CN111797232A - Legal event map construction method, legal event reasoning method, device and electronic equipment - Google Patents
Legal event map construction method, legal event reasoning method, device and electronic equipment Download PDFInfo
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Abstract
The invention provides a legal event map construction method, a legal event reasoning method, a device and electronic equipment, wherein the method comprises the following steps: acquiring a legal event map; acquiring a first requirement and a first requirement relation of a legal event described by an interactive object based on a multi-round interactive strategy; and dynamically updating the legal event map by utilizing the first requirement and the first requirement relation to obtain a legal event evolution interaction map. The method comprises the steps of constructing a legal event evolution interaction map based on multiple rounds of interaction information, serving static and incomplete features of the event map, adding interaction and evolution characteristics to the event map, continuously supplementing requirements and relations of a legal event in the process of interaction with a user and an expert, adjusting the current legal reasoning event, and finally obtaining complete relevant information so as to ensure that materials required by reasoning are accurate and complete.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a legal event map construction method, a legal event reasoning device and electronic equipment.
Background
Inference is a very important and challenging task in the field of natural language processing, whose purpose is to make decisions on unseen input information using existing knowledge and inference techniques. Machine reasoning requires heuristics and strategies, which are usually done by knowledgeable domain experts. This process is where machine reasoning is difficult for an enterprise to scale, as it requires a lot of expert manpower to complete the strategy.
Machine reasoning is best suited for deterministic scenarios. That is, it is determined whether an event is real or whether it will occur. Machine Reasoning (Machine learning) has good application in tasks such as common sense question answering, fact detection, natural language Reasoning, visual common sense Reasoning, visual question answering, document level question answering, multiple rounds of semantic analysis and question answering.
For legal events, a legal text corpus in the judicial field is constructed, and natural language processing technology is used for mining the matters which are concerned by a user, occur in a specific scene, a specific plot, a specific time range and a specific area, have at least one role and have at least one action, and the matters are called events. Unlike general events, legal events emphasize the fact and phenomenon that the subjective will of the subject of the right is a transfer, and the basis for construction is the legal event. A large number of knowledge map reasoning technologies depend on a knowledge base constructed in an early stage, a complete knowledge base does not exist in law, the cognition of any expert also has deviation, legal knowledge is dynamically updated, in a consultation process, the description of a user may be inconsistent or incomplete, complete information cannot be obtained, and the accuracy required by law is difficult to achieve depending on the knowledge base constructed in the early stage.
Disclosure of Invention
In order to solve the problem of poor accuracy of automatic inference of legal events in the prior art, the invention provides a method and a device for data processing under multiple tasks and electronic equipment.
In a first aspect, the present invention provides a method for constructing a legal event map, comprising: acquiring a legal event map; acquiring a first requirement and a first requirement relation of a legal event described by an interactive object based on a multi-round interactive strategy; and dynamically updating the legal event map by utilizing the first requirement and the first requirement relation to obtain a legal event evolution interaction map.
Optionally, the obtaining of the first requirement and the first requirement relationship of the legal event described by the interactive object based on the multiple rounds of interaction strategies includes: understanding the legal event described by the interactive object, and acquiring at least one of the field, intention, event and legal entity of the legal event; extracting the first requirement and the first requirement relation based on the field, intention, event and legal entity of the legal event; generating feedback information based on the legal event, wherein the feedback information is used for interacting with the interacted object; repeatedly executing the step to understand the legal events described by the interactive objects, and acquiring the field, intention, events and legal entities of the legal events; extracting the first requirement and the first requirement relation based on the field, intention, event and legal entity of the legal event; and generating feedback information based on the legal events until the legal event evolution interaction map can represent complete information of the legal events.
Optionally, the extracting the first requirement and the first requirement relationship based on the domain, the intention, the event and the legal entity of the legal event comprises: tracking and recording textual information including at least one of the domain, the intent, the event, and the legal entity, the textual information including contextual information and semantic relationships described by the interactive object; and predicting the relation between the first requirement and the first requirement in the text information by using a prediction model, wherein the prediction model is obtained by training by using the text information.
Optionally, the generating feedback information based on the legal event comprises: comparing the first requirement and the first requirement relation with preset legal information; adjusting the inference logic of the current legal event according to the comparison result; and generating natural language interacting with the interacted object based on the adjusted legal events.
Optionally, the obtaining the legal event map comprises: defining a legal event map; collecting legal corpora on a legal event map; and extracting a second requirement and a second requirement relation from the legal corpus based on the legal corpus.
According to a second aspect, an embodiment of the present invention provides a legal event inference method, including: acquiring a legal event interaction map; inputting the legal event interaction map into a pre-training reasoning model to obtain a reasoning result of the legal event; the pre-trained inference model is obtained based on training of the legal event graph generated by the legal event graph construction method of any one of the first aspect.
According to a third aspect, an embodiment of the present invention provides a legal event building apparatus, including: the first acquisition module is used for acquiring a legal event map; the second acquisition module is used for acquiring a first requirement and a first requirement relation of the legal event described by the interactive object based on the multi-round interaction strategy; and the updating module is used for dynamically updating the legal event map by utilizing the first requirement and the first requirement relation to obtain a legal event evolution interaction map.
Optionally, the second obtaining module includes: the language understanding unit is used for understanding the legal events described by the interactive objects and acquiring at least one of the fields, intentions, events and legal entities of the legal events; the extracting unit is used for extracting the first requirement and the first requirement relation based on the field, the intention, the event and the legal entity of the legal event; and the interaction information generating unit is used for generating feedback information based on the legal events, and the feedback information is used for interacting with the interacted object.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute the method for building a legal event graph according to any one of the above first aspects or the method for reasoning about legal events according to the above second aspect.
According to a fifth aspect, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the legal event graph construction method of any one of the above first aspects or the legal event inference method of the above second aspect.
The method comprises the steps of constructing a legal event evolution interaction map based on multiple rounds of interaction information, serving static and incomplete features of the event map, adding interaction and evolution characteristics to the event map, continuously supplementing requirements and relations of a legal event in the process of interaction with a user and an expert, adjusting the current legal reasoning event, and finally obtaining complete relevant information so as to ensure that materials required by reasoning are accurate and complete.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a legal event map construction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a first round of event evolution interaction map provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a second round of event evolution interaction map provided by the embodiment of the present invention;
FIG. 4 is a schematic diagram of an event evolution interaction map of a third round provided by the embodiment of the invention;
FIG. 5 is a schematic diagram of an event evolution interaction map of a fourth round according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a multi-turn question-answering framework provided by an embodiment of the present invention;
FIG. 7 is a flowchart illustrating a legal event inference method according to an embodiment of the present invention;
FIG. 8 is a diagram of a probability of propositions in a three-level theory according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a legal event map building apparatus according to an embodiment of the present invention;
fig. 10 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As described in the background, current knowledge maps suffer from a significant drawback: most researches are mainly used for describing static conceptual knowledge, the dynamic and procedural description of the knowledge is limited, and the mechanism of reasoning is also limited.
To a certain extent, the problem is also the granularity problem of knowledge acquisition, and to solve the problem, research on events as core concepts is being started, and unlike entities, events can be used as direct portraits of human behavior activities, and compared with entities, the entities have richer semantics, and the temporal relationship, causal relationship, sequential bearing relationship and other relationships among events describe knowledge of an instance layer better. An event is a special type of knowledge that can be viewed as a complex combination of empirical facts and factual relationships observed in text. Events are distinguished from entities in that events provide a context for understanding, or framework for understanding, deeper levels of expression for knowledge. For example, a chinese patent with publication number CN109977237A discloses a method for constructing a dynamic legal event map for legal field, the minimum unit of the dynamic legal event map is a legal event rather than an entity, and the constructed map is composed of the relationship between the legal event and the legal event; compared with the traditional event extraction, the knowledge in the constructed dynamic legal event graph is dynamic, computable and reasonable, and is not simple for extracting the event. The event graph provides representation and calculation foundation of knowledge in the theoretical background in a computer, for example, representation of an entity, attributes and characteristics of legal entities such as a behavior main body, a hazard behavior, a crime main body and the like are constrained through an ontology, and the representation different from a character string has more comprehensive context and semantic relationship. For the expression of logic, the relation of entities and events in cases and laws and regulations is extracted, and a relation example library in the legal field is constructed. In summary, an event graph is a computer description of theoretical knowledge and real-world events, enabling the computer to have the ability to identify, understand, and compute them. However, the inventor finds that a large number of knowledge graph reasoning technologies depend on a knowledge base constructed in the early stage, a complete knowledge base does not exist in law, the cognition of any expert may have deviation, legal knowledge is dynamically updated, the description of a user may be inconsistent or incomplete in the consultation process, and a graph without evolution and interaction obviously cannot acquire complete information, so that the accuracy required by the law is difficult to achieve by depending on the knowledge base constructed in the early stage (for example, a Chinese patent with publication number "CN 109977237A"). Based on the inventor, a method for constructing a legal event map is provided, and as shown in fig. 1, the method can comprise the following steps:
s11, obtaining a legal event map. Specifically, a legal event map is defined; collecting legal corpora on a legal event map; and extracting a second requirement and a second requirement relation from the legal corpus based on the legal corpus. The second requirement may be identified in the legal corpus based on the legal corpus, such as legal documents, legal files, and the like.
S12, acquiring a first requirement and a first requirement relation of the legal event described by the interactive object based on the multiple rounds of interaction strategies. Specifically, understanding the legal event described by the interactive object, and acquiring at least one of the field, intention, event and legal entity of the legal event; extracting the first requirement and the first requirement relation based on the field, intention, event and legal entity of the legal event; generating feedback information based on the legal event, wherein the feedback information is used for interacting with the interacted object; repeatedly executing the step to understand the legal events described by the interactive objects, and acquiring the field, intention, events and legal entities of the legal events; extracting the first requirement and the first requirement relation based on the field, intention, event and legal entity of the legal event; and generating feedback information based on the legal events until the legal event evolution interaction map can represent complete information of the legal events. In this embodiment, the completion information may be determined according to actual conditions.
In the process of acquiring the first requirement and the first requirement relationship, interaction information of a user can be acquired, and text information including at least one of the field, the intention, the event and the legal entity is tracked and recorded, wherein the text information includes context information and semantic relationship described by an interactive object; and predicting the first requirement and the first requirement relation in the text information by using a prediction model, wherein the prediction model is obtained by training by using the text information.
And S13, dynamically updating the legal event map by utilizing the first requirement and the first requirement relation to obtain a legal event evolution interaction map.
The method comprises the steps of constructing a legal event evolution interaction map based on multiple rounds of interaction information, serving static and incomplete features of the event map, adding interaction and evolution characteristics to the event map, continuously supplementing requirements and relations of a legal event in the process of interaction with a user and an expert, adjusting the current legal reasoning event, and finally obtaining complete relevant information so as to ensure that materials required by reasoning are accurate and complete.
Specifically, a user interacts with the event map engine through a plurality of rounds of question and answer engines to gradually form an event evolution interaction map example aiming at the legal intention and the legal scene of the user, the example represents the explained complete information of the user and is represented in a knowledge map mode, and the identification and calculation of a computer are facilitated. An exemplary event evolution interaction graph constructed by the dialog shown in fig. 2-5 is used as an example for illustration:
after the session starts, the user states: "i drink some liquor, drive and hit passerby, how do" identifies natural language based on question and answer engine, constructs the time evolution interaction map of the first round shown in fig. 2: the behavior subject is 'my', criminal behavior 'driving after drinking' is implemented, and the harm result 'colliding' is caused. And based on the user's natural language and an action runner in the question-and-answer engine, such as a query for laws and regulations, understand the user's interactive content and generate corresponding dialog feedback to the user. Based on the user's interactive content, the following dialog may be generated: a machine: "ask for what the content of blood alcohol test is", user: "185 mg/ml", based on the natural language recognized by the question-answering engine, the second round of time-evolution interaction graph shown in fig. 3 is constructed by supplementing the requirements of the events and the relationship between the requirements on the basis of fig. 2: the essential attributes of the add event are: the alcohol content was 185 mg/ml. Continuing to understand the user interaction context, a new dialog is generated: a machine: "ask what car you drive", user: "family Benz". Based on the natural language recognized by the question-answering engine, the third round of time evolution interaction graph shown in fig. 4 is constructed by supplementing the requirements of the events and the relationship between the requirements on the basis of fig. 3: the essential attributes of the add event are: the vehicle type is that of a galloping vehicle belonging to a common motor vehicle. Continuing to understand the user interaction context, a new dialog is generated: a machine: "hit several people, how well the hit passerby is", user: "hit 3, 1 severe injury 1 death". Based on the question-answering engine to identify natural language, the relationship between the key elements of the event and the key elements is supplemented on the basis of fig. 4, so as to construct a fourth turn of the time evolution interaction map shown in fig. 5: the requirements for the add event result is progressively: resulting in progressive crash and bruising. The four-round interactive question-answering in the embodiment is only an exemplary illustration of the construction process of the event evolution interaction map, and more rounds of interaction and fewer rounds of interaction are also within the protection scope of the embodiment of the present application.
And continuously identifying the supplemented essential elements such as behavior bodies, hazard behaviors, criminal objects, hazard results and the like and the relationship among the essential elements based on the event evolution interaction map, and finally, existing in the instance of the knowledge map.
The event evolution interaction map is constructed by a knowledge map generated by dynamic interaction updating, the information of the user is interacted with the user according to the intention of the user, the corresponding information of legal event scenes, elements, experiences and the like, the information of the user is obtained, the event evolution interaction map is dynamically constructed by system processing, and the map reveals complete information of legal propositions.
As an exemplary embodiment, for the prediction model, see fig. 6 for a multi-turn question-and-answer framework of the identification process involved in the evolutionary interaction process:
wherein
The XNLP represents a natural language processing module and is used for lexical analysis, syntactic analysis and the like of legal texts.
The XNLU represents a natural language understanding module for domain recognition, intent recognition, event recognition and legal entity recognition of legal text
The dialog Engine represents a dialog Engine, and comprises two core modules: state trackers and policy management. The state tracker records the context of the user interaction, and the policy management is used to distinguish the action returned by the current dialog machine.
The Action Runner represents an Action Runner that runs skills of the robot, such as inquiry of laws and regulations, and the like.
And the XNLG represents natural language generation, automatically generates a dialogue according to a feedback action, feeds the dialogue back to a user, feeds the currently collected information back to the event evolution interaction map, and updates the current map example. Specifically, the generating of the feedback information based on the legal event includes: comparing the first requirement and the first requirement relation with preset legal information; adjusting the inference logic of the current legal event according to the comparison result; and generating natural language interacting with the interacted object based on the adjusted legal events.
The embodiment of the present application further provides a legal practice reasoning method, as shown in fig. 7, the method may include the following steps:
and S21, obtaining a legal event interaction map. The details of the construction method of the legal event map can be seen in the above embodiments.
And S22, inputting the legal event interaction map into a pre-training reasoning model to obtain a reasoning result of the legal event. The pre-trained inference model is obtained by training based on the legal event graph generated by the legal event graph construction method described in the embodiment.
Specifically, for the training of the inference model, the inference model can be constructed first, and after the legal event interaction map is obtained, the legal proposition representation can be performed on the legal event map by using an inference representation framework corresponding to the knowledge type based on the knowledge type in the legal event map;
and performing probabilistic representation on the legal proposition by using a three-level theory to obtain a legal proposition probability model. In this embodiment, the knowledge types in the legal event graph may include entity relationships of the essences in the event, attribute relationships of the essences in the event, and descriptive text. Different inference representation frameworks are employed for different knowledge types in this embodiment, and the so-called inference representation frameworks may include a symbolic logic-based representation framework, a statistical rule-based representation framework, and an embedded vector representation framework. Therefore, in the embodiment, a plurality of reasoning representation modes are applied, and propositions are observed from different angles. Several representations are generated based on the specific knowledge of the event evolution interaction map, and since different knowledge fits different representation frameworks, multiple representations are required to be able to solve the problem. The factual entity relationships in the event that a common speed is a motor vehicle, for example, are consistent with a symbol-based logical representation, namely, the representation as < speed, is, motor vehicle >. The requirement attribute relationship of alcohol content and drunk driving can be statistically calculated from a large Number of cases, and forms knowledge, which is expressed as Number (alcohol content, quantity) > Action (drunk driving).
Some descriptive textual knowledge is difficult to represent in some formal language, which is a solution using representation learning, and representing propositions as high-dimensional vectors, e.g. "breathe while alighting" is difficult to directly map to propositions S (death or death), which can be represented as high-dimensional vectors in this embodiment based on a large number of pre-trained corpora, i.e. S ("breathe while alighting") [1.2,2.34,63.4, …,5.6 ]. The expression based on symbolic logic refers to extracting corresponding ontology content from the event evolution interaction map, and reasoning the relationship between concept classification and concept based on the rule of a production formula. The event interaction evolution map representation can be expressed as: symbolic logic, statistical rules and embedded vectors, and the three data completely store fact requirement information.
The three-level theory is used for representing the law proposition in a probabilistic manner to obtain a law proposition probability model, specifically, the three-level theory proposition probability graph shown in figure 8 can be used for representing the law proposition in a probabilistic manner, the event evolution interaction graph comprises a large amount of event knowledge, the event knowledge is required to serve the three-level theory, the facts and the proposition need to be quantized, after the quantization, the probability graph model is used for associating according to the evolution relation of the three-level theory, and finally the representing process is finished
Specifically, referring to the probability diagram of the proposition in the three-level theory shown in fig. 8, propositions are represented by x1, x2, x3, …, xn: an action subject proposition of x1, an attack object proposition of x2, an action result establishment proposition of x3, a causal relationship proposition of x4, an intentional proposition of x5, an intention proposition of x5, an objective constituent proposition of x6, a subjective constituent proposition of x7, a constituent proposition of legitimacy of x8, and the like. The overall probability formula is as follows:
the method comprises the following steps of decomposing step by step according to the dependency relationship in the graph, converting corresponding marginal probability distribution in case data, and further performing overall distribution construction, such as objectively constructing requirement propositions:
P(x6|x1，x2，x3)
the key element legality distribution:
P(x8|x6，x7)
after each distribution is obtained, model learning and reasoning can be performed.
If it is assumed that the crime is true proposition Y, then parameter estimation can be done according to the following formula:
wherein, Y*The parameters of the reasoning model are Y, crime establishment proposition and X legal proposition.
After the model parameters are estimated, the construction of a reasoning model is completed, and when a new event interaction evolution diagram exists, the current model can be used for prediction.
An embodiment of the present application provides a legal event building apparatus, as shown in fig. 9, the apparatus may include: the first acquisition module 10 is used for acquiring a legal event map; the second obtaining module 20 is configured to obtain a first requirement and a first requirement relationship of a legal event described by an interactive object based on multiple rounds of interaction strategies; and the updating module 30 is configured to dynamically update the legal event map by using the first requirement and the first requirement relationship to obtain a legal event evolution interaction map.
As an alternative embodiment, the second obtaining module 20 includes: the language understanding unit is used for understanding the legal events described by the interactive objects and acquiring at least one of the fields, intentions, events and legal entities of the legal events; the extracting unit is used for extracting the first requirement and the first requirement relation based on the field, the intention, the event and the legal entity of the legal event; and the interaction information generating unit is used for generating feedback information based on the legal events, and the feedback information is used for interacting with the interacted object.
An embodiment of the present invention provides an electronic device, as shown in fig. 10, the electronic device includes one or more processors 31 and a memory 32, and one processor 33 is taken as an example in fig. 10.
The controller may further include: an input device 33 and an output device 34.
The processor 31, the memory 32, the input device 33 and the output device 34 may be connected by a bus or other means, and the bus connection is exemplified in fig. 10.
The processor 31 may be a Central Processing Unit (CPU). The processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 32, which is a non-transitory computer readable storage medium, can be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the control methods in the embodiments of the present application. The processor 31 executes various functional applications of the server and data processing, i.e., the legal event map construction method and/or the legal event inference method of the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 32.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of a processing device operated by the server, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, which may be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 33 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the processing device of the server. The output device 34 may include a display device such as a display screen.
One or more modules are stored in the memory 32, which when executed by the one or more processors 31 perform the methods illustrated in fig. 1 and/or 7.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the motor control methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a Random Access Memory (RAM), a flash memory (FlashMemory), a hard disk (hard disk drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Finally, the principle and the implementation of the present invention are explained by applying the specific embodiments in the present invention, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for constructing a legal event map, comprising:
acquiring a legal event map;
acquiring a first requirement and a first requirement relation of a legal event described by an interactive object based on a multi-round interactive strategy;
and dynamically updating the legal event map by utilizing the first requirement and the first requirement relation to obtain a legal event evolution interaction map.
2. The legal event graph construction method of claim 1, wherein obtaining the first requirement and the first requirement relationship of the legal event described by the interactive object based on multiple rounds of interaction strategies comprises:
understanding the legal event described by the interactive object, and acquiring at least one of the field, intention, event and legal entity of the legal event;
extracting the first requirement and the first requirement relation based on the field, intention, event and legal entity of the legal event;
generating feedback information based on the legal event, wherein the feedback information is used for interacting with the interacted object;
repeatedly executing the step to understand the legal events described by the interactive objects, and acquiring the field, intention, events and legal entities of the legal events; extracting the first requirement and the first requirement relation based on the field, intention, event and legal entity of the legal event; and generating feedback information based on the legal events until the legal event evolution interaction map can represent complete information of the legal events.
3. The method of claim 2, wherein the event map is a map of the events; the extracting the first requirement and the first requirement relationship based on the domain, the intention, the event and the legal entity of the legal event comprises:
tracking and recording textual information including at least one of the domain, the intent, the event, and the legal entity, the textual information including contextual information and semantic relationships described by the interactive object;
and predicting the relation between the first requirement and the first requirement in the text information by using a prediction model, wherein the prediction model is obtained by training by using the text information.
4. The legal event map construction method of claim 2, wherein the generating feedback information based on the legal event comprises:
comparing the first requirement and the first requirement relation with preset legal information;
adjusting the inference logic of the current legal event according to the comparison result;
and generating natural language interacting with the interacted object based on the adjusted legal events.
5. The method of claim 1, wherein the obtaining the legal event map comprises:
defining a legal event map;
collecting legal corpora on a legal event map;
and extracting a second requirement and a second requirement relation from the legal corpus based on the legal corpus.
6. A legal event inference method, comprising:
acquiring a legal event interaction map;
inputting the legal event interaction map into a pre-training reasoning model to obtain a reasoning result of the legal event;
the pre-trained inference model is trained based on a legal event graph generated by the legal event graph construction method of any one of claims 1-5.
7. A legal event building apparatus comprising:
the first acquisition module is used for acquiring a legal event map;
the second acquisition module is used for acquiring a first requirement and a first requirement relation of the legal event described by the interactive object based on the multi-round interaction strategy;
and the updating module is used for dynamically updating the legal event map by utilizing the first requirement and the first requirement relation to obtain a legal event evolution interaction map.
8. The legal event map building apparatus of claim 1, wherein the second obtaining module comprises:
the language understanding unit is used for understanding the legal events described by the interactive objects and acquiring at least one of the fields, intentions, events and legal entities of the legal events;
the extracting unit is used for extracting the first requirement and the first requirement relation based on the field, the intention, the event and the legal entity of the legal event;
and the interaction information generating unit is used for generating feedback information based on the legal events, and the feedback information is used for interacting with the interacted object.
9. A computer-readable storage medium storing computer instructions for causing a computer to execute the legal event graph construction method of any one of claims 1-5 or the legal event inference method of claim 6.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the legal event graph construction method of any one of claims 1-5 or the legal event inference method of claim 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115358896A (en) * | 2022-10-20 | 2022-11-18 | 四川大学华西医院 | Method, device, equipment and medium for constructing criminal evolution network by using massive documents |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104166493A (en) * | 2013-03-14 | 2014-11-26 | 三星电子株式会社 | Electronic device with social interaction mechanism and method of operation thereof |
| US20160275432A1 (en) * | 2015-03-20 | 2016-09-22 | Adp, Llc | Trending chart representation of healthcare law compliance |
| CN107330071A (en) * | 2017-06-30 | 2017-11-07 | 北京神州泰岳软件股份有限公司 | A kind of legal advice information intelligent replies method and platform |
| CN108073988A (en) * | 2017-06-21 | 2018-05-25 | 北京华宇元典信息服务有限公司 | A kind of law cognitive approach, device and medium based on intensified learning |
| CN109977237A (en) * | 2019-05-27 | 2019-07-05 | 南京擎盾信息科技有限公司 | A kind of dynamic law occurrence diagram spectrum construction method towards legal field |
| CN110059193A (en) * | 2019-06-21 | 2019-07-26 | 南京擎盾信息科技有限公司 | Legal advice system based on law semanteme part and document big data statistical analysis |
| CN110232564A (en) * | 2019-08-02 | 2019-09-13 | 南京擎盾信息科技有限公司 | A kind of traffic accident law automatic decision method based on multi-modal data |
| CN110377715A (en) * | 2019-07-23 | 2019-10-25 | 天津汇智星源信息技术有限公司 | Reasoning type accurate intelligent answering method based on legal knowledge map |
| CN110414007A (en) * | 2019-08-02 | 2019-11-05 | 南京擎盾信息科技有限公司 | A kind of legal concept recognition methods based on legal principle rule map engine |
-
2020
- 2020-06-11 CN CN202010533502.6A patent/CN111797232B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104166493A (en) * | 2013-03-14 | 2014-11-26 | 三星电子株式会社 | Electronic device with social interaction mechanism and method of operation thereof |
| US20160275432A1 (en) * | 2015-03-20 | 2016-09-22 | Adp, Llc | Trending chart representation of healthcare law compliance |
| CN108073988A (en) * | 2017-06-21 | 2018-05-25 | 北京华宇元典信息服务有限公司 | A kind of law cognitive approach, device and medium based on intensified learning |
| CN107330071A (en) * | 2017-06-30 | 2017-11-07 | 北京神州泰岳软件股份有限公司 | A kind of legal advice information intelligent replies method and platform |
| CN109977237A (en) * | 2019-05-27 | 2019-07-05 | 南京擎盾信息科技有限公司 | A kind of dynamic law occurrence diagram spectrum construction method towards legal field |
| CN110059193A (en) * | 2019-06-21 | 2019-07-26 | 南京擎盾信息科技有限公司 | Legal advice system based on law semanteme part and document big data statistical analysis |
| CN110377715A (en) * | 2019-07-23 | 2019-10-25 | 天津汇智星源信息技术有限公司 | Reasoning type accurate intelligent answering method based on legal knowledge map |
| CN110232564A (en) * | 2019-08-02 | 2019-09-13 | 南京擎盾信息科技有限公司 | A kind of traffic accident law automatic decision method based on multi-modal data |
| CN110414007A (en) * | 2019-08-02 | 2019-11-05 | 南京擎盾信息科技有限公司 | A kind of legal concept recognition methods based on legal principle rule map engine |
Non-Patent Citations (1)
| Title |
|---|
| 邹爱玲: ""基于法律的知识图谱构建"", 《中国优秀硕士学位论文全文数据库 社会科学Ⅰ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115358896A (en) * | 2022-10-20 | 2022-11-18 | 四川大学华西医院 | Method, device, equipment and medium for constructing criminal evolution network by using massive documents |
| CN115358896B (en) * | 2022-10-20 | 2023-02-03 | 四川大学华西医院 | Method, device, equipment and medium for constructing criminal evolution network by using massive documents |
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