CN109376964A - A kind of criminal case charge prediction technique based on Memory Neural Networks - Google Patents

A kind of criminal case charge prediction technique based on Memory Neural Networks Download PDF

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CN109376964A
CN109376964A CN201811505665.2A CN201811505665A CN109376964A CN 109376964 A CN109376964 A CN 109376964A CN 201811505665 A CN201811505665 A CN 201811505665A CN 109376964 A CN109376964 A CN 109376964A
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charge
neural networks
memory neural
key
criminal case
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CN109376964B (en
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王世晞
张亮
徐建忠
李娇娇
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Hangzhou Shiping Information & Technology Co Ltd
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Hangzhou Shiping Information & Technology Co Ltd
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Abstract

The criminal case charge prediction technique based on Memory Neural Networks that the invention discloses a kind of, comprising the following steps: Step 1: building training dataset, obtains merit description and charge as training data；Step 2: constructive memory neural network model and being trained by training data；The key-value stored in Memory Neural Networks model is right to i.e. " merit Expressive Features vector "-" charge coding "；Step 3: being judged by Memory Neural Networks model after training to criminal case charge.Model of the invention also can be carried out good prediction to low frequency charge, and the present invention provides a set of charge prediction frameworks end to end, and strong reference can be provided to judge, improve judicial automation and intelligence degree.

Description

A kind of criminal case charge prediction technique based on Memory Neural Networks
Technical field
The present invention relates to judicial intelligent fields, and in particular to a kind of criminal case charge based on Memory Neural Networks is pre- Survey method.
Background technique
Currently, the charge prediction of criminal case is generally seen as a text classification problem: merit description is as to be sorted Text, and then its corresponding charge trains a SVM or neural network model to be divided as corresponding tag along sort Class.
Training dataset of the existing general Text Classification System dependent on sufficient training data and categorical measure balance, However, there are natural unbalanced phenomenas for the distribution of different classes of case, for example " theft " crime might have in the database Thousands of examples, and " crime of concealing deposits abroad " may only have several in the database, and its frequency distribution is a long-tail Distribution, i.e. high frequency charge is actually less, and most of charge is that frequency is relatively low.Categorical data amount is very few to will cause mould Type study it is insufficient, and data distribution imbalance then makes model tend to the classification more than forecast sample quantity, the two are former Because making the file classification method of current main-stream that can only obtain considerable effect in a small amount of high frequency charge in such a scenario.
Summary of the invention
It is an object of the invention to be directed to above-mentioned the problems of the prior art, a kind of punishment based on Memory Neural Networks is provided Thing case charge prediction technique is also able to carry out good prediction to low frequency charge, promotes judicial automation and intelligence degree.
To achieve the goals above, the technical solution adopted by the present invention the following steps are included:
Step 1: building training dataset, obtains merit description and charge as training data；
Step 2: constructive memory neural network model and being trained by training data；
The key-value stored in Memory Neural Networks model is right to i.e. " merit Expressive Features vector "-" charge coding "；
Step 3: being judged by Memory Neural Networks model after training to criminal case charge.
The step 1 crawls criminal written verdict from Chinese judgement document net, obtains merit description and charge as instruction Practice data.
The specific steps of constructive memory neural network model include: in the step 2
Step 2-1) merit is described to segment, and it is mapped as term vector sequence；
Step 2-2) do one-dimensional convolutional neural networks in term vector sequence and extract feature, obtain the feature of merit description to Measure q；
Step 2-3) by feature vector q input key-value memory module progress K- neighborhood matching, calculate separately q and each key Cosine similarity, select the maximum key of K cosine similarity simultaneously to take its encoded radio, select K be worth in the most value of quantity；
Step 2-4) by obtained encoded radio be converted to its correspond to charge.
The step 2-1) in participle using Tsinghua University Open-Source Tools thulac, mapping follows directly after model instruction Practice.
When being trained in the step 2 by training data, for a training sample, if it predicts charge and mark Quasi- charge is inconsistent, then using its feature vector as key, charge coding as value insertion key-value memory module；If predicting charge It is consistent with standard charge, then it is sought averagely by its feature vector and with its most like corresponding key of identical charge, and turn back to original Position.
When being trained in the step 2 by training data, when being inserted into a new feature vector, but remember When module has taken, selection abandons the position not updated at most in memory module, replaces into new key-value pair.
Compared with prior art, the technical solution adopted by the present invention are as follows: using the description of the merit of standard and its charge as instructing Practice data and build training dataset, the Memory Neural Networks model constructed is trained by training dataset, " merit description Feature vector "-" charge coding " is to the key-value pair stored in Memory Neural Networks model is converted to, using multi-layer perception (MLP) point Class device judges criminal case charge, and model of the invention also can be carried out good prediction to low frequency charge, provides a set of Charge prediction framework end to end can provide strong reference to judge, improve judicial automation and intelligence degree.
Detailed description of the invention
Fig. 1 criminal case charge prediction technique flow diagram of the present invention.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings.
Referring to Fig. 1, the present invention is based on the criminal case charge prediction technique of Memory Neural Networks the following steps are included:
Step 1) constructs training dataset；
Criminal written verdict is crawled from Chinese judgement document net, obtains merit description and charge as training data；If Merit, which describes corresponding charge, to be had multinomial, takes its main charge, each charge is mapped as a unique integer as it later Coding.
Step 2) constructs and training Memory Neural Networks model；
A. building neural network model specifically includes:
Step 2-1) merit is described to segment, and it is mapped as term vector；Participle is using Tsinghua University's Open-Source Tools Thulac, since term vector is related with field in the task, without using the term vector of pre-training, which follows directly after model instruction Practice.
Step 2-2) one-dimensional convolutional neural networks extraction feature is done in the term vector sequence that step 2-1) is obtained, it must appear in court The feature vector q of feelings description；Other feature extraction networks can also be used, for example extract temporal aspect using LSTM.But at this In task, compared to other network structures, such as Recognition with Recurrent Neural Network, convolutional neural networks are extracting feature more directly effectively.
Step 2-3) the feature vector q that step 2-2) is obtained enter key-value memory module carry out K- neighborhood matching, matching Method is that q and each key calculate cosine similarity respectively, selects this maximum key of K cosine similarity, obtains its value (herein i.e. For the coding of charge classification), select the value that quantity is most in this K value；
The key-value stored in memory module is right to actually " merit Expressive Features vector "-" charge coding ".
Step 2-4) its correspondence charge will be converted in encoded radio obtained in step 2-3).
B. the memory module training step of neural network includes:
Step 3-1) to a training sample, if it predicts that charge and standard charge are inconsistent, its feature vector is made For key, charge coding is as value insertion key-value memory module；If predicting, charge is consistent with standard charge, by its feature vector It is averaged with its most like corresponding key of identical charge, and turns back to original position；
Step 3-2) in step 3-1), when being inserted into a new feature vector, and when memory module has taken, selection The position not updated at most in memory module is abandoned, is replaced into new key-value pair.

Claims (7)

1. a kind of criminal case charge prediction technique based on Memory Neural Networks, which comprises the following steps:
Step 1: building training dataset, obtains merit description and charge as training data；
Step 2: constructive memory neural network model and being trained by training data；
The key-value stored in Memory Neural Networks model is right to i.e. " merit Expressive Features vector "-" charge coding "；
Step 3: being judged by Memory Neural Networks model after training to criminal case charge.
2. the criminal case charge prediction technique based on Memory Neural Networks according to claim 1, it is characterised in that: described The step of one crawl criminal written verdict from Chinese judgement document net, obtain merit description and charge as training data.
3. the criminal case charge prediction technique based on Memory Neural Networks according to claim 1, it is characterised in that: if case Feelings, which describe corresponding charge, to be had multinomial, takes its main charge, and each charge is mapped as a unique integer as its volume later Code.
4. the criminal case charge prediction technique based on Memory Neural Networks according to claim 1, which is characterized in that described The specific steps of constructive memory neural network model include: in step 2
Step 2-1) merit is described to segment, and it is mapped as term vector sequence；
Step 2-2) one-dimensional convolutional neural networks extraction feature is done in term vector sequence, obtain the feature vector q of merit description；
Step 2-3) feature vector q input key-value memory module is subjected to K- neighborhood matching, calculate separately the remaining of q and each key String similarity selects the maximum key of K cosine similarity and takes its encoded radio, selects the value that quantity is most in K value；
Step 2-4) by obtained encoded radio be converted to its correspond to charge.
5. the criminal case charge prediction technique based on Memory Neural Networks according to claim 4, it is characterised in that: described Step 2-1) in participle using Tsinghua University Open-Source Tools thulac, mapping follows directly after model training.
6. the criminal case charge prediction technique based on Memory Neural Networks according to claim 1, which is characterized in that described When being trained in step 2 by training data, for a training sample, if it predicts that charge and standard charge are inconsistent, Then using its feature vector as key, charge coding as value insertion key-value memory module；If predicting charge and standard charge one It causes, is then sought averagely by its feature vector and with its most like corresponding key of identical charge, and turn back to original position.
7. the criminal case charge prediction technique based on Memory Neural Networks according to claim 1, which is characterized in that described When being trained in step 2 by training data, when being inserted into a new feature vector, but when memory module has taken, Selection abandons the position not updated at most in memory module, replaces into new key-value pair.
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CN111260114A (en) * 2020-01-08 2020-06-09 昆明理工大学 Low-frequency confusable criminal name prediction method for integrating case auxiliary sentence
CN112396201A (en) * 2019-07-30 2021-02-23 北京国双科技有限公司 Criminal name prediction method and system
CN113515631A (en) * 2021-06-18 2021-10-19 深圳大学 Method, device, terminal equipment and storage medium for predicting criminal name

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112396201A (en) * 2019-07-30 2021-02-23 北京国双科技有限公司 Criminal name prediction method and system
CN111260114A (en) * 2020-01-08 2020-06-09 昆明理工大学 Low-frequency confusable criminal name prediction method for integrating case auxiliary sentence
CN111260114B (en) * 2020-01-08 2022-06-17 昆明理工大学 Low-frequency confusable criminal name prediction method for integrating case auxiliary sentence
CN113515631A (en) * 2021-06-18 2021-10-19 深圳大学 Method, device, terminal equipment and storage medium for predicting criminal name
CN113515631B (en) * 2021-06-18 2024-05-17 深圳大学 Method, device, terminal equipment and storage medium for predicting crime name

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