acquire the target information.
Please refer to fig. 5-6.
As shown in fig. 5 to 6, the second embodiment provides a legal decision making system comprising: the text information extraction module 21 is configured to acquire a legal document uploaded by a user, perform text extraction on the legal document when the legal document is a picture document, and store the obtained text information; the text segment information classification module 22 is used for matching the text segment information in the text information with pre-stored category information to obtain a matching category, and classifying and storing the text segment information according to the matching category; and the legal decision making module 23 is configured to respond to a legal decision making request initiated by a user, and write target information queried by the user from all the text information into a legal decision making template to make a legal decision.
As an example, by the text information extraction module 21, when the legal document uploaded by the user is acquired, it is determined whether the legal document is a picture document, and if the legal document is a picture document, text extraction is performed on the legal document, and the obtained text information is stored, so that the text segment information in the text information is stored in a classified manner in the following process.
Through the text segment information classification module 22, after storing the text information, the text information is filtered into a plurality of text segment information, and each text segment information is respectively matched with the pre-stored category information to obtain a corresponding matching category, so that the text segment information is classified and stored according to the matching category, and a subsequent user can directly inquire the corresponding text segment information according to the matching category.
Through the legal decision making module 23, when a legal decision making request initiated by a user is received, the legal decision making template is called according to the user requirement, and target information inquired by the user from all the text information is written into the legal decision making template to make a legal decision.
In the embodiment, through the text information extraction module 21, when the legal document uploaded by the user is a picture document, text extraction is performed on the legal document, the obtained text information is stored, and then through the text information classification module 22, the text information in the text information is matched with the pre-stored category information, and the text information is classified and stored according to the obtained matching category, so that through the legal decision making module 23, a legal decision making request initiated by the user is responded, and target information inquired by the user from all the text information is written into the legal decision making template, and a legal decision making is generated. According to the method and the device, the text information in the legal document uploaded in a picture form is stored, and the text information in the text information is stored in a classified manner, so that when a user initiates a legal decision book generation request, target information inquired by the user from all the text information can be written into a legal decision book template, a legal decision book is automatically generated, effective information in the picture document is fused, and the legal decision book is automatically generated.
In a preferred embodiment, when the legal document is a picture document, performing text extraction on the legal document, and storing the obtained text information specifically includes: according to the optical character recognition technology, text extraction is carried out on legal documents to obtain text information, and the text information is stored in a database in a text format.
Optical Character Recognition (OCR) refers to a technology in which an electronic device, such as a scanner or a digital camera, inspects characters printed on paper, determines the shape of the characters by detecting dark and light patterns, and then translates the shape into computer characters by a Character Recognition method, that is, for the characters in the printed form, characters in a paper document are optically converted into an image file of a black-and-white dot matrix, and characters in the image are converted into a text format by Recognition software for further editing and processing by word processing software.
As an example, by the text information extraction module 21, when the legal document uploaded by the user is acquired, it is determined whether the legal document is a picture document, if the legal document is not a picture document, the importing fails, at which point the text extraction of the legal document according to the optical character recognition technology may not be performed, and if the legal document is a picture document, the importing succeeds, at which point the text extraction of the legal document according to the optical character recognition technology may be performed. In the process of extracting the text information, if the characters cannot be recognized from the legal document or the recognized characters cannot be translated into the computer characters, stopping extracting the text from the legal document, if the characters can be recognized from the legal document and the recognized characters can be translated into the computer characters, taking the translated computer characters as the text information, storing the text information in a database in a text format, and then establishing a json data packet to generate, process and record and store the json data packet in the database.
In the embodiment, the text information extraction module 21 is used for extracting the text of the legal document according to the optical character recognition technology, so that the text information can be extracted from the legal document uploaded in a picture form, the effective information in the picture document can be fused, and the legal decision book can be automatically generated.
In a preferred embodiment, the matching the text segment information in the text information with the pre-stored category information to obtain a matching category, and the storing the text segment information according to the matching category by category specifically includes: according to a pre-stored regular expression, text segment extraction is carried out on the text information to obtain text segment information; matching the text segment information with pre-stored category information according to a preset matching algorithm to obtain a matching category; and storing the text segment information in a database in a text format according to the matching category.
The preset matching algorithm includes, but is not limited to, a forward maximum matching algorithm, a reverse maximum matching algorithm, and a bidirectional matching algorithm.
The regular expression is a logic formula for operating on character strings, namely, specific characters defined in advance and a combination of the specific characters are used for forming a 'regular character string', and the 'regular character string' is used for expressing a filtering logic for the character strings. For example, by predefining keywords at the beginning and the end of a text segment, a regular expression is used for segmenting a single word string from text information, then the single word string is compared with pre-stored category information, if the single word string is a word, the single word string is recorded, otherwise, a single word is added or reduced, and the comparison with the pre-stored category information is continued until only one single word is left.
Illustratively, segment information is extracted from the text information by the segment information classification module 22 according to a pre-stored regular expression, and the segment information is matched with pre-stored category information according to a preset matching algorithm, if matching fails, the next segment information is continuously matched, and if matching succeeds, the segment information is stored in a database in a text format according to the matching category. After the classified storage of the text information is finished, when a user inputs the matched type query text information, the text information under the matched type can be screened from the database for table display, so that the user can quickly acquire required information from the table.
In a preferred embodiment, the generating of the legal decision in response to a legal decision making request initiated by a user writes target information queried by the user from all the text information into a legal decision template to generate the legal decision, further includes: and generating a legal decision book template through a template engine.
The template engine includes, but is not limited to, freeMaeker.
Illustratively, according to the referee document in the database, a word document is generated by a legal decision book generation module 23 through a freeMaeker template engine, so that the xml template is directly converted into the word document, then specific information is extracted from the database, the xml document is filled into the xml document and stored in the database, further the xml document is converted into an ftl format, then the content is modified, and finally the legal decision book template is created. And after the legal decision template is created, writing target information inquired by the user from all the text information into the legal decision template to generate a legal decision.
In this embodiment, the legal decision making module 23 generates the legal decision making template by using the template engine, which is beneficial to automatically generating the legal decision making.
In a preferred embodiment, the generating of the legal decision in response to a legal decision making request initiated by a user writes target information queried by the user from all the text information into a legal decision template to generate the legal decision, further includes: responding to a target information query request initiated by a user, and sending the segment information corresponding to the matching category input by the user to the user as target information.
Illustratively, through the legal decision rule generating module 23, when a target information query request initiated by a user is received, according to a matching category input by the user, segment information under the matching category is screened from all stored segment information as target information, and the target information is issued to the user.
In the embodiment, the legal decision making module 23 responds to the target information query request initiated by the user, and issues the text segment information corresponding to the matching category input by the user as the target information to the user, so that the user can quickly acquire the target information.
In summary, the embodiment of the present invention has the following advantages:
When the legal document uploaded by a user is a picture document, text extraction is carried out on the legal document, the obtained text information is stored, then the text information in the text information is matched with the pre-stored category information, and the text information is stored according to the obtained matching category classification, so that a legal decision book generation request initiated by the user is responded, target information inquired by the user from all the text information is written into a legal decision book template, and a legal decision book is generated. According to the embodiment of the invention, the text information in the legal document uploaded in a picture form is stored, and the text information in the text information is stored in a classified manner, so that when a user initiates a legal decision book generation request, target information inquired by the user from all the text information can be written into a legal decision book template, a legal decision book is automatically generated, effective information in the picture file is fused, and the legal decision book is automatically generated.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the above embodiments may be implemented by hardware related to instructions of a computer program, and the computer program may be stored in a computer readable storage medium, and when executed, may include the processes of the above embodiments. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A legal decision making method comprising:
the method comprises the steps of obtaining a legal document uploaded by a user, extracting a text of the legal document when the legal document is a picture document, and storing the obtained text information;
matching the text segment information in the text information with pre-stored category information to obtain a matching category, and storing the text segment information according to the matching category in a classified manner;
and responding to a legal decision book generation request initiated by the user, writing target information inquired by the user from all the text information into a legal decision book template, and generating a legal decision book.
2. The method for generating a legal decision making according to claim 1, wherein when the legal document is a picture document, the text extraction is performed on the legal document, and the obtained text information is stored, specifically:
And according to an optical character recognition technology, text extraction is carried out on the legal document to obtain the text information, and the text information is stored in a database in a text format.
3. The legal decision making method according to claim 1, wherein the matching of the text information in the text information with pre-stored category information to obtain a matching category, and the storing of the text information according to the matching category in a classified manner, specifically:
according to a pre-stored regular expression, segment extraction is carried out on the text information to obtain the segment information;
matching the segment information with the pre-stored category information according to a preset matching algorithm to obtain the matching category;
and storing the text segment information in a database in a text format according to the matching category.
4. The legal decision making method of claim 1, wherein said writing target information that the user inquires from all the segment information into a legal decision making template in response to the legal decision making request initiated by the user to make a legal decision, further comprises:
and generating the legal decision template through a template engine.
5. The legal decision making method of claim 1, wherein said writing target information that the user inquires from all the segment information into a legal decision making template in response to the legal decision making request initiated by the user to make a legal decision, further comprises:
responding to a target information query request initiated by the user, and sending the text segment information corresponding to the matching category input by the user to the user as the target information.
6. A legal decision making system comprising:
the text information extraction module is used for acquiring legal documents uploaded by a user, extracting texts of the legal documents when the legal documents are picture documents, and storing the obtained text information;
the text segment information classification module is used for matching the text segment information in the text information with pre-stored category information to obtain a matching category, and storing the text segment information according to the matching category;
and the legal decision making module is used for responding to a legal decision making request initiated by the user, writing target information inquired by the user from all the text information into a legal decision making template, and generating a legal decision making.
7. The system for generating a legal decision making according to claim 6, wherein when the legal document is a picture document, the system extracts a text of the legal document and stores the obtained text information, specifically:
and according to an optical character recognition technology, text extraction is carried out on the legal document to obtain the text information, and the text information is stored in a database in a text format.
8. The system for generating a legal decision as claimed in claim 6, wherein said matching the text information in the text information with pre-stored category information to obtain a matching category, and storing the text information according to the matching category by category, specifically:
according to a pre-stored regular expression, segment extraction is carried out on the text information to obtain the segment information;
matching the segment information with the pre-stored category information according to a preset matching algorithm to obtain the matching category;
and storing the text segment information in a database in a text format according to the matching category.
9. The legal decision making system according to claim 6, wherein said writing target information that the user inquires from all the segment information into a legal decision template in response to the user-initiated legal decision making request to make a legal decision, further comprises:
And generating the legal decision template through a template engine.
10. The legal decision making system according to claim 6, wherein said writing target information that the user inquires from all the segment information into a legal decision template in response to the user-initiated legal decision making request to make a legal decision, further comprises:
responding to a target information query request initiated by the user, and sending the text segment information corresponding to the matching category input by the user to the user as the target information.
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