阅读说明：本技术 确定争议焦点的方法和装置、存储介质和设备 (Method and device for determining dispute focus, storage medium and equipment ) 是由 陈春磊 刘洋 于 2019-09-27 设计创作，主要内容包括：本公开涉及一种确定争议焦点的方法和装置、存储介质和设备,所述方法包括：从案件的法律文书中确定诉讼请求描述和抗辩事由描述；根据预设的解析规则从所述诉讼请求描述确定诉请要素,以及从所述抗辩事由描述中确定抗辩要素；从所述诉请要素和所述抗辩要素中,确定对立的至少一组诉请要素和抗辩要素,作为目标焦点组；根据所述目标焦点组,生成所述案件的争议焦点。(The present disclosure relates to a method and apparatus, storage medium, and device for determining a point of dispute focus, the method comprising: determining a litigation request description and a disfiguration resistant routing description from the legal documents of the case; determining a complaint element from the litigation request description according to a preset resolving rule, and determining an anti-dialect element from the anti-dialect description; determining at least one set of opponent element and anti-convincing element from the appeal element and the anti-convincing element as a target focus group; and generating a dispute focus of the case according to the target focus group.)

1. A method of determining a point of dispute focus, the method comprising:

determining a litigation request description and a disfiguration resistant routing description from the legal documents of the case;

determining a complaint element from the litigation request description according to a preset resolving rule, and determining an anti-dialect element from the anti-dialect description;

determining at least one set of opponent element and anti-convincing element from the appeal element and the anti-convincing element as a target focus group;

and generating a dispute focus of the case according to the target focus group.

2. The method of claim 1, wherein said determining a litigation-request description and an anti-prodigial description from a legal document of a case comprises:

determining litigation requesting and anti-resolution passages from the legal document;

the litigation-request description is identified from the litigation-request passage and the contra-story description is identified from the contra-passage by text parsing.

3. The method of claim 1, wherein determining the prose element from the litigation request description according to a preset parsing rule, and wherein determining the dispute element from the dispute routing description comprises:

classifying the litigation request description and the anti-dispute affair route description according to a preset classification rule to generate a normative litigation request item and a normative dispute affair route item;

and extracting the litigation request items from the litigation request items and extracting the anti-dialect items from the anti-dialect matter items according to preset analysis rules aiming at each classified item.

4. The method of claim 3, wherein determining at least one set of opposing complaint elements and anti-complaint elements from the complaint elements and the anti-complaint elements as a target focal group comprises:

determining at least one set of litigation-requiring and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same category;

and determining whether an opposite keyword exists between each group of the appeal elements and the anti-argumentation elements, and if so, determining the group of the appeal elements and the anti-argumentation elements as the target focus group.

5. An apparatus for determining a point of dispute focus, the apparatus comprising:

a description determination module for determining a litigation-request description and a disfavor description from a legal document of a case;

the element determining module is used for determining the element of the petition from the litigation request description according to a preset resolving rule and determining the resistant element from the resistant course description;

a target determination module, configured to determine at least one set of opponent elements and anti-argy elements from the appeal elements and anti-argy elements as a target focal group;

and the focus generating module is used for generating a dispute focus of the case according to the target focus group.

6. The apparatus of claim 5, wherein the description determination module is configured to determine a litigation request paragraph and an anti-resolution paragraph from a legal document; the litigation-request description is identified from the litigation-request passage and the contra-story description is identified from the contra-passage by text parsing.

7. The apparatus of claim 5, wherein the element determining module comprises:

the matter generation submodule is used for classifying the litigation request description and the anti-dispute matter description according to a preset classification rule, and generating a normative litigation request matter and a normative dispute matter;

and the element extraction submodule is used for extracting the litigation request elements from the litigation request items and extracting the anti-dialect elements from the anti-dialect matter items according to a preset analysis rule aiming at each classified item.

8. The apparatus of claim 7, wherein the goal determination module comprises:

a group class determination submodule for determining at least one group of litigation-requiring elements and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same classification;

and the relation determining submodule is used for determining whether an opposite keyword exists between each group of the appeal elements and the anti-dialect elements, and if the opposite keyword exists, determining that the group of the appeal elements and the anti-dialect elements are the target focus group.

9. A storage medium having a program stored thereon, the program being adapted to carry out the steps of the method of any of claims 1-4 when executed by a processor.

10. An apparatus, characterized in that the apparatus comprises:

at least one processor, and at least one memory, bus connected with the processor;

the processor and the memory complete mutual communication through the bus;

the processor is configured to invoke program instructions in the memory to perform the steps of the method of any of claims 1-4.

Technical Field

The present disclosure relates to the field of data processing, and in particular, to a method and apparatus, a storage medium, and a device for determining a dispute focus.

Background

In civil litigation cases, there are cases where the original report and the reported report have different requested contents for the same item, and the civil litigation is also generally conducted on the basis of the item with disputed matters, and the problem to be solved in the court is the focus of disputes of the civil cases. The dispute focus comprises a fact focus, a law focus, an evidence focus and a program focus, is the main content of the court trial, is a main line penetrating through the official documents, and has an important role in civil officials.

The current focus of disputes is often summarized and summarized by judges, experts or law workers in the party's paper, which is inefficient and may be missed.

Disclosure of Invention

An object of the present disclosure is to provide a method and apparatus, a storage medium, and a device for determining a dispute focus to solve the above technical problems.

In a first aspect of the present disclosure, a method for determining a dispute focus is provided, including: determining a litigation request description and a disfiguration resistant routing description from the legal documents of the case; determining a complaint element from the litigation request description according to a preset resolving rule, and determining an anti-dialect element from the anti-dialect description; determining at least one set of opponent element and anti-convincing element from the appeal element and the anti-convincing element as a target focus group; and generating a dispute focus of the case according to the target focus group.

Optionally, said determining a litigation-request description and an anti-prosecution description from a legal document of a case comprises: determining litigation requesting and anti-resolution passages from the legal document; the litigation-request description is identified from the litigation-request passage and the contra-story description is identified from the contra-passage by text parsing.

Optionally, the determining the prose element from the litigation request description according to the preset parsing rule, and the determining the resistant element from the resistant course description comprises: classifying the litigation request description and the anti-dispute affair route description according to a preset classification rule to generate a normative litigation request item and a normative dispute affair route item; and extracting the litigation request items from the litigation request items and extracting the anti-dialect items from the anti-dialect matter items according to preset analysis rules aiming at each classified item.

Optionally, the determining at least one set of opponent elements and anti-disc elements from the appeal elements and anti-disc elements as a target focus set includes: determining at least one set of litigation-requiring and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same category; and determining whether an opposite keyword exists between each group of the appeal elements and the anti-argumentation elements, and if so, determining the group of the appeal elements and the anti-argumentation elements as the target focus group.

In a second aspect of the present disclosure, there is provided an apparatus for determining a dispute focus, comprising: a description determination module for determining a litigation-request description and a disfavor description from a legal document of a case; the element determining module is used for determining the element of the petition from the litigation request description according to a preset resolving rule and determining the resistant element from the resistant course description; a target determination module, configured to determine at least one set of opponent elements and anti-argy elements from the appeal elements and anti-argy elements as a target focal group; and the focus generating module is used for generating a dispute focus of the case according to the target focus group.

Optionally, the description determination module is configured to determine a litigation request section and an anti-contest section from a legal document; the litigation-request description is identified from the litigation-request passage and the contra-story description is identified from the contra-passage by text parsing.

Optionally, the element determining module includes: the matter generation submodule is used for classifying the litigation request description and the anti-dispute matter description according to a preset classification rule, and generating a normative litigation request matter and a normative dispute matter; and the element extraction submodule is used for extracting the litigation request elements from the litigation request items and extracting the anti-dialect elements from the anti-dialect matter items according to a preset analysis rule aiming at each classified item.

Optionally, the goal determination module includes: a group class determination submodule for determining at least one group of litigation-requiring elements and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same classification; and the relation determining submodule is used for determining whether an opposite keyword exists between each group of the appeal elements and the anti-dialect elements, and if the opposite keyword exists, determining that the group of the appeal elements and the anti-dialect elements are the target focus group.

In a third aspect of the present disclosure, a storage medium is provided, on which a program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the first aspect of the present disclosure.

In a fourth aspect of the present disclosure, there is provided an apparatus comprising: at least one processor, and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the steps of the method of any one of the first aspects of the disclosure.

By the technical scheme, litigation request description and anti-dispute description are extracted from the legal documents, and the litigation elements and the anti-dispute elements are further determined, so that at least one set of opposite litigation elements and anti-dispute elements are determined, and dispute focuses of cases are generated according to the opposite litigation elements and anti-dispute elements.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:

FIG. 1 is a flow chart illustrating a method of determining a point of dispute focus according to an exemplary disclosed embodiment.

FIG. 2 is a flow chart illustrating a method of determining a point of dispute focus according to another exemplary disclosed embodiment.

FIG. 3 is a block diagram illustrating an apparatus for determining a point of dispute focus in accordance with an exemplary disclosed embodiment.

FIG. 4 is a block diagram illustrating an apparatus according to an exemplary disclosed embodiment.

Detailed Description

The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.

FIG. 1 is a flow chart illustrating a method of determining a point of dispute focus according to an exemplary disclosed embodiment. As shown in fig. 1, the method comprises the steps of:

s11, determining litigation request description and dispute resolution description from the legal documents of the case.

The case may be a civil case and the legal documents may be civil appetitive, civil answer, or civil referee. The keywords described by the litigation request can be identified in the civil affair prosecution state, for example, the corresponding lition request and reason of fact are determined from the keyword "litigation request" and the segment with the paragraph number (such as one, two, three, etc.) after the litigation request, or from the keyword "fact and reason" and the segment with the paragraph number after the fact and reason, and are described as the litigation request. The keyword for marking the anti-dispute phrase description can be identified from the civil answer phrase, such as the keyword 'request phrase' and the segment with paragraph mark after the request phrase, or the keyword 'fact and reason' and the segment with paragraph mark after the fact and reason are determined to be the corresponding request phrase and reason, and the corresponding request phrase and reason are used as the anti-dispute phrase description, or the keyword 'think, claim, term' with subjective consciousness and the like are extracted, and the subsequent sentence segment is used as the litigation request description or the anti-dispute description. The process for the civil referee book is similar.

For example, in a civil referee book, the following expressions exist: "the original report asks the court to request litigation: first, the request order is returned to borrow 177,000 yuan. And secondly, requiring the directive to be reported to pay the interest 214, 952 Yuan, changing the second litigation request into a litigation request requiring the directive to be reported to pay the default fund of 84,000 Yuan, and withdrawing the litigation request requiring the interest to be paid. … … is alleged to confirm that the original debit principal is owed for 177,000 dollars, but not agreed to pay the default fee for 84,000 dollars. … … are believed that litigation requests for the original to require payment of the breach fund have been aged. The keywords ' litigation request ', ' one, two ', ' defendant dialect ', ' defendant ' and the like can be extracted from the ' litigation request ', ' one, two ', ' defendant and the like, and the description of the litigation request is obtained: "177,000 yuan for the return of the offend for the demand of the offend", "84,000 yuan for the payment of the offend for the demand of the offend", the event of obtaining the resistant debt is described as: "confirm that the original report debt principal 177,000 yuan but not agree to pay the default fund 84,000 yuan" and "the litigation request of the original report requiring payment of the default fund is legal.

Optionally, a litigation request section and an anti-argumentation section may also be determined from the legal documents, and the litigation request description identified from the litigation request section and the anti-argumentation description identified from the anti-argumentation section by text parsing.

The litigation request paragraph and the anti-argumentation paragraph can be determined from the legal document by extracting keywords, or the legal document can be segmented by a pre-trained segmentation model, so that the litigation request paragraph and the anti-argumentation paragraph can be determined. Specifically, the keyword "litigation request" and the following segment with segment labels (e.g., one, two, three, etc.) can be extracted from the legal document as litigation request segments, or the keyword "facts and reasons" and the following segment with segment labels can be extracted as resistant segments.

Optionally, in practical applications, after the "civil litigation request" is extracted, it may be determined whether there is an output value after the "fact and reason" paragraph, if not, the content after the "civil litigation request" keyword is output as the description of the litigation request, and if so, the description of the litigation request may be extracted continuously after the "litigant participant" keyword.

S12, determining the petition element from the litigation request description according to the preset resolving rule, and determining the anti-dialect element from the anti-dialect description.

The appetitive element and the anti-dialect element are highly summarized short sentences which can express the intention of the party. Specifically, the extracted litigation request description and the extracted counseling event description may be analyzed and processed by text analysis, the litigation request elements may be summarized from the litigation request description, and the counseling event elements may be summarized from the counseling event description.

For example, for litigation request descriptions: "177,000 yuan for claiming the awards of the awards" and "84,000 yuan for claiming the awards of the awards" can be summarized as the following claiming elements: "debit amount 177,000", "pay default money"; for the case of the resistant cause description: "confirm that the debt principal is 177,000 yuan but not agree to pay the default fund by 84,000 yuan" and the lawsuit request of the original claim for paying the default fund is lawsuit-aged ", can be summarized as the following anti-dialect elements: the arrearage amount is 177,000, the default fund is not paid, and the lawsuit aging is exceeded.

And S13, determining at least one set of opponent element and anti-argy element from the appeal element and the anti-argy element as a target focus group.

Among the appeal elements and the anti-debt elements, there may be those having an opposition relationship, for example, in the appeal elements "borrowed amount 177,000", "pay default money" and the anti-debt elements "owed amount 177,000", "no-pay default money" and "over-lawsuit time", the appeal elements "borrowed amount 177,000" and the appeal elements "owed amount 177,000" have the same meaning, and there is no opposition relationship; the appeal element 'pay default money' and the anti-debt element 'do not pay default money' have an opponent relationship; the anti-discriminative element "exceeding lawsuit time" has no related lawsuit element. Therefore, among the above appeal elements and the anti-debt elements, the set of appeal elements and anti-debt elements having an opponent relationship is "pay default money" and "not pay default money", and both constitute a set of target focus groups.

It should be noted that a case may have one target focal group or a plurality of target focal groups, and the existence of a plurality of target focal groups means that there are a plurality of dispute focuses for the case.

And S14, generating a dispute focus of the case according to the target focus group.

The text learning technology can be used for classifying and analyzing the focus problems of the target focus group, or extracting keywords in the focus problems, and judging dispute objects of the focus problems and the keywords, namely dispute focuses.

For example, for the target focus groups "pay default money" and "not pay default money", the keyword "default money" can be obtained, and thus, the dispute focus of the case is the problem of the default money.

Optionally, after the dispute focus is generated, the case information may be listed in a preset format for the user to view. For example, case information may be listed in the format of "case a-litigation request description-argument from description-appeal element-argument focus".

By the technical scheme, litigation request description and anti-dispute description are extracted from the legal documents, and the litigation elements and the anti-dispute elements are further determined, so that at least one set of opposite litigation elements and anti-dispute elements are determined, and dispute focuses of cases are generated according to the opposite litigation elements and anti-dispute elements.

FIG. 2 is a flow chart illustrating a method of determining a point of dispute focus according to another exemplary disclosed embodiment. As shown in fig. 2, the method comprises the steps of:

s21, determining litigation request description and dispute resolution description from the legal documents of the case.

The case may be a civil case and the legal documents may be civil appetitive, civil answer, or civil referee. The keywords described by the litigation request can be identified in the civil affair prosecution state, for example, the corresponding lition request and reason of fact are determined from the keyword "litigation request" and the segment with the paragraph number (such as one, two, three, etc.) after the litigation request, or from the keyword "fact and reason" and the segment with the paragraph number after the fact and reason, and are described as the litigation request. The keyword for marking the anti-dispute phrase description can be identified from the civil answer phrase, such as the keyword 'request phrase' and the segment with paragraph mark after the request phrase, or the keyword 'fact and reason' and the segment with paragraph mark after the fact and reason are determined to be the corresponding request phrase and reason, and the corresponding request phrase and reason are used as the anti-dispute phrase description, or the keyword 'think, claim, term' with subjective consciousness and the like are extracted, and the subsequent sentence segment is used as the litigation request description or the anti-dispute description. The process for the civil referee book is similar.

For example, in a civil referee book, the following expressions exist: "the original report asks the court to request litigation: first, the request order is returned to borrow 177,000 yuan. And secondly, requiring the directive to be reported to pay the interest 214, 952 Yuan, changing the second litigation request into a litigation request requiring the directive to be reported to pay the default fund of 84,000 Yuan, and withdrawing the litigation request requiring the interest to be paid. … … is alleged to confirm that the original debit principal is owed for 177,000 dollars, but not agreed to pay the default fee for 84,000 dollars. … … are believed that litigation requests for the original to require payment of the breach fund have been aged. The keywords ' litigation request ', ' one, two ', ' defendant dialect ', ' defendant ' and the like can be extracted from the ' litigation request ', ' one, two ', ' defendant and the like, and the description of the litigation request is obtained: "177,000 yuan for the return of the offend for the demand of the offend", "84,000 yuan for the payment of the offend for the demand of the offend", the event of obtaining the resistant debt is described as: "confirm that the original report debt principal 177,000 yuan but not agree to pay the default fund 84,000 yuan" and "the litigation request of the original report requiring payment of the default fund is legal.

Optionally, a litigation request section and an anti-argumentation section may also be determined from the legal documents, and the litigation request description identified from the litigation request section and the anti-argumentation description identified from the anti-argumentation section by text parsing.

The litigation request paragraph and the anti-argumentation paragraph can be determined from the legal document by extracting keywords, or the legal document can be segmented by a pre-trained segmentation model, so that the litigation request paragraph and the anti-argumentation paragraph can be determined. Specifically, the keyword "litigation request" and the following segment with segment labels (e.g., one, two, three, etc.) can be extracted from the legal document as litigation request segments, or the keyword "facts and reasons" and the following segment with segment labels can be extracted as resistant segments.

Optionally, in practical applications, after the "civil litigation request" is extracted, it may be determined whether there is an output value after the "fact and reason" paragraph, if not, the content after the "civil litigation request" keyword is output as the description of the litigation request, and if so, the description of the litigation request may be extracted continuously after the "litigant participant" keyword.

And S22, classifying the litigation request description and the anti-dispute affair description according to a preset classification rule, and generating a normative litigation request item and a normative dispute action item.

The litigation request description or the anti-dispute event description can be semantically identified to judge which classification the litigation request description or the anti-dispute event description belongs to, for example, the litigation request description or the anti-dispute event description can be a borrowing class, a default fund class, a nourishment fee class, a labor wage class and the like, each class has a respective normative expression template, keywords in the litigation request description or the anti-dispute event description can be extracted to be filled into the normative expression templates, and the litigation request item or the anti-dispute event item of the normative expression can be generated.

For example, for litigation request descriptions: "177,000 yuan for claiming the debt and returning the loan" can be classified as a loan class, 177,000 yuan of keywords are filled into the "X yuan for claiming the loan" template for the loan class, so that a normative litigation request item, namely, "177,000 yuan for claiming the loan", can be obtained, and similarly, "84,000 yuan for claiming the debt and paying the default fee" can be classified as a default money class, and "84,000 yuan for claiming the default payment" can be obtained; the antagonism dialectical term "confirms that the debt principal is 177,000 yuan but the disapproval of payment of the default fund is 84,000 yuan" can be classified as the default fund, and the normative dialectical term "disapproval of payment of the default fund is 84,000 yuan".

And S23, extracting the litigation request items to obtain the litigation elements and the anti-argumentation factors from the anti-argumentation event according to preset analysis rules aiming at each classified event.

Each classified item has its different parsing rule, for example, for the default fund type item, it can be parsed whether it has keywords "pay" or "not pay", and for the labor wage type, it can be parsed whether it has keywords "delinquent" or "not delinquent" or the like. Thus, litigation-requesting matters and counseling-resisting matters can be generated as highly summarized phrases that can express the will of the parties.

For example, for a lawsuit request item "request for payment of a default fund 84,000 yuan", the keyword "payment" under the default fund classification may be resolved to generate a corresponding lawsuit element "claim payment of a default fund"; for the contral cause of the breach of promise fund, namely, "not agreeing to pay 84,000 yuan", the keyword "not paying" under the contral fund classification can be analyzed, and the corresponding contral factor "not paying breach of promise fund" is generated.

S24, determining at least one set of litigation-requiring and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same category.

The appeal element and the anti-discriminative element under the same category are related elements aiming at the same event, and can be equivalent elements, opposite elements or related elements. For example, "X Yuan of the borrowed amount" and "X Yuan of the confirmed borrowed amount" are equivalent elements, and "claim payment of the default money" and "non-payment of the default money" are opposite elements. Therefore, the logic judgment is carried out on a group of complaint elements and anti-discriminative elements under the same classification, the method is closer to the practical application, and the computing resources required by semantic learning can be reduced.

And S25, determining whether an opposite keyword exists between each group of the appeal elements and the anti-argumentation elements, and if so, determining that the group of the appeal elements and the anti-argumentation elements are the target focus group.

The opposite keyword may be a negative keyword such as "none", "objection", or a different number word (for example, the keyword "two years" appears in the appealing element, but the keyword "three years" appears in the dialectical element). It should be noted that negative-meaning opposite keywords may appear in the appeasing element or the anti-recognition element, but if they appear simultaneously, they are considered to be equivalent elements.

And S26, generating a dispute focus of the case according to the target focus group.

The text learning technology can be used for classifying and analyzing the focus problems of the target focus group, or extracting keywords in the focus problems, and judging dispute objects of the focus problems and the keywords, namely dispute focuses.

For example, for the target focus groups "pay default money" and "not pay default money", the keyword "default money" can be obtained, and thus, the dispute focus of the case is the problem of the default money.

Optionally, after the dispute focus is generated, the case information may be listed in a preset format for the user to view. For example, case information may be listed in the format of "case a-litigation request description-argument from description-appeal element-argument focus".

By the technical scheme, litigation request description and anti-dispute matter description are extracted from a legal document, the litigation request description and the anti-dispute matter description are classified, the litigation request matters and the anti-dispute matter matters which are expressed in a standard are generated, and then the litigation elements and the anti-dispute elements under the same classification are determined, so that at least one set of contradictory elements and anti-dispute elements are determined, and the dispute focus of a case is generated according to the contradictory elements and the anti-dispute elements, therefore, the dispute focus of the case can be generated through the legal document of the case, manual participation is reduced, the efficiency and the correctness of determining the dispute focus are improved, and omission of the dispute focus can be reduced; by classifying and preprocessing the appeal elements and the anti-dispute elements, the required computing resources can be reduced, and the efficiency of determining the dispute focus is further improved.

FIG. 3 is a block diagram illustrating an apparatus for determining a point of dispute focus according to an exemplary disclosed embodiment, and as shown in FIG. 3, the apparatus 300 includes a description determination module 310, an element determination module 320, a goal determination module 330, and a focus generation module 340.

Wherein, the description determining module 310 is used for determining the litigation request description and the dispute resolution description from the legal documents of the case.

The element determining module 320 is configured to determine a prose element from the litigation request description according to a preset parsing rule, and determine an anti-dialect element from the anti-dialect description.

The target determining module 330 is configured to determine at least one set of opponent elements from the appeal elements and the anti-argy elements as a target focal group.

The focus generating module 340 is configured to generate a dispute focus of the case according to the target focus group.

Optionally, the description determination module is configured to determine a litigation request section and an anti-contest section from a legal document; the litigation-request description is identified from the litigation-request passage and the contra-story description is identified from the contra-passage by text parsing.

Optionally, the element determining module includes: the matter generation submodule is used for classifying the litigation request description and the anti-dispute matter description according to a preset classification rule, and generating a normative litigation request matter and a normative dispute matter; and the element extraction submodule is used for extracting the litigation request elements from the litigation request items and extracting the anti-dialect elements from the anti-dialect matter items according to a preset analysis rule aiming at each classified item.

Optionally, the goal determination module includes: a group class determination submodule for determining at least one group of litigation-requiring elements and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same classification; and the relation determining submodule is used for determining whether an opposite keyword exists between each group of the appeal elements and the anti-dialect elements, and if the opposite keyword exists, determining that the group of the appeal elements and the anti-dialect elements are the target focus group.

The dispute focus determining device comprises a processor and a memory, wherein the description determining module 310, the element determining module, the target determining module, the focus generating module and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.

The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel may set one or more, and the dispute focus is determined by adjusting the kernel parameters.

An embodiment of the present invention provides a storage medium on which a program is stored, which when executed by a processor implements the method for determining a dispute focus.

The embodiment of the invention provides a processor, which is used for running a program, wherein the method for determining the dispute focus is executed when the program runs.

The embodiment of the present invention provides an apparatus 400, where the apparatus 400 includes at least one processor 401, at least one memory 402 connected to the processor 401, and a bus 403; the processor 401 and the memory 402 complete communication with each other through the bus 403; processor 401 is operative to call program instructions in memory 402 to perform the above-described method of determining the focus of dispute. The device herein may be a server, a PC, a PAD, a mobile phone, etc.

The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: determining a litigation request description and a disfiguration resistant routing description from the legal documents of the case; determining a complaint element from the litigation request description according to a preset resolving rule, and determining an anti-dialect element from the anti-dialect description; determining at least one set of opponent element and anti-convincing element from the appeal element and the anti-convincing element as a target focus group; and generating a dispute focus of the case according to the target focus group.

Optionally, said determining a litigation-request description and an anti-prosecution description from a legal document of a case comprises: determining litigation requesting and anti-resolution passages from the legal document; the litigation-request description is identified from the litigation-request passage and the contra-story description is identified from the contra-passage by text parsing.

Optionally, the determining the prose element from the litigation request description according to the preset parsing rule, and the determining the resistant element from the resistant course description comprises: classifying the litigation request description and the anti-dispute affair route description according to a preset classification rule to generate a normative litigation request item and a normative dispute affair route item; and extracting the litigation request items from the litigation request items and extracting the anti-dialect items from the anti-dialect matter items according to preset analysis rules aiming at each classified item.

Optionally, the determining at least one set of opponent elements and anti-disc elements from the appeal elements and anti-disc elements as a target focus set includes: determining at least one set of litigation-requiring and dispute elements generated from the litigation-requiring and dispute-requiring items belonging to the same category; and determining whether an opposite keyword exists between each group of the appeal elements and the anti-argumentation elements, and if so, determining the group of the appeal elements and the anti-argumentation elements as the target focus group.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.

The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.