CN111062834A

CN111062834A - Dispute case entity identification method and device, computer equipment and storage medium

Info

Publication number: CN111062834A
Application number: CN201911267517.6A
Authority: CN
Inventors: 何海龙; 李如先; 申志彬
Original assignee: Shenzhen Qianhai Huanrong Lianyi Information Technology Service Co Ltd
Current assignee: Shenzhen Qianhai Huanrong Lianyi Information Technology Service Co Ltd
Priority date: 2019-12-11
Filing date: 2019-12-11
Publication date: 2020-04-24

Abstract

The invention relates to a dispute case entity identification method, a dispute case entity identification device, computer equipment and a storage medium, wherein the method comprises the steps of obtaining financial dispute cases; acquiring related legal documents according to financial dispute cases to obtain document main bodies; carrying out feature engineering extraction on the document main body to obtain a target document; judging whether the target document is a document with a standard format; if so, matching the target document through the regular expression to obtain key elements, and sending the key elements to the terminal for statistical analysis by the terminal; if not, preprocessing the target document to obtain a word vector; inputting the word vectors into an entity recognition model for element classification to obtain key element categories; and extracting the key elements according to the key element categories and the target document, and sending the key elements to the terminal for statistical analysis by the terminal. The invention realizes automatic acquisition of key elements of financial loan disputes, has high efficiency and is convenient for the terminal to carry out statistical analysis.

Description

Dispute case entity identification method and device, computer equipment and storage medium

Technical Field

The invention relates to a case entity identification method, in particular to a dispute case entity identification method, a dispute case entity identification device, computer equipment and a storage medium.

Background

Financial disputes are often related to financial companies, and refer to disputes between financial institutions and citizens, legal persons and other organizations, and between financial institutions caused by currency fusion. In order to reduce judicial losses caused by financial disputes, financial companies often need to spend a large amount of manpower and material resources to extract key elements of financial loan dispute from existing cases, however, the existing extraction mode adopts a manual extraction mode, the efficiency is low, and the standards for extracting the key elements are not equal, so that the accuracy rate of the extracted key elements is low, and the accuracy rate of solving the financial loan dispute cases is affected.

Therefore, it is necessary to design a method for automatically acquiring key elements of financial loan dispute cases, which is efficient and convenient for the terminal to perform statistical analysis and can increase the accuracy of extracting the key elements.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a dispute case entity identification method, a dispute case entity identification device, computer equipment and a storage medium.

In order to achieve the purpose, the invention adopts the following technical scheme: the dispute case entity identification method comprises the following steps:

acquiring financial dispute cases;

acquiring related legal documents according to financial dispute cases to obtain document main bodies;

carrying out feature engineering extraction on the document main body to obtain a target document;

judging whether the target document is a document with a standard format;

if the target document is a document with a standard format, matching the target document through a regular expression to obtain key elements, and sending the key elements to a terminal for statistical analysis by the terminal;

if the target document is not a document with a standard format, preprocessing the target document to obtain a word vector;

inputting the word vectors into an entity recognition model for element classification to obtain key element categories;

extracting key elements according to the key element categories and the target document, and sending the key elements to the terminal for statistical analysis by the terminal;

the entity recognition model is obtained by training a convolutional neural network through vectors obtained by word vectorization of a plurality of text data with key element classification labels.

The further technical scheme is as follows: obtain relevant legal documents according to financial dispute case to obtain the document main part, include:

acquiring a civil case according to the financial dispute case;

filtering the content of the civil case to obtain a primary document;

and merging the first trial case and the second trial case of the primary document through the case number to obtain a document main body.

The further technical scheme is as follows: the characteristic engineering extraction of the document main body to obtain the target document comprises the following steps:

filtering stop words and punctuation marks in the document main body to obtain a filtering result;

discarding the document contents with the document content length not meeting the requirement on the filtering result to obtain an intermediate document;

and performing word segmentation processing and part-of-speech tagging on the intermediate document to obtain a target document.

The further technical scheme is as follows: the preprocessing the target document to obtain a word vector includes:

and carrying out word vectorization on the target document to obtain a word vector.

The further technical scheme is as follows: the entity recognition model is obtained by training a convolutional neural network through vectors obtained after word vectorization of a plurality of text data with key element classification labels, and comprises the following steps:

constructing a convolutional neural network and a loss function;

acquiring a plurality of text data with key element classification labels, performing word vectorization on the text data to obtain vectors with the key element classification labels, and dividing the vectors with the key element classification labels into a training set and a test set;

inputting the training set into the convolutional neural network for convolutional training to obtain a training result;

calculating the difference between the training result and the key element classification label by adopting a loss function to obtain a loss value;

judging whether the loss value is kept unchanged;

if the loss value is not maintained, adjusting parameters of a convolutional neural network, and executing the convolutional training by inputting a training set into the convolutional neural network to obtain a training result;

if the loss value is kept unchanged, inputting the test set into a convolutional neural network for element classification to obtain a test result;

judging whether the test result meets the requirement or not;

if the test result does not meet the requirement, executing the parameter of the convolutional neural network;

and if the test result meets the requirement, taking the convolutional neural network as an entity recognition model.

The further technical scheme is as follows: the training results include key element categories.

The further technical scheme is as follows: inputting the training set into the convolutional neural network for convolutional training to obtain a training result, including:

and inputting the training set into the convolutional neural network and performing convolutional training by using CRF or LSTM + CRF to obtain a training result.

The invention also provides a dispute case entity recognition device, which comprises:

the case acquisition unit is used for acquiring financial dispute cases;

the document acquiring unit is used for acquiring related legal documents according to the financial dispute cases to obtain a document main body;

the engineering extraction unit is used for carrying out characteristic engineering extraction on the document main body to obtain a target document;

a judging unit configured to judge whether the target document is a document of a standard format;

the matching unit is used for matching the target document through a regular expression to obtain key elements if the target document is a document with a standard format, and sending the key elements to the terminal for statistical analysis by the terminal;

the preprocessing unit is used for preprocessing the target document to obtain a word vector if the target document is not a document with a standard format;

the category acquisition unit is used for inputting the word vectors into the entity recognition model to perform element classification so as to obtain key element categories;

and the element extraction unit is used for extracting the key elements according to the key element categories and the target document and sending the key elements to the terminal for statistical analysis by the terminal.

The invention also provides computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes the method when executing the computer program.

The invention also provides a storage medium storing a computer program which, when executed by a processor, is operable to carry out the method as described above.

Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of filtering legal documents corresponding to cases, performing word segmentation and part-of-speech tagging, performing direct matching by adopting a regular expression if the legal documents are documents with standard formats, improving efficiency, performing word vectorization on the legal documents which are not documents with standard formats, inputting word vectors into a trained entity recognition model to classify element categories, combining original words to obtain key elements, automatically obtaining the key elements of financial loan dispute cases, and improving the accuracy of the extracted key elements.

The invention is further described below with reference to the accompanying drawings and specific embodiments.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of a dispute case entity identification method according to an embodiment of the present invention;

fig. 2 is a schematic flow chart of a dispute case entity identification method according to an embodiment of the present invention;

fig. 3 is a schematic sub-flow diagram of a dispute case entity identification method according to an embodiment of the present invention;

fig. 4 is a schematic sub-flow diagram of a dispute case entity identification method according to an embodiment of the present invention;

fig. 5 is a schematic sub-flow diagram of a dispute case entity identification method according to an embodiment of the present invention;

fig. 6 is a schematic block diagram of a dispute case entity identification apparatus according to an embodiment of the present invention;

fig. 7 is a schematic block diagram of a document acquiring unit of the dispute case entity recognition apparatus according to the embodiment of the present invention;

fig. 8 is a schematic block diagram of an engineering extraction unit of the dispute case entity identification apparatus according to the embodiment of the present invention;

FIG. 9 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of a dispute case entity identification method according to an embodiment of the present invention. Fig. 2 is a schematic flowchart of a dispute case entity identification method according to an embodiment of the present invention. The dispute case entity identification method is applied to a server. The server performs data interaction with the terminal, acquires a financial dispute case needing entity identification from the terminal, acquires a corresponding legal document, namely a standard document, from the financial dispute case, performs a series of processing on the document to obtain a document consisting of words with parts of speech, performs element matching directly on the standard document, judges the category of the words by adopting a model for the irregular document, and determines elements by the category and the document consisting of the words with parts of speech.

Fig. 2 is a schematic flow chart of a dispute case entity identification method according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S180.

And S110, acquiring financial dispute cases.

In this embodiment, the financial dispute case refers to the case information of financial loan dispute inputted by the terminal, and generally, the case information is brief case information, and includes information related to the related person and the event.

And S120, acquiring related legal documents according to the financial dispute cases to obtain document main bodies.

In this embodiment, the document body is a legal document formed by combining the first and second cases in the legal document after content filtering.

In an embodiment, referring to fig. 3, the step S120 may include steps S121 to S123.

And S121, acquiring a civil case according to the financial dispute case.

In this embodiment, a civil case refers to a corresponding case obtained from a judge paperwork according to a financial dispute case; there may be one or more, and therefore, a second filtration is required.

And S122, filtering the content of the civil case to obtain a preliminary document.

In this embodiment, the preliminary documents refer to corresponding financial loan-related civil cases formed after preliminary filtering, such as keyword filtering.

Specifically, the primary contents of the civil case are filtered through keywords including but not limited to finance, lending, borrowing and the like, so that a primary document is obtained, and the accuracy of entity identification is improved.

And S123, merging the first trial case and the second trial case of the primary document through the case number to obtain a document main body.

In this embodiment, the document body refers to a legal document formed by combining the information related to the first trial case and the information related to the second trial case.

The primary document combines the cases which do not obey to relive the upper appeal again and the cases which are related to the primary document through the case number of the primary document, the combining process is to extract the judgment result of the secondary audit related to the primary document, to the rejected retained documents, the documents which are re-audited are abandoned, the rest documents take the secondary audit result as the final result, and the document main bodies which are audited twice are combined into the final document main body, so that the accuracy and the integrity of the documents are ensured, and the accuracy of entity identification is increased.

And S130, extracting the characteristic engineering of the document main body to obtain the target document.

In this embodiment, the target document refers to a legal document formed by screening content meeting the requirement, and performing word segmentation processing and part-of-speech tagging on the content.

In an embodiment, referring to fig. 4, the step S130 may include steps S131 to S133.

S131, filtering stop words and punctuation marks in the document main body to obtain a filtering result.

In this embodiment, the filtering result refers to the document after the stop words and punctuation marks in the document main body have been filtered.

Specifically, word judgment is carried out on the document main body according to a preset stop word bank, and once the same words appear, filtering is carried out to complete stop word filtering of the document main body.

Punctuation is filtered through string.division, interference on word segmentation processing and part-of-speech tagging is reduced, and workload of subsequent word segmentation processing and part-of-speech tagging can be reduced.

And S132, discarding the document contents with the document content length not meeting the requirement on the filtering result to obtain an intermediate document.

In this embodiment, the intermediate document refers to a content whose character length is greater than a preset value, such as a character length of a word, for example, the character length is less than a certain threshold, for example, 150 words, and the document content whose length does not meet the requirement is filtered and discarded, so as to ensure that the remaining document content is the content that can be subjected to word segmentation, and reduce subsequent workload, so as to improve recognition efficiency.

And S133, performing word segmentation processing and part-of-speech tagging on the intermediate document to obtain a target document.

Specifically, the word segmentation processing may be performed by methods such as, but not limited to, python, LSTM, CRF, and HMM, and the part-of-speech tagging may be performed by methods such as, but not limited to, python and NLP.

S140, judging whether the target document is a document with a standard format.

In the present embodiment, the legal document has a certain format specification, and the format of each item of content including the arrangement and order of titles is specified by the same standard, and therefore, it is possible to determine whether the target document is a document of the format specification according to the standard.

And S150, if the target document is a document with a standard format, matching the target document through a regular expression to obtain key elements, and sending the key elements to the terminal for statistics and analysis by the terminal.

In this embodiment, the key elements include the key content of the financial dispute case, such as 1. main body of loan; 2. a loan body; 3. the term of borrowing; 4. repayment time; 5. expiration time; 6. the amount of the debit; 7. contract and whether it is valid; 8. presence or absence of executable property; 9. the age of the borrower; 10. borrowing purposes; 11. running a bank; 12. interest borrowing; 13. payment records, etc.

Specifically, when the target document is a document with a standard format, a uniform regular expression may be used for matching, such as an original? The company | being advertised \ s [ \ u4e00- \ u9fa5] {1,3} | in? Amount of borrowing? The money is overdue, and then key elements are obtained through direct matching.

And S160, if the target document is not a document with a standard format, preprocessing the target document to obtain a word vector.

In this embodiment, the word vector refers to a corresponding vector obtained by performing word vectorization on the word segments in the target document.

Specifically, word vectorization is performed on the target document to obtain a word vector. Word vectorization may be performed using, but not limited to tf-idf, one-hot, word2 vec.

The word segmentation, the part-of-speech tagging and the word vectorization can better analyze the content corresponding to the word subjected to the part-of-speech tagging in the target document, such as which words correspond to the main body of borrowing of key elements.

And S170, inputting the word vectors into the entity recognition model for element classification to obtain key element categories.

In this embodiment, the key element category refers to which category the word vector corresponds to, and for example, the category belongs to a borrower or a loan body.

In one embodiment, referring to fig. 5, the step S170 may include steps S171 to S179.

S171, constructing a convolutional neural network and a loss function.

In this embodiment, the convolutional neural network is a deep learning model, having a network of input layers, convolutional layers, and output layers. The Loss function may be a Center Loss function.

S172, obtaining a plurality of text data with key element classification labels, performing word vectorization on the text data to obtain vectors with the key element classification labels, and dividing the vectors with the key element classification labels into a training set and a test set.

In this embodiment, the training set refers to data used for training the model, and the test set refers to data used for testing the trained model.

The text data is formed by performing the content filtering, word segmentation and part-of-speech tagging on documents of the judge document network as original data, performing corresponding key element classification label tagging on the formed documents to serve as reference data, and using vectors formed by performing word vectorization on the reference data as input.

Manually labeling documents of a referee document network after the segmentation, marking key elements to be extracted as labels of corresponding classification, such as borrowers and lenders, as P, marking B as a start according to law, I as a middle, E as an end, and the rest as O; the entity recognition of the irregular document is subjected to vectorization by using words or words plus parts of speech after being marked as input, and the marked result is used as output to train the network.

And S173, inputting the training set into the convolutional neural network for convolutional training to obtain a training result.

Specifically, the convolutional training is carried out in the product neural network by using CRF or LSTM + CRF so as to obtain a training result.

In this embodiment, the training result refers to that the training set sequentially inputs to the convolutional neural network and then outputs the class label corresponding to the training set, that is, the probability of the key element class, and the comparison is performed with a preset threshold, when the probability of the key element class exceeds the preset threshold, the class label is output as the key element class, otherwise, the class label is output as not the key element class.

And S174, calculating the difference between the training result and the key element classification label by adopting a loss function to obtain a loss value.

In this embodiment, the loss value refers to a difference between the training result and the corresponding class label calculated by using the loss function.

And S175, judging whether the loss value is kept unchanged.

In this embodiment, when the loss value remains unchanged, that is, the current convolutional neural network has converged, that is, the loss value is substantially unchanged and very small, it also indicates that the current convolutional neural network can be used as an entity recognition model, generally, the loss value is relatively large when training is started, and the loss value is smaller after training, and if the loss value does not remain unchanged, it indicates that the current convolutional neural network cannot be used as an entity recognition model, that is, the estimated category is not accurate, which may result in inaccurate correlation processing of later key elements.

And S176, if the loss value is not maintained, adjusting parameters of the convolutional neural network, and executing the convolutional training by inputting the training set into the convolutional neural network to obtain a training result.

In this embodiment, adjusting the parameter of the convolutional neural network refers to adjusting the weight value of each layer in the convolutional neural network. Through continuous training, a convolutional neural network meeting the requirements can be obtained.

And S177, if the loss value is kept unchanged, inputting the test set into the convolutional neural network for element classification to obtain a test result.

In this embodiment, the test result refers to that after the element classification is performed on the test set, the corresponding element class can be obtained.

S178, judging whether the test result meets the requirement;

if the test result does not meet the requirement, executing the step S176;

and S179, if the test result meets the requirement, taking the convolutional neural network as an entity recognition model.

When the two indexes of the precision and the recall rate of the test result are evaluated to be in accordance with the conditions, the fitting degree is indicated to be in accordance with the requirements, and the test result can be considered to be in accordance with the requirements; otherwise, the test result is considered to be not qualified. And stopping training when the convolutional neural network converges. And testing the convolutional neural network after the convolutional neural network is trained, and if the test result is not good, adjusting a training strategy to train the convolutional neural network again. Certainly, in the training process, training and testing are carried out, and the testing is carried out in order to check the training condition in real time; and after the test of the training convolutional neural network is finished, the execution accuracy of the whole convolutional neural network is evaluated by using two indexes of precision and recall rate.

The key elements are changed due to the change of the labeled labels, so that different requirements of business personnel can be flexibly met.

And S180, extracting key elements according to the key element categories and the target document, and sending the key elements to the terminal for statistical analysis by the terminal.

The participles in the target document are all used for determining the key requirement category, namely the content of the target document already determines the category of the key element, and the key element category and the target document can determine the key element.

For example: the following documents are the specific contents of the documents after feature engineering processing, and several key elements of borrowers, interest and contracts need to be extracted from the following documents.

The original report Zhejiang village contract law after receiving the Zhu-third financial borrowing contract dispute one case from the institute in 2011 at 1, 24 days is to say that the Zhejiang village contract law is to say that the Zhu-first is to loan 5 ten thousand yuan to Chun east lake treatment at 2009 at 8, 3 days, 7 months at 20 days, the Anmo interest rate is 7.965 per thousand, and the Zhu-third in the Japanese is to bear the contract law and is to be reported not to pay principal and interest according to the contract agreement after the certain Zhu-third in the Japanese is to bear the contract so that the original report proposes the appeal to the institute: 1, the fact that the first defended original debt is not returned to the original report, namely the principal 5 ten thousand yuan and the fact that the third defended original report is not answered to the original report, and the fact that the third defended original report is not returned to the original report, the fact that the first defended original report has not been returned to the original report, and the fact that the third defended original report has not been answered to the original report, namely the fact that the first defended original report has not been returned to the original report, and the fact that the first defended original report has not been returned to the original report, namely the principal 5 million yuan, the interest 5992.69 yuan 2, the fact that a certain Zhu-propane of the defended original report has not been returned to the home.

After word segmentation labeling, the contents of the first two columns are formed, and then the third column is manually labeled to form the following format data: jinhua City ns B-Ni; gold east ns I-Ni; i E-Ni of people's court; civil affairs b O; decision n O; jindongsheng n O; initial letters n O; 61 st 61m O; number q O; a grandfather n O; zhejiang Ns S-Ns; village closing row n O; and p O; an announcement n O; cinnabar Nh S-Nh; n O, prescription B; a certain r O; cinnamyl S-Nh; finance n O; a debit v O; a contract n O; disputes n O; m O, a first film; case n O; the home r O; at p O; nt O in 2011; 1 month nt O; nt O for 24 days; accepting v O; and then nd O; a grandfather n O; zhejiang Ns S-Ns; village closing row n O; claim v O; an announcement n O; cinnabar Nh S-Nh; at p O; nt O in 2009; nt O at 8 months; 3m O.

For the above, reference may be made to the following explanations: village lines n O, where village lines are entities, n is part of speech, and O is a label.

The result of selecting the first word Jinhua City for word vector is as follows: [ -0.1245572-0.14788760.15683092.. 0.07756944-0.07562571-0.05162033 ]; the word vector is used as input, after passing through an entity recognition model, the probability value of each corresponding category label can be obtained, the category with the maximum probability value, such as O category or B category, is obtained, and after being used as output, the word vector is converted into the one-hot code by combining with the initial label as follows: the method comprises the following steps of { 'O':0, 'B-Ni':1, 'I-Ni':2, 'S-Ns':3, 'S-Nh':4, 'S-Ns':5}, and knowing which key element the word vector belongs to, thereby realizing the identification of the key elements.

According to the dispute case entity identification method, after the legal documents corresponding to the cases are filtered, word segmentation and part-of-speech tagging are carried out, the legal documents are documents with standard formats, and then direct matching is carried out by adopting a regular expression, so that the efficiency is improved, when the legal documents are not documents with standard formats, word vectorization is carried out firstly, the word vectors are input into the trained entity identification model to classify the element categories, the key elements can be known by combining the original words, the key elements of financial loan dispute cases can be automatically obtained, the efficiency is high, the statistical analysis can be conveniently carried out on the terminal, and the accuracy of the extracted key elements can be increased.

Fig. 6 is a schematic block diagram of a dispute case entity identification apparatus 300 according to an embodiment of the present invention. As shown in fig. 6, the present invention further provides a dispute case entity recognition device 300 corresponding to the dispute case entity recognition method. The dispute case entity recognition apparatus 300 includes a unit for performing the dispute case entity recognition method described above, and the apparatus may be configured in a server. Specifically, referring to fig. 6, the dispute case entity recognition apparatus 300 includes a case obtaining unit 301, a document obtaining unit 302, a project extracting unit 303, a determining unit 304, a matching unit 305, a preprocessing unit 306, a category obtaining unit 307, and an element extracting unit 308.

A case obtaining unit 301, configured to obtain a financial dispute case; the document acquiring unit 302 is used for acquiring related legal documents according to the financial dispute case to obtain a document main body; the project extraction unit 303 is used for performing feature project extraction on the document main body to obtain a target document; a judging unit 304 for judging whether the target document is a document of a format specification; a matching unit 305, configured to, if the target document is a document with a standard format, match the target document through a regular expression to obtain a key element, and send the key element to a terminal for statistical analysis by the terminal; a preprocessing unit 306, configured to, if the target document is not a document with a standard format, perform preprocessing on the target document to obtain a word vector; a category obtaining unit 307, configured to input the word vector into the entity identification model to perform element classification, so as to obtain a key element category; and the element extraction unit 308 is configured to extract key elements according to the key element categories and the target document, and send the key elements to the terminal for statistical analysis by the terminal.

In one embodiment, as shown in fig. 7, the document acquiring unit 302 includes a civil case acquiring subunit 3021, a content filtering subunit 3022, and a merging subunit 3023.

A case acquisition subunit 3021, configured to acquire a case according to the financial dispute case;

a content filtering subunit 3022, configured to perform content filtering on the civil case to obtain a preliminary document;

and a merging subunit 3023, configured to merge the first trial case and the second trial case with the case number of the preliminary document to obtain a document main body.

In one embodiment, as shown in fig. 8, the engineering extraction unit 303 includes a filtering subunit 3031, a discarding subunit 3032, and a labeling subunit 3033.

A filtering subunit 3031, configured to filter stop words and punctuation marks in the document main body to obtain a filtering result; a discarding subunit 3032, configured to discard, to the filtering result, document contents whose document content length does not meet the requirement, to obtain an intermediate document; and a labeling subunit 3033, configured to perform word segmentation processing and part-of-speech labeling on the intermediate document to obtain a target document.

In an embodiment, the entity recognition system further includes a model forming unit, where the model forming unit is configured to train a convolutional neural network through vectors obtained by word vectorization of a plurality of text data with key element classification labels, so as to obtain an entity recognition model.

In an embodiment, the model forming unit includes a constructing subunit, a data obtaining subunit, a training subunit, a loss calculating subunit, a loss value judging subunit, an adjusting subunit, a testing subunit, and a testing result judging subunit.

The building subunit is used for building a convolutional neural network and a loss function; the data acquisition subunit is used for acquiring a plurality of text data with key element classification labels, performing word vectorization on the text data to obtain vectors with the key element classification labels, and dividing the vectors with the key element classification labels into a training set and a test set; the training subunit is used for inputting the training set into the convolutional neural network for convolutional training to obtain a training result; the loss calculating subunit is used for calculating the difference between the training result and the key element classification label by adopting a loss function so as to obtain a loss value; a loss value judging subunit, configured to judge whether the loss value remains unchanged; the adjusting subunit is configured to adjust a parameter of the convolutional neural network if the loss value is not maintained, and perform convolutional training by inputting the training set to the convolutional neural network to obtain a training result; the test subunit is used for inputting the test set into the convolutional neural network for element classification if the loss value is kept unchanged so as to obtain a test result;

the test result judging subunit is used for judging whether the test result meets the requirement or not; if the test result does not meet the requirement, executing the parameter of the convolutional neural network; and if the test result meets the requirement, taking the convolutional neural network as an entity recognition model.

It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation processes of the dispute case entity identification apparatus 300 and each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.

The dispute case entity recognition apparatus 300 may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 9.

Referring to fig. 9, fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server.

Referring to fig. 9, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer programs 5032 include program instructions that, when executed, cause the processor 502 to perform a dispute case entity identification method.

The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be caused to execute a dispute case entity identification method.

The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 9 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps:

acquiring financial dispute cases; acquiring related legal documents according to financial dispute cases to obtain document main bodies; carrying out feature engineering extraction on the document main body to obtain a target document; judging whether the target document is a document with a standard format; if the target document is a document with a standard format, matching the target document through a regular expression to obtain key elements, and sending the key elements to a terminal for statistical analysis by the terminal; if the target document is not a document with a standard format, preprocessing the target document to obtain a word vector; inputting the word vectors into an entity recognition model for element classification to obtain key element categories; and extracting the key elements according to the key element categories and the target document, and sending the key elements to the terminal for statistical analysis by the terminal.

In an embodiment, when the processor 502 implements the step of obtaining the relevant legal document according to the financial dispute case to obtain the document main body, the following steps are specifically implemented:

acquiring a civil case according to the financial dispute case; filtering the content of the civil case to obtain a primary document; and merging the first trial case and the second trial case of the primary document through the case number to obtain a document main body.

In an embodiment, when the processor 502 performs the feature engineering extraction on the document main body to obtain the target document, the following steps are specifically implemented:

filtering stop words and punctuation marks in the document main body to obtain a filtering result; discarding the document contents with the document content length not meeting the requirement on the filtering result to obtain an intermediate document; and performing word segmentation processing and part-of-speech tagging on the intermediate document to obtain a target document.

In an embodiment, when the processor 502 implements the step of preprocessing the target document to obtain the word vector, the following steps are specifically implemented:

In an embodiment, when the processor 502 implements the step that the entity recognition model is obtained by training the convolutional neural network using a vector obtained by word vectorizing a plurality of text data with key element classification labels, the following steps are specifically implemented:

constructing a convolutional neural network and a loss function; acquiring a plurality of text data with key element classification labels, performing word vectorization on the text data to obtain vectors with the key element classification labels, and dividing the vectors with the key element classification labels into a training set and a test set; inputting the training set into the convolutional neural network for convolutional training to obtain a training result; calculating the difference between the training result and the key element classification label by adopting a loss function to obtain a loss value; judging whether the loss value is kept unchanged; if the loss value is not maintained, adjusting parameters of a convolutional neural network, and executing the convolutional training by inputting a training set into the convolutional neural network to obtain a training result; if the loss value is kept unchanged, inputting the test set into a convolutional neural network for element classification to obtain a test result; judging whether the test result meets the requirement or not; if the test result does not meet the requirement, executing the parameter of the convolutional neural network; and if the test result meets the requirement, taking the convolutional neural network as an entity recognition model.

Wherein the training result comprises a key element category.

In an embodiment, when the step of inputting the training set into the convolutional neural network for convolutional training to obtain a training result is implemented by the processor 502, the following steps are specifically implemented:

It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 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, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.

Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the steps of:

In an embodiment, when the processor executes the computer program to obtain the legal document according to the financial dispute case to obtain the document main body, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to perform the feature engineering extraction on the document main body to obtain the target document, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step of preprocessing the target document to obtain the word vector, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step that the entity recognition model is obtained by training a convolutional neural network with a vector obtained by word vectorizing a plurality of text data with key element classification labels, the following steps are specifically implemented:

Wherein the training result comprises a key element category.

In an embodiment, when the processor executes the computer program to implement the step of inputting the training set into the convolutional neural network for convolutional training to obtain a training result, the following steps are specifically implemented:

The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.

The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. The dispute case entity identification method is characterized by comprising the following steps:

acquiring financial dispute cases;

judging whether the target document is a document with a standard format;

2. The method for identifying a dispute case entity according to claim 1, wherein the obtaining of the relevant legal documents according to the financial dispute case to obtain the document body comprises:

acquiring a civil case according to the financial dispute case;

filtering the content of the civil case to obtain a primary document;

3. The dispute case entity identification method according to claim 1, wherein the performing feature engineering extraction on the document main body to obtain the target document comprises:

4. The method for identifying a dispute case entity according to claim 1, wherein the preprocessing the target document to obtain a word vector comprises:

5. The dispute case entity recognition method according to claim 1, wherein the entity recognition model is obtained by training a convolutional neural network through vectors obtained by word vectorization of a plurality of text data with key element classification labels, and comprises:

constructing a convolutional neural network and a loss function;

judging whether the loss value is kept unchanged;

judging whether the test result meets the requirement or not;

6. The method of claim 5, wherein the training results include key element categories.

7. The dispute case entity identification method according to claim 5, wherein the inputting the training set into the convolutional neural network for convolutional training to obtain a training result comprises:

8. Dispute case entity recognition device, its characterized in that includes:

the case acquisition unit is used for acquiring financial dispute cases;

9. A computer device, characterized in that the computer device comprises a memory, on which a computer program is stored, and a processor, which when executing the computer program implements the method according to any of claims 1 to 7.

10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.