CN114218452A - Lawyer recommending method and device based on public information and electronic equipment - Google Patents
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Abstract
The invention provides a lawyer recommending method and device based on public information and electronic equipment, and relates to the technical field of intelligent recommendation, wherein the method comprises the steps of obtaining case description of a user; processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof; matching the case keywords and the parts of speech thereof in the acquired lawyer graph database to obtain matched lawyers and the association degree of the matched lawyers and the case description of the user; sorting the matched lawyers based on the relevance degree to obtain a lawyer recommendation list; presenting the lawyer recommendation list to the user. The method and the device improve the efficiency and accuracy of searching lawyers by the user.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to a lawyer recommending method and device based on public information and electronic equipment.
Background
At present, lawyers found in the market by using a software method are mainly filtered and matched through fixed conditions such as lawyer names, cities, skilled fields, occupation years and the like, and the requirements of users cannot be accurately matched.
Therefore, a method, a device and an electronic device for recommending lawyers based on public information are provided.
Disclosure of Invention
The specification provides a lawyer recommending method based on public information, a lawyer recommending device based on the public information and an electronic device, and the efficiency and accuracy of searching lawyers by a user are improved.
The method for recommending lawyers based on public information provided by the application adopts the following technical scheme that the method comprises the following steps:
acquiring case description of a user;
processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof;
matching the case keywords and the parts of speech thereof in the acquired lawyer graph database to obtain matched lawyers and the association degree of the matched lawyers and the case description of the user;
sorting the matched lawyers based on the relevance degree to obtain a lawyer recommendation list;
presenting the lawyer recommendation list to the user.
Optionally, the obtained lawyer graph database includes:
acquiring public information of lawyers;
constructing an entity relationship network taking lawyers as the center based on the public information of the lawyers;
and fusing a plurality of entity relationship networks taking lawyers as centers to obtain the lawyer graph database.
Optionally, the processing the case description through the natural language processing model to obtain case keywords and parts of speech thereof includes:
performing word segmentation processing on the case description to obtain case keywords;
and performing part-of-speech analysis on the case keywords to obtain the parts-of-speech of the case keywords.
Optionally, the processing the case description through the natural language processing model to obtain case keywords and parts of speech thereof further includes:
judging whether the part of speech of the case keyword is a verb or an adjective;
and when the part of speech of the case keyword is a verb or an adjective, attaching a label to the case description.
Optionally, the matching the case keywords and the parts of speech thereof in the obtained lawyer graph database to obtain the degree of association between the matched lawyers and the case description of the user includes:
matching lawyers in the acquired lawyer graph database based on the case keywords and/or the tags;
and obtaining the association degree of the matched lawyers and the case description according to the part-of-speech priority of the case keywords.
Optionally, the obtaining of the association degree between the matched lawyer and the case description according to the part-of-speech priority of the case keyword includes:
acquiring weighting coefficients corresponding to the parts of speech of different case keywords, wherein the weighting coefficients are in direct proportion to the priority of the parts of speech of the case keywords;
acquiring all the case keywords of the matched lawyers;
determining weighted values of all the case keywords of the matched lawyers based on the weighted coefficients, wherein the weighted values are used as the association degrees of the matched lawyers and the case description.
Optionally, the sorting the matched lawyers based on the association degree to obtain a lawyer recommendation list, further includes:
judging whether the association degree exceeds a preset threshold value or not;
and when the relevance exceeds a preset threshold, sorting the matched lawyers with the relevance exceeding the preset threshold to obtain a lawyer recommendation list.
Optionally, the presenting the lawyer recommendation list to the user further includes:
acquiring a display mode selected by a user;
and displaying the lawyer recommendation list to the user based on the display mode selected by the user.
The device based on public information recommendation lawyer that this application provided adopts following technical scheme, includes:
the acquisition module is used for acquiring case description of a user;
the processing module is used for processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof;
the matching module is used for matching the case keywords and the parts of speech thereof in the acquired lawyer graph database to obtain matched lawyers and the association degree of the matched lawyers and the case description of the user;
the sorting module is used for sorting the matched lawyers based on the relevance degree to obtain a lawyer recommendation list;
a display module to display the lawyer recommendation list to the user.
Optionally, the obtained lawyer graph database includes:
the first acquisition unit is used for acquiring public information of lawyers;
the building unit is used for building an entity relationship network taking lawyers as centers on the basis of the public information of the lawyers;
and the fusion unit is used for fusing a plurality of entity relationship networks taking lawyers as centers to obtain the lawyer graph database.
Optionally, the processing module includes:
the word segmentation unit is used for carrying out word segmentation on the case description to obtain case keywords;
and the analysis unit is used for performing part-of-speech analysis on the case keywords to obtain the parts-of-speech of the case keywords.
Optionally, the processing module further includes:
the first judging unit is used for judging whether the part of speech of the case keyword is a verb or an adjective;
and the labeling unit is used for attaching a label to the case description when the part of speech of the case keyword is a verb or an adjective.
Optionally, the matching the case keywords and the parts of speech thereof in the obtained lawyer graph database to obtain the degree of association between the matched lawyers and the case description of the user includes:
matching lawyers in the acquired lawyer graph database based on the case keywords and/or the tags;
and obtaining the association degree of the matched lawyers and the case description according to the part-of-speech priority of the case keywords.
Optionally, the matching module includes:
the second acquisition unit is used for acquiring weighting coefficients corresponding to the parts of speech of different case keywords, and the weighting coefficients are in direct proportion to the priority of the parts of speech of the case keywords;
a third obtaining unit, configured to obtain all the case keywords of the matched lawyers;
and the calculating unit is used for determining weighted values of all the case keywords of the matched lawyers based on the weighted coefficients, and the weighted values are used as the association degree of the matched lawyers and the case description.
Optionally, the sorting module further includes:
the second judging unit is used for judging whether the association degree exceeds a preset threshold value or not;
and the sorting unit is used for sorting the matched lawyers with the relevance degrees exceeding a preset threshold value to obtain a lawyer recommendation list when the relevance degrees exceed the preset threshold value.
Optionally, the display module further includes:
the third acquisition unit is used for acquiring the display mode selected by the user;
and the display unit is used for displaying the lawyer recommendation list to the user based on the display mode selected by the user.
The present specification also provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
In summary, the present application has at least the following advantages:
1. according to the method, the user seeks legal help in a narrative mode, the user does not need to input filtering conditions to screen lawyers according to a traditional method, the use threshold is reduced, and the method is more convenient to use;
2. the case description is accurately matched with the lawyer graph database, and the case description can be acquired: seniority requirements for lawyers; attorneys are located that correspond to the specific company, area of expertise, and stage of case brokering mentioned in the case description that was once brokered. Moreover, the user can adjust the matching result in a mode of supplementing description or modifying description;
3. the public information is effectively integrated to generate a network effect, so that the information is split to accurately match the result.
Drawings
FIG. 1 is a schematic diagram of a method for recommending attorneys based on public information provided by an embodiment of the present description;
FIG. 2 is a schematic diagram of a structure of a lawyer graph database provided by an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a public information recommendation lawyer-based device provided by an embodiment of the present specification;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Exemplary embodiments of the present invention are described more fully below with reference to the accompanying fig. 1-5. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a method for recommending attorneys based on public information according to an embodiment of the present disclosure, where the method may include:
s101: acquiring case description of a user;
in the embodiment of the present specification, the case description is information related to legal issues, and may be case information known by the user through keyboard input, voice input, handwriting input, scan input, or any legal documents related to or having legal effectiveness, such as complaints, answers, contracts, and agreements. The format of the case description may be Word, TXT, PDF format, etc.
S102: processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof;
in the embodiment of the present specification, the natural Language processing model includes an NLP (natural Language processing) model, and the case description is subjected to operations such as chinese word segmentation, part of speech analysis, keyword extraction, semantic analysis, and the like through the NLP model.
Optionally, the processing the case description through the natural language processing model to obtain case keywords and parts of speech thereof includes:
performing word segmentation processing on the case description to obtain case keywords;
and performing part-of-speech analysis on the case keywords to obtain the parts-of-speech of the case keywords.
In the embodiment of the specification, the case description is divided into single phrases by the NLP model, and based on the background field of law, the case keywords related to the legal case are extracted from the phrases by the NLP model.
Then, the NLP model analyzes the part of speech of the case keyword, wherein the part of speech comprises noun, verb and adjective. The nouns are used to match companies, lawyers, etc., and "Shanghai XX company" is a noun. The verb is used for matching with the subsequent case execution stage, such as 'victory' and 'unreceived' are verbs, and the adjectives are used for matching with corresponding entity attributes, such as 'experienced' are adjectives.
Optionally, the processing the case description through the natural language processing model to obtain case keywords and parts of speech thereof further includes:
judging whether the part of speech of the case keyword is a verb or an adjective;
and when the part of speech of the case keyword is a verb or an adjective, attaching a label to the case description.
In this specification embodiment, when the part of speech of case keywords is verb, such as "victory complaint" and "not received", based on these case keywords, the case stage can be determined to be "case execution", and this case description is tagged for matching with the corresponding lawyer in the lawyer graph database. When the part of speech of the case keyword is an adjective, such as "experienced", the recommended lawyer's working years requirement can be determined based on the case keyword for matching the corresponding lawyer in the lawyer graph database.
S103: matching the case keywords and the parts of speech thereof in the acquired lawyer graph database to obtain matched lawyers and the association degree of the matched lawyers and the case description of the user;
in the embodiment of the specification, the lawyer graph database adopts main open source graph data of a big data NoSQL type, and supports efficient storage and retrieval of massive graph structure data.
Optionally, the obtained lawyer graph database includes:
acquiring public information of lawyers;
constructing an entity relationship network taking lawyers as the center based on the public information of the lawyers;
and fusing a plurality of entity relationship networks taking lawyers as centers to obtain the lawyer graph database.
In the embodiment of the specification, the lawyer graph database can be established through information submitted by lawyers, or can be established through information counted by people and input into the database.
If the training system is too large from beginning to end, the data of law referee documents, enterprise business information, lawyers, law firms and the like are processed by a pre-training model to obtain a law firm database. The pre-training model comprises a BERT (bidirectional Encoder reproduction from transformations) model, is an advanced NLP pre-training model, is an improvement of a transform model comprising an Encoder-Decoder structure, utilizes an Attention mechanism to realize understanding of dependency relationship between words in a text, particularly solves the problems of word ambiguity, long-range dependency and the like in the prior art, has good processing and contextualization recognition on the text of long or short frames, and particularly can exert advantages on the text processing with strong throughout dependency of a legal referee document; meanwhile, the BERT contains a large amount of general knowledge, word vectors and word vectors do not need to be trained in advance, and only a text sequence needs to be directly input into the BERT, the BERT can automatically extract rich word-level features, grammatical structure features and semantic features in the sequence, output contextualized word vectors to the BiLSTM processing long-distance entity labels, and obtain the optimal prediction sequence by the CRF according to the dependency relationship between adjacent labels, so that advantage complementation is realized. The method solves the problem of gradient explosion or gradient disappearance generated during RNN training, realizes Long-Term Memory by using a gating concept, captures sequence information in a Long text, and better adapts to the scene that the front direction and the rear direction in the context of the Chinese text depend on, such as the situation that the reason is listed after the conclusion is drawn in the declarative text. The crf (conditional Random field) is a relatively mature technology, and well captures sequence information therein based on natural sequences of characters and words in a text to realize identification and optimization of adjacent entities, for example, in a text that "shanghai bank first promotes deposit interest rate in shanghai banking industry by a large margin," shanghai bank "is extracted in front, and" shanghai "and" banking industry "are extracted in the back, which is an optimal entity identification result. The entities thus identified are the name of the business, the city of the business, the agent attorney of the original business, the agent attorney of the reported business, the law, the name of the court, the name of the judge, the case number, etc. The extraction of the relation is mainly trained by depending on a small amount of labeled data, and mainly comprises litigation forenotice, litigation waited notice, original notice agency, waited agency, court acceptance, judge by judges, employment and the like, so that the entity and the relation are extracted and then integrated, redundancy elimination and other integration processing are carried out, different strategies are adopted for different entities, for example, enterprise names are directly combined by entity names, and the enterprise legal representative names adopt a triple form of 'enterprise legal representative-main person relation-enterprise names' in consideration of the condition of duplication of names, redundancy judgment and combination are carried out, and then the combined entity and relation are stored in a lawyer database. As shown in fig. 2, the stored lawyer nodes establish a network relationship with nodes such as a law institute, a case, a company, a court, a judge, and the like, which provides a basis for subsequent accurate matching.
The core entities entering the lawyer graph database are mainly lawyers and law departments, firstly, a series of labels are marked on the lawyer entities, the core texts such as case statement in the official documents of the historical acting cases are classified mainly through a classification algorithm, such as 'contract dispute', 'construction engineering', 'marriage family', and the like, the classification algorithm also uses the BERT technology, but the specificity of the legal texts is considered, the classification algorithm comprises terms or proper nouns of specific legal scenes, so most of official document public data are required to be used for training, and the accuracy of classification prediction is guaranteed. During training, inputting all legal documents with classification labels into a neural network, comparing a predicted output result with the classification labels on an output layer after passing through a plurality of hidden layers, repeatedly adjusting parameters of the comparative loss function by adopting Cross Entropy (Cross Entropy) through an internal feedback mechanism of the neural network, finally forming a model deployment line, then performing classification prediction on a newly added legal document text, and adding a classification result to a lawyer node of a graph database in a label form. In addition, the corresponding numerical values generated by statistics of indexes such as the total number of cases of attorneys, the total number of cases and the number of winning cases in the identity of original agents, the total number of cases and the number of winning cases in the identity of reported agents and the like are added to the corresponding attributes of the attorneys and the law nodes. Together with some attributes of the lawyer and law, such as the start date of lawyer's practice and the number of the law places, the attributes constitute the overall ' image ' data of the lawyer and law places, which are stored in the database of the lawyer database.
Optionally, the matching the case keywords and the parts of speech thereof in the obtained lawyer graph database to obtain the degree of association between the matched lawyers and the case description of the user includes:
matching lawyers in the acquired lawyer graph database based on the case keywords and/or the tags;
and obtaining the association degree of the matched lawyers and the case description according to the part-of-speech priority of the case keywords.
In the embodiment of the specification, lawyers with the same hit keywords and/or tags are found by matching in lawyer graph data according to keywords and/or tags of case descriptions, and the association degree between the matched lawyers and the case descriptions is calculated and obtained based on the priority of the part of speech of the case keywords.
Optionally, the obtaining of the association degree between the matched lawyer and the case description according to the part-of-speech priority of the case keyword includes:
acquiring weighting coefficients corresponding to the parts of speech of different case keywords, wherein the weighting coefficients are in direct proportion to the priority of the parts of speech of the case keywords;
acquiring all the case keywords of the matched lawyers;
determining weighted values of all the case keywords of the matched lawyers based on the weighted coefficients, wherein the weighted values are used as the association degrees of the matched lawyers and the case description.
In the embodiment of the present specification, the priority of part of speech of case keywords is from high to low: nouns, verbs, adjectives, and weighting coefficients corresponding to the priorities of the parts of speech of case keywords are from high to low: a weight coefficient of a noun, a weight coefficient of a verb, and a weight coefficient of an adjective. And acquiring all the case keywords of the matched lawyers, and calculating the weighting coefficients of all the case keywords to obtain the weighting values of all the case keywords, wherein the weighting values are the association degrees of the matched lawyers and the case description.
S104: sorting the matched lawyers based on the relevance degree to obtain a lawyer recommendation list;
in the embodiment of the specification, the matched lawyers are sorted in the order of the relevance degree from high to low, and a lawyer recommendation list is obtained.
Optionally, the sorting the matched lawyers based on the association degree to obtain a lawyer recommendation list, further includes:
judging whether the association degree exceeds a preset threshold value or not;
and when the relevance exceeds a preset threshold, sorting the matched lawyers with the relevance exceeding the preset threshold to obtain a lawyer recommendation list.
In this embodiment of the present specification, the preset threshold may be a value obtained empirically, a value obtained through statistical analysis, or a value selected by a user; meanwhile, the preset threshold value can be manually and automatically adjusted. Judging whether the correlation degree exceeds a preset threshold value or not, and displaying matched lawyers exceeding the preset threshold value when the correlation degree exceeds the preset threshold value; and when the preset threshold value is not exceeded, the display is not carried out.
S105: presenting the lawyer recommendation list to the user.
In this specification embodiment, a lawyer recommendation list based on case description is presented to the user for further selection by the user.
Optionally, the presenting the lawyer recommendation list to the user further includes:
acquiring a display mode selected by a user;
and displaying the lawyer recommendation list to the user based on the display mode selected by the user.
In this embodiment of the present specification, the presentation manner selected by the user may be a layout manner selected by the user, or may be a presentation information category selected by the user. Such as: the user prefers to display the information of 5 lawyers on the page and selects the image-text display mode; and (4) the user focuses on the winning rate of the lawyer, and the winning rate of the lawyer is displayed in the display page.
The application has at least the following advantages:
1. according to the method, the user seeks legal help in a narrative mode, the user does not need to input filtering conditions to screen lawyers according to a traditional method, the use threshold is reduced, and the method is more convenient to use;
2. the case description is accurately matched with the lawyer graph database, and the case description can be acquired: seniority requirements for lawyers; attorneys are located that correspond to the specific company, area of expertise, and stage of case brokering mentioned in the case description that was once brokered. Moreover, the user can adjust the matching result in a mode of supplementing description or modifying description;
3. the public information is effectively integrated to generate a network effect, so that the information is split to accurately match the result.
Fig. 3 is a schematic structural diagram of an apparatus for recommending lawyers based on public information according to an embodiment of the present disclosure, where the apparatus may include:
an obtaining module 301, configured to obtain a case description of a user;
the processing module 302 is used for processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof;
a matching module 303, configured to match the case keywords and the parts of speech thereof in the obtained lawyer graph database, so as to obtain a matched lawyer and an association degree between the matched lawyer and the case description of the user;
a sorting module 304, configured to sort the matched lawyers based on the association degree to obtain a lawyer recommendation list;
a display module 305, configured to display the lawyer recommendation list to the user.
Optionally, the obtained lawyer graph database includes:
the first acquisition unit is used for acquiring public information of lawyers;
the building unit is used for building an entity relationship network taking lawyers as centers on the basis of the public information of the lawyers;
and the fusion unit is used for fusing a plurality of entity relationship networks taking lawyers as centers to obtain the lawyer graph database.
Optionally, the processing module includes:
the word segmentation unit is used for carrying out word segmentation on the case description to obtain case keywords;
and the analysis unit is used for performing part-of-speech analysis on the case keywords to obtain the parts-of-speech of the case keywords.
Optionally, the processing module further includes:
the first judging unit is used for judging whether the part of speech of the case keyword is a verb or an adjective;
and the labeling unit is used for attaching a label to the case description when the part of speech of the case keyword is a verb or an adjective.
Optionally, the matching the case keywords and the parts of speech thereof in the obtained lawyer graph database to obtain the degree of association between the matched lawyers and the case description of the user includes:
matching lawyers in the acquired lawyer graph database based on the case keywords and/or the tags;
and obtaining the association degree of the matched lawyers and the case description according to the part-of-speech priority of the case keywords.
Optionally, the matching module includes:
the second acquisition unit is used for acquiring weighting coefficients corresponding to the parts of speech of different case keywords, and the weighting coefficients are in direct proportion to the priority of the parts of speech of the case keywords;
a third obtaining unit, configured to obtain all the case keywords of the matched lawyers;
and the calculating unit is used for determining weighted values of all the case keywords of the matched lawyers based on the weighted coefficients, and the weighted values are used as the association degree of the matched lawyers and the case description.
Optionally, the sorting module further includes:
the second judging unit is used for judging whether the association degree exceeds a preset threshold value or not;
and the sorting unit is used for sorting the matched lawyers with the relevance degrees exceeding a preset threshold value to obtain a lawyer recommendation list when the relevance degrees exceed the preset threshold value.
Optionally, the display module further includes:
the third acquisition unit is used for acquiring the display mode selected by the user;
and the display unit is used for displaying the lawyer recommendation list to the user based on the display mode selected by the user.
The functions of the apparatus in the embodiment of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details that are not described in the present embodiment, and further details are not described herein.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. An electronic device 400 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 4, electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 that connects the various system components (including the memory unit 420 and the processing unit 410), a display unit 440, and the like.
Wherein the storage unit stores program code executable by the processing unit 410 to cause the processing unit 410 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 410 may perform the steps as shown in fig. 1.
The storage unit 420 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 400 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 450. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown in FIG. 4, other hardware and/or software modules may be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is 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.
Claims (11)
1. A method for recommending lawyers based on public information, comprising:
acquiring case description of a user;
processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof;
matching the case keywords and the parts of speech thereof in the acquired lawyer graph database to obtain matched lawyers and the association degree of the matched lawyers and the case description of the user;
sorting the matched lawyers based on the relevance degree to obtain a lawyer recommendation list;
presenting the lawyer recommendation list to the user.
2. The method of claim 1, wherein the obtained humorous graph database comprises:
acquiring public information of lawyers;
constructing an entity relationship network taking lawyers as the center based on the public information of the lawyers;
and fusing a plurality of entity relationship networks taking lawyers as centers to obtain the lawyer graph database.
3. The method of claim 1, wherein said processing said case description through a natural language processing model to obtain case keywords and parts of speech thereof comprises:
performing word segmentation processing on the case description to obtain case keywords;
and performing part-of-speech analysis on the case keywords to obtain the parts-of-speech of the case keywords.
4. The method of claim 3, wherein said case description is processed through a natural language processing model to obtain case keywords and parts of speech thereof, further comprising:
judging whether the part of speech of the case keyword is a verb or an adjective;
and when the part of speech of the case keyword is a verb or an adjective, attaching a label to the case description.
5. The method of claim 4, wherein said matching said case keywords and parts of speech thereof in an acquired lawyer graph database to obtain matching lawyers and their association with said user's case description comprises:
matching lawyers in the acquired lawyer graph database based on the case keywords and/or the tags;
and obtaining the association degree of the matched lawyers and the case description according to the part-of-speech priority of the case keywords.
6. The method as claimed in claim 5, wherein said obtaining the association degree of said matched attorney with said case description according to the priority of part of speech of said case keyword comprises:
acquiring weighting coefficients corresponding to the parts of speech of different case keywords, wherein the weighting coefficients are in direct proportion to the priority of the parts of speech of the case keywords;
acquiring all the case keywords of the matched lawyers;
determining weighted values of all the case keywords of the matched lawyers based on the weighted coefficients, wherein the weighted values are used as the association degrees of the matched lawyers and the case description.
7. The method of claim 1, wherein the ranking the matched attorneys based on the relevance results in an attorney recommendation list, further comprising:
judging whether the association degree exceeds a preset threshold value or not;
and when the relevance exceeds a preset threshold, sorting the matched lawyers with the relevance exceeding the preset threshold to obtain a lawyer recommendation list.
8. The method of claim 1, wherein the presenting the lawyer recommendation list to the user further comprises:
acquiring a display mode selected by a user;
and displaying the lawyer recommendation list to the user based on the display mode selected by the user.
9. An apparatus for recommending attorneys based on public information, comprising:
the acquisition module is used for acquiring case description of a user;
the processing module is used for processing the case description through a natural language processing model to obtain case keywords and parts of speech thereof;
the matching module is used for matching the case keywords and the parts of speech thereof in the acquired lawyer graph database to obtain matched lawyers and the association degree of the matched lawyers and the case description of the user;
the sorting module is used for sorting the matched lawyers based on the relevance degree to obtain a lawyer recommendation list;
a display module to display the lawyer recommendation list to the user.
10. An electronic device, wherein the electronic device comprises:
a processor;
and a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-8.
11. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-8.
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