WO2023134084A1 - Procédé et appareil d'identification multi-étiquettes, dispositif électronique, et support d'enregistrement - Google Patents

Procédé et appareil d'identification multi-étiquettes, dispositif électronique, et support d'enregistrement Download PDF

Info

Publication number
WO2023134084A1
WO2023134084A1 PCT/CN2022/090726 CN2022090726W WO2023134084A1 WO 2023134084 A1 WO2023134084 A1 WO 2023134084A1 CN 2022090726 W CN2022090726 W CN 2022090726W WO 2023134084 A1 WO2023134084 A1 WO 2023134084A1
Authority
WO
WIPO (PCT)
Prior art keywords
vector
label
comment
user
matrix
Prior art date
Application number
PCT/CN2022/090726
Other languages
English (en)
Chinese (zh)
Inventor
舒畅
陈又新
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2023134084A1 publication Critical patent/WO2023134084A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present application relates to the technical field of artificial intelligence, and in particular to a multi-tag identification method, device, electronic equipment and storage medium.
  • the main purpose of the embodiment of the present application is to provide a multi-tag identification method, device, electronic device and storage medium, aiming at improving the recognition accuracy and recognition efficiency of user portrait tags.
  • the embodiment of the present application proposes a multi-label identification method, the method comprising:
  • the raw data includes user basic data, user behavior data and user comment data
  • the comment text word segment vector is input into the pre-trained comparative learning model, so that the comment text word segment vector and the reference word embedding matrix in the comparative learning model are matrix multiplied to obtain the comment word embedding vector;
  • the target portrait label is obtained.
  • the embodiment of the present application proposes a multi-tag identification device, which includes:
  • a data acquisition module configured to acquire raw data, wherein the raw data includes user basic data, user behavior data and user comment data;
  • a normalization module configured to perform normalization processing on the user basic data to obtain user basic features
  • Feature extraction module for carrying out feature extraction to described user behavior data by pre-trained graph convolution model, obtains behavior feature matrix
  • a word segmentation module configured to perform word segmentation processing on the user comment data to obtain a comment text word segment vector
  • a comparative learning module for inputting the comment text word segment vector into a pre-trained comparative learning model, so that the comment text word segment vector and the reference word embedding matrix in the comparative learning model are matrix multiplied, Get the comment embedding vector;
  • the fusion module is used to perform fusion processing on the user basic features, the behavior feature matrix and the comment word embedding vector to obtain a standard portrait feature vector;
  • a tag recognition module configured to perform tag recognition processing on the standard portrait feature vector through a pre-trained tag recognition model to obtain the probability value of each preset portrait tag;
  • the comparison module is used to obtain the target portrait label according to the magnitude relationship between the probability value and the preset probability threshold.
  • the embodiment of the present application provides an electronic device, the electronic device includes a memory, a processor, a program stored in the memory and operable on the processor, and a program for implementing the processor
  • a data bus connecting and communicating with the memory when the program is executed by the processor, a multi-label identification method is implemented, wherein the multi-label identification method includes: obtaining original data, wherein the original The data includes user basic data, user behavior data, and user comment data; the user basic data is normalized to obtain user basic features; the user behavior data is extracted through a pre-trained graph convolution model to obtain Behavior feature matrix; word segmentation processing is carried out to described user comment data, obtain comment text phrase vector; Described comment text phrase vector is input in the comparative learning model of pre-training, so that described comment text phrase vector and all The reference word embedding matrix in the comparative learning model is multiplied by matrix to obtain the comment word embedding vector; the user basic feature, the behavior feature matrix and the comment word embedding vector are fused to obtain a standard
  • the embodiment of the present application provides a storage medium, the storage medium is a computer-readable storage medium for computer-readable storage, the storage medium stores one or more programs, and the one or more This program can be executed by one or more processors to implement a multi-label identification method, wherein the multi-label identification method includes: obtaining original data, wherein the original data includes user basic data, user behavior data, and User comment data; normalize the user basic data to obtain user basic features; perform feature extraction on the user behavior data through a pre-trained graph convolution model to obtain a behavior feature matrix; Carry out participle processing, obtain comment text phrase vector; Described comment text phrase vector is input in the comparative learning model of pre-training, so that described comment text phrase vector and the reference word embedding matrix in described contrast learning model Perform matrix multiplication to obtain comment word embedding vectors; perform fusion processing on the user basic features, the behavior feature matrix, and the comment word embedding vectors to obtain standard portrait feature vectors; The standard portrait feature vector performs label recognition processing
  • the multi-tag recognition method, device, electronic equipment, and storage medium proposed in this application can greatly shorten the model training time, improve the recognition efficiency, and improve the recognition accuracy of user portrait tags.
  • Fig. 1 is a flow chart of the multi-tag identification method provided by the embodiment of the present application.
  • Fig. 2 is the flowchart of step S103 in Fig. 1;
  • Fig. 3 is the flowchart of step S104 in Fig. 1;
  • Fig. 4 is the flowchart of step S105 in Fig. 1;
  • Fig. 5 is another flow chart of the multi-tag identification method provided by the embodiment of the present application.
  • Fig. 6 is the flowchart of step S107 in Fig. 1;
  • Fig. 7 is the flowchart of step S108 in Fig. 1;
  • Fig. 8 is a schematic structural diagram of a multi-tag identification device provided by an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application.
  • embodiments of the present application provide a multi-tag identification method, device, electronic equipment, and storage medium, aiming at improving the identification accuracy of user portrait tags.
  • the multi-tag identification method, device, electronic device, and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the multi-tag identification method in the embodiment of the present application is described.
  • AI artificial intelligence
  • the embodiments of the present application may acquire and process relevant data based on artificial intelligence technology.
  • artificial intelligence is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .
  • Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics.
  • Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
  • the multi-tag identification method provided in the embodiment of the present application relates to the fields of artificial intelligence and digital medical technology.
  • the multi-tag identification method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running on a terminal or a server.
  • the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.
  • the server end can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or It can be configured as a cloud that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.
  • the server; the software may be an application to realize the multi-label identification method, but is not limited to the above forms.
  • the application can be used in numerous general purpose or special purpose computer system environments or configurations. Examples: personal computers, server computers, handheld or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, including A distributed computing environment for any of the above systems or devices, etc.
  • This application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • the application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer storage media including storage devices.
  • Fig. 1 is an optional flow chart of the multi-tag identification method provided by the embodiment of the present application.
  • the method in Fig. 1 may include but not limited to steps S101 to S108.
  • Step S101 obtaining original data, wherein the original data includes user basic data, user behavior data and user comment data;
  • Step S102 performing normalization processing on the basic user data to obtain basic user features
  • Step S103 using the pre-trained graph convolution model to perform feature extraction on user behavior data to obtain a behavior feature matrix
  • Step S104 performing word segmentation processing on user comment data to obtain comment text word segment vectors
  • Step S105 inputting the comment text word segment vector into the pre-trained comparative learning model, so that the comment text word segment vector and the reference word embedding matrix in the comparative learning model are matrix multiplied to obtain the comment word embedding vector;
  • Step S106 performing fusion processing on user basic features, behavior feature matrix and comment word embedding vectors to obtain standard portrait feature vectors
  • Step S107 using the pre-trained label recognition model to perform label recognition processing on the standard portrait feature vector to obtain the probability value of each preset portrait label;
  • Step S108 according to the magnitude relationship between the probability value and the preset probability threshold, the target portrait label is obtained.
  • the multi-label recognition method of the present application can realize the recognition of different portrait labels through a label recognition model. Compared with the traditional technology, which needs to train classifiers for different label categories separately, the model can be greatly shortened. The training time improves the recognition efficiency. At the same time, the multi-label recognition method of the present application performs corresponding data preprocessing for different types of user data, so that the obtained standard portrait feature vectors can better meet the recognition requirements, and can improve the recognition accuracy of user portrait labels.
  • the original user data can be crawled from multiple preset data sources by means of a web crawler, wherein the basic data includes the user's gender, education background, age group, etc.; behavior data Including the user's click data on the display of the course content and the click data of the recommended courses on the course page, etc.; the comment data is the user's text comment data on the course, etc.
  • a series of numbers can be set for different types of basic data according to preset normalization conditions.
  • Educational background is divided into ⁇ 1,2,3,4,5,6,7,8 ⁇ set, 1 represents primary school, 2 represents junior high school, 3 represents technical secondary school, 4 represents high school, 5 represents junior college, 6 represents undergraduate, 7 represents Master, 8 represents Ph.D.
  • the age group is divided into ⁇ 5, 6, 7, 8, 9, 0 ⁇ sets, 5 represents the post-50s, 6 represents the post-60s, and so on.
  • step S103 may include but not limited to include steps S201 to S204:
  • Step S201 mapping user behavior data to a preset vector space to obtain user behavior feature vectors
  • Step S202 constructing a behavior feature map according to the preset course types and user behavior feature vectors
  • Step S203 performing graph convolution processing on the behavior feature map to obtain a behavior degree matrix and a behavior adjacency matrix
  • Step S204 performing difference processing on the behavior degree matrix and the behavior matrix to obtain a behavior characteristic matrix.
  • the user behavior data can be mapped to a preset vector space by using the MLP network to map the semantic space of the user behavior data to the vector space, and obtain the user behavior feature vector.
  • step S202 record each preset course as a node, analyze the user's behavior data, and if it is detected that the user clicks on another course through the recommendation module of a course page, then establish a link between the two courses. side.
  • the relationship between each course type and user behavior feature vector is constructed to obtain an undirected graph, which is the behavior feature graph.
  • the adjacency matrix is 0; therefore, by performing graph convolution processing on the behavior feature map, the Laplace transform of the behavior feature map can be realized, and the behavior degree matrix (ie, the angle matrix D) and the behavior adjacency matrix (ie, the adjacency matrix A) can be obtained.
  • y is the output value and ⁇ is the sigmoid activation function.
  • L is the Laplacian matrix
  • x is the input labeled behavioral feature map
  • j is the number of rows of the Laplacian matrix, where j is generally much smaller than the number of nodes in the behavioral feature map.
  • ⁇ is the weight matrix.
  • the parameter values of the weight matrix are randomly generated when the graph convolution model is initialized, and can be adjusted later by training the graph convolution model. Specifically, calculate the error between the marked behavior features and the predicted features, and then The error is backpropagated to update the parameter value to optimize the graph convolution model.
  • step S104 may include but not limited to include steps S301 to S302:
  • Step S301 performing word segmentation processing on the user comment data through a preset word segmenter to obtain comment text segments
  • Step S302 encoding the word segment of the review text to obtain a word segment vector of the review text.
  • step S301 when using the Jieba tokenizer to perform word segmentation processing on the user comment data, first generate a directed acyclic graph corresponding to the user comment data by comparing the dictionary in the Jieba tokenizer, and then according to the preset selection Patterns and dictionaries search for the shortest path on the directed acyclic graph, and intercept the user comment data according to the shortest path, or directly intercept the user comment data to obtain comment text segments.
  • HMM Hidden Markov Model
  • the positions B, M, E, and S of the characters in the comment text segment are regarded as hidden states, and the characters are observed states, where B/M/E/S represent the words that appear at the beginning, middle, end, and word respectively into words.
  • a dictionary file is used to store representation probability matrix, initial probability vector and transition probability matrix between characters respectively. Then use the Viterbi algorithm to solve the maximum possible hidden state, so as to obtain the comment text segment.
  • a preset BERT encoder may be used to encode the word segment of the comment text, so that each character on the word segment of the comment text carries a corresponding code, thereby obtaining a word segment vector of the comment text.
  • the method before step S105, the method further includes pre-training a contrastive learning model, which may specifically include but not limited to steps a to f:
  • sample pairs according to the initial embedding data, wherein the sample pairs include positive example pairs and negative example pairs;
  • step a and step b first obtain the sample user data, encode the sample user data, map the sample user data to the embedding space, and perform vector representation on the sample user data, so as to obtain the initial embedded data (ie Initial embedding data), the initial embedding data includes positive sample data and negative sample data.
  • Initial embedding data the initial embedding data includes positive sample data and negative sample data.
  • the data enhancement processing is performed on the initial embedded data through the dropout mask mechanism; the embodiment of the present application replaces the traditional data enhancement method through the dropout mask mechanism, that is, the same sample data is input into the dropout encoder twice to obtain
  • the two vectors of the two vectors are used as positive example pairs for comparative learning, and the effect is good enough, because for example, a different dropout mask is randomly generated for each dropout inside BERT, so only the same sample data (that is, the initial Embedding data) is input to the simCSE model twice, and the two vectors obtained are the results of applying two different dropout masks.
  • the dropout mask is a random network model, which is the mask of the model parameter W, which prevents overfitting.
  • the data (that is, the first vector and the second vector) obtained through data enhancement processing are positive example pairs, and other data that have not undergone data enhancement are negative example pairs.
  • some of the initial embedded data in a batch can be processed through data enhancement to obtain positive example pairs, and the other part of the initial embedded data can be used as negative example pairs.
  • step d is performed to input the sample pair into the contrastive learning model.
  • both the first similarity and the second similarity are cosine similarity.
  • step f may include, but is not limited to include:
  • the loss function Maximize the first similarity to the first value and minimize the second similarity to the first value to optimize the loss function; where the first similarity is the numerator of the loss function, the first similarity and the second The similarity is the denominator of the loss function, the first value is 1, and the second value is 0.
  • the numerator is the first similarity of the corresponding positive example pair
  • the denominator is the first similarity and the second similarity of all negative example pairs
  • the value of the molecular formula composed of the numerator and the denominator is wrapped in -log() In this way, the loss function can be minimized by maximizing the numerator and minimizing the denominator.
  • minimizing the loss function infoNCE loss is to maximize the numerator and minimize the denominator, that is, to maximize the first similarity of the positive pair and minimize the second similarity of the negative pair, and the loss The function is minimized to realize the optimization of the loss function. More specifically, the loss function is shown in formula (2):
  • the loss function represents the loss of sample i; in the loss function, the numerator is the similarity of the positive pair , the denominator is the similarity between positive pairs and all negative pairs, and then wrap this value in -log(), so that maximizing the numerator and minimizing the denominator can minimize the loss function.
  • step S105 may include but not limited to include steps S401 to S402:
  • Step S401 input the comment text word segment vector into the comparative learning model, so that the comment text word segment vector and the reference word embedding matrix are matrix multiplied to obtain a plurality of basic word embedding vectors;
  • Step S402 performing mapping processing on the basic word embedding vectors to obtain review word embedding vectors.
  • step S401 is executed, and the value of the reference word embedding matrix in the comparison model can be completely fixed by training the comparison model, and other model parameters of the comparison model are also fixed. Therefore, inputting the comment text word segment vector into the comparison model, the fixed reference word embedding matrix can be used to perform matrix multiplication with each comment text word segment vector to obtain multiple basic word embedding vectors.
  • step S402 use the fixed MLP network in the comparison model to perform mapping processing on the basic word embedding vector to obtain the comment word embedding vector.
  • the MLP network includes a linear layer, a ReLu activation function, and a linear layer.
  • step S106 when step S106 is executed, the basic feature data and the behavioral feature matrix are respectively vectorized to obtain the basic feature vector and the behavioral feature vector, and then the basic feature vector, the behavioral feature vector and the word embedding feature vector are performed Fusion processing to get the standard feature vector.
  • the standard feature vector X [gender, education, age group, [GCN], [BERT]].
  • GCN is a 256-dimensional vector
  • BERT is a 512-dimensional vector
  • X is a 3+256+512 vector.
  • the method further includes pre-training the label recognition model, which may specifically include but not limited to steps S501 to S505:
  • Step S501 acquiring marked user data
  • Step S502 performing feature extraction on the labeled user data to obtain sample feature vectors
  • Step S503 inputting the sample feature vector into the label recognition model
  • Step S504 calculate the sample probability prediction value of each portrait label category through the loss function of the label recognition model
  • Step S505 optimize the loss function of the label recognition model according to the sample probability prediction value, so as to update the label recognition model.
  • the label recognition model can be a textcnn model, and the label recognition model includes an Embedding layer, a convolution layer, a pooling layer and an output layer.
  • the Embedding layer of the label recognition model can use ELMO, GLOVE, Word2Vector, Bert and other algorithms to generate a dense vector from the input text data. Then, the dense vector is convoluted and pooled through the convolution layer and pooling layer of the label recognition model to obtain the target feature vector, and then the target feature vector is input to the output layer, and the preset function in the output layer is The classification operation can be performed on the target feature vector to obtain the label feature vector and the probability value of each preset category.
  • step S501 is executed to obtain marked user data, where the marked user data includes user portrait category labels. Furthermore, step S502 is executed, using the MLP network to perform multiple mapping processes on the labeled user data to obtain sample feature vectors.
  • step S503 is executed to input the sample feature vector into the label recognition model.
  • step S504 the sample feature vector is generated into a dense feature vector through the Embedding layer of the label recognition model, and then the dense feature vector is convoluted and pooled through the convolution layer and the pooling layer to obtain the target The feature vector, and then input the target feature vector to the output layer, and calculate the sample probability prediction value of each portrait label category through the loss function; where the loss function is shown in formula (3):
  • t is the target value (target), and t needs to take a value between [0,1]. Since in the embodiment of this application, t is used as a portrait label category, the value of t is 0 or 1, and o indicates label identification The model's probabilistic predictions.
  • step S505 is executed to calculate the model loss of the label recognition model based on the sample probability prediction value, that is, the loss value, and then use the gradient descent method to backpropagate the loss value, feed the loss value back to the label recognition model, and modify the label recognition model.
  • the preset iteration condition is that the number of iterations can reach the preset value, or the variance of the loss function is smaller than the preset threshold.
  • the backpropagation can be stopped, and the final model parameter can be used as the final model parameter to complete the update of the label recognition model.
  • step S107 may also include but not limited to steps S601 to S602:
  • Step S601 reconstructing the standard portrait feature vector according to the preset label dimension to obtain the label feature vector
  • Step S602 using a preset function to identify the tag feature vector to obtain the probability value of each preset portrait tag.
  • step S601 is first performed to reconstruct the standard image feature vector according to the preset label dimension and encoder, for example, encode the standard image feature vector according to the bottom-up encoding sequence and label dimension.
  • the standard portrait feature vector is encoded for the first time to obtain the bottom label feature vector z1, and then the downsampling process is performed layer by layer to obtain the label feature vector [z2, z3..., zk] corresponding to each label dimension.
  • step S602 the preset function is a sigmoid function, and the sigmoid function can be expressed as shown in formula (4):
  • the sigmoid function will classify the label feature vector according to the preset portrait label category, and create a probability distribution on each portrait label category, so as to obtain the value of each preset portrait label. probability value.
  • step S108 may also include but not limited to include steps S701 to S702:
  • Step S701 including the portrait labels whose probability value is greater than or equal to the preset probability threshold into the same set to obtain a set of candidate portrait labels;
  • Step S702 screening the candidate portrait label set to obtain the target portrait label.
  • first execute step S701 if the probability value is less than the preset probability threshold, filter out the portrait label corresponding to the probability value; if the probability value is greater than or equal to the preset probability threshold, then include the portrait label corresponding to the probability value into the candidate Portrait labels set.
  • the preset probability threshold is 0.6, and when the probability value is greater than or equal to 0.6, it can be considered that the user has the current portrait tag.
  • the portrait tags in the candidate portrait tag set can be screened by means of manual review, etc., and the portrait tag with the highest matching degree with the current user is extracted to obtain the target portrait tag.
  • the portrait tags in the candidate portrait tag set can also be arranged in descending order according to the size of the probability value, and the top five portrait tags are selected as the target portrait tags of the current user.
  • Other methods may also be used to filter the portrait tags in the candidate portrait tag set, which is not limited thereto.
  • raw data is acquired, wherein the raw data includes basic user data, user behavior data, and user comment data.
  • the user basic data is normalized to obtain the user basic features;
  • the user behavior data is extracted through the pre-trained graph convolution model to obtain the behavior feature matrix;
  • the user comment data is word-segmented to obtain the comment text word segment vector, and input the comment text segment vector into the pre-trained comparative learning model, so that the review text segment vector and the reference word embedding matrix in the comparative learning model are matrix multiplied to obtain the review word embedding vector.
  • different types of data can be preprocessed separately to obtain user basic features, behavior feature matrices, and comment embedding vectors, which improves the rationality of user data.
  • the standard portrait feature vector is obtained by fusing the user's basic features, behavior feature matrix, and comment word embedding vector.
  • the pre-trained label recognition model is used to perform label recognition processing on the standard portrait feature vector to obtain the probability value of each preset portrait label, and obtain the target portrait label according to the relationship between the probability value and the preset probability threshold.
  • the multi-label recognition method of the present application can realize the recognition of different portrait labels through a label recognition model. Compared with the traditional technology that needs to train classifiers for different label categories separately, it can greatly shorten the model training time and improve the recognition efficiency.
  • the multi-label recognition method of the present application performs corresponding data preprocessing for different types of user data, so that the obtained standard portrait feature vectors can better meet the recognition requirements, and can improve the recognition accuracy of user portrait labels.
  • the embodiment of the present application also provides a multi-label identification device, which can realize the above-mentioned multi-label identification method, the device includes:
  • a data acquisition module 801, configured to acquire raw data, wherein the raw data includes user basic data, user behavior data and user comment data;
  • a normalization module 802 configured to perform normalization processing on user basic data to obtain user basic features
  • the feature extraction module 803 is used to perform feature extraction on user behavior data through a pre-trained graph convolution model to obtain a behavior feature matrix
  • the word segmentation module 804 is used to perform word segmentation processing on the user comment data to obtain the comment text word segment vector;
  • Contrastive learning module 805 for inputting the comment text word segment vector into the pre-trained comparative learning model, so that the comment text word segment vector and the reference word embedding matrix in the comparative learning model are matrix multiplied to obtain the comment word embedding vector ;
  • the fusion module 806 is used to fuse the user basic features, behavior feature matrix and comment word embedding vectors to obtain standard portrait feature vectors;
  • the label recognition module 807 is used to perform label recognition processing on the standard portrait feature vector through the pre-trained label recognition model to obtain the probability value of each preset portrait label;
  • the comparison module 808 is configured to obtain the target portrait label according to the magnitude relationship between the probability value and the preset probability threshold.
  • the specific implementation of the multi-tag identification device is basically the same as the specific embodiment of the above-mentioned multi-label identification method, and will not be repeated here.
  • the embodiment of the present application also provides an electronic device, the electronic device includes: a memory, a processor, a program stored in the memory and operable on the processor, and a data bus for realizing connection and communication between the processor and the memory , when the program is executed by the processor, the above multi-label identification method is realized.
  • the electronic device may be any intelligent terminal including a tablet computer, a vehicle-mounted computer, and the like.
  • FIG. 9 illustrates a hardware structure of an electronic device in another embodiment.
  • the electronic device includes:
  • the processor 901 may be implemented by a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute related programs, so as to realize The technical solutions provided by the embodiments of the present application;
  • a general-purpose CPU Central Processing Unit, central processing unit
  • a microprocessor an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute related programs, so as to realize The technical solutions provided by the embodiments of the present application;
  • ASIC Application Specific Integrated Circuit
  • the memory 902 may be implemented in the form of a read-only memory (ReadOnlyMemory, ROM), a static storage device, a dynamic storage device, or a random access memory (RandomAccessMemory, RAM).
  • the memory 902 can store operating systems and other application programs.
  • the relevant program codes are stored in the memory 902 and called by the processor 901 to execute a multi- The label recognition method, wherein the multi-label recognition method includes: obtaining original data, wherein the original data includes user basic data, user behavior data, and user comment data; normalizing the user basic data to obtain user basic features;
  • the trained graph convolution model extracts features from user behavior data to obtain a behavior feature matrix; performs word segmentation processing on user comment data to obtain comment text segment vectors; input comment text segment vectors into the pre-trained comparative learning model, Multiply the word segment vector of the comment text with the reference word embedding matrix in the comparative learning model to obtain the comment word embedding vector; perform fusion processing on the user basic features, behavior feature matrix and comment word embedding vector to obtain the standard portrait feature vector Carrying out label recognition processing on the standard portrait feature vector through the pre-trained label recognition model to obtain the probability value of each preset portrait label; according to the size relationship between the probability value
  • the input/output interface 903 is used to realize information input and output
  • the communication interface 904 is used to realize the communication interaction between the device and other devices, and the communication can be realized through a wired method (such as USB, network cable, etc.), or can be realized through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.);
  • bus 905 for transferring information between various components of the device (such as processor 901, memory 902, input/output interface 903 and communication interface 904);
  • the processor 901 , the memory 902 , the input/output interface 903 and the communication interface 904 are connected to each other within the device through the bus 905 .
  • An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium for computer-readable storage, and the computer-readable storage medium may be non-volatile or volatile.
  • the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement a multi-label identification method, wherein the multi-label identification method includes: obtaining original data, wherein the original data Including user basic data, user behavior data and user comment data; normalize user basic data to obtain user basic features; use pre-trained graph convolution model to extract user behavior data to obtain behavior feature matrix;
  • the user comment data is subjected to word segmentation processing to obtain the comment text segment vector; the comment text segment vector is input into the pre-trained comparative learning model, so that the comment text segment vector and the reference word embedding matrix in the comparative learning model are matrix correlated.
  • memory can be used to store non-transitory software programs and non-transitory computer-executable programs.
  • the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices.
  • the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.
  • the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including multiple instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method in each embodiment of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), magnetic disk or optical disc, etc., which can store programs. medium.
  • ROM read-only memory
  • RAM random access memory
  • magnetic disk or optical disc etc., which can store programs. medium.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention se rapporte au domaine technique de l'intelligence artificielle. Selon certains modes de réalisation, la présente invention concerne un procédé et un appareil d'identification multi-étiquettes, ainsi qu'un dispositif électronique et un support de stockage. Le procédé comprend les étapes suivantes: l'exécution d'un traitement de normalisation sur des données de base d'utilisateur pour obtenir des caractéristiques de base d'utilisateur; l'exécution d'une extraction de caractéristiques sur des données de comportement d'utilisateur au moyen d'un modèle de convolution graphique pour obtenir une matrice de caractéristiques de comportement; l'exécution d'un traitement de segmentation de mots sur des données de commentaire d'utilisateur pour obtenir des vecteurs de segment de mots de texte de commentaire; la saisie des vecteurs de segment de mots de texte de commentaire dans un modèle d'apprentissage comparatif de sorte qu'une multiplication matricielle est effectuée sur les vecteurs de segment de mots de texte de commentaire et une matrice d'incorporation de mots de référence afin d'obtenir des vecteurs d'incorporation de mots de commentaire; l'exécution d'un traitement de fusion sur les caractéristiques de base d'utilisateur, la matrice de caractéristiques de comportement et les vecteurs d'incorporation de mots de commentaire pour obtenir des vecteurs de caractéristiques de portrait standard; l'exécution d'un traitement d'identification d'étiquettes sur les vecteurs de caractéristiques de portrait standard au moyen d'un modèle d'identification d'étiquettes pour obtenir des valeurs de probabilité d'étiquettes de portrait; et l'obtention d'une étiquette de portrait cible selon les valeurs de probabilité. Selon des modes de réalisation de la présente invention, la précision avec laquelle des étiquettes de portrait d'un utilisateur sont identifiées est améliorée.
PCT/CN2022/090726 2022-01-11 2022-04-29 Procédé et appareil d'identification multi-étiquettes, dispositif électronique, et support d'enregistrement WO2023134084A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210027793.0A CN114358007A (zh) 2022-01-11 2022-01-11 多标签识别方法、装置、电子设备及存储介质
CN202210027793.0 2022-01-11

Publications (1)

Publication Number Publication Date
WO2023134084A1 true WO2023134084A1 (fr) 2023-07-20

Family

ID=81108800

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/090726 WO2023134084A1 (fr) 2022-01-11 2022-04-29 Procédé et appareil d'identification multi-étiquettes, dispositif électronique, et support d'enregistrement

Country Status (2)

Country Link
CN (1) CN114358007A (fr)
WO (1) WO2023134084A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116910628A (zh) * 2023-09-12 2023-10-20 联通在线信息科技有限公司 创作者专业度画像评估方法及系统
CN116910377A (zh) * 2023-09-14 2023-10-20 长威信息科技发展股份有限公司 一种网格事件分类搜索推荐方法及系统

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114358007A (zh) * 2022-01-11 2022-04-15 平安科技（深圳）有限公司 多标签识别方法、装置、电子设备及存储介质
CN115035763A (zh) * 2022-06-22 2022-09-09 深圳市沃特沃德信息有限公司 听写优化方法、装置、计算机设备和存储介质
CN115689648B (zh) * 2022-10-28 2023-07-28 广东柏烨互动网络科技有限公司 应用于定向投放的用户信息处理方法及系统
CN117421497B (zh) * 2023-11-02 2024-04-26 北京蜂鸟映像电子商务有限公司 作品对象的处理方法、装置、可读存储介质及电子设备

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200142999A1 (en) * 2018-11-02 2020-05-07 Valve Corporation Classification and moderation of text
CN112528163A (zh) * 2020-12-04 2021-03-19 中山大学 一种基于图卷积网络的社交平台用户职业预测方法
CN113139141A (zh) * 2021-04-22 2021-07-20 康键信息技术（深圳）有限公司 用户标签扩展标注方法、装置、设备及存储介质
CN113792818A (zh) * 2021-10-18 2021-12-14 平安科技（深圳）有限公司 意图分类方法、装置、电子设备及计算机可读存储介质
CN113868417A (zh) * 2021-09-27 2021-12-31 平安国际智慧城市科技股份有限公司 一种敏感评论识别方法、装置、终端设备和存储介质
CN113887215A (zh) * 2021-10-18 2022-01-04 平安科技（深圳）有限公司 文本相似度计算方法、装置、电子设备及存储介质
CN114358007A (zh) * 2022-01-11 2022-04-15 平安科技（深圳）有限公司 多标签识别方法、装置、电子设备及存储介质

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109918507B (zh) * 2019-03-08 2021-04-27 北京工业大学 一种基于TextCNN改进的文本分类方法
CN113392317A (zh) * 2021-01-07 2021-09-14 腾讯科技（深圳）有限公司 一种标签配置方法、装置、设备及存储介质

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200142999A1 (en) * 2018-11-02 2020-05-07 Valve Corporation Classification and moderation of text
CN112528163A (zh) * 2020-12-04 2021-03-19 中山大学 一种基于图卷积网络的社交平台用户职业预测方法
CN113139141A (zh) * 2021-04-22 2021-07-20 康键信息技术（深圳）有限公司 用户标签扩展标注方法、装置、设备及存储介质
CN113868417A (zh) * 2021-09-27 2021-12-31 平安国际智慧城市科技股份有限公司 一种敏感评论识别方法、装置、终端设备和存储介质
CN113792818A (zh) * 2021-10-18 2021-12-14 平安科技（深圳）有限公司 意图分类方法、装置、电子设备及计算机可读存储介质
CN113887215A (zh) * 2021-10-18 2022-01-04 平安科技（深圳）有限公司 文本相似度计算方法、装置、电子设备及存储介质
CN114358007A (zh) * 2022-01-11 2022-04-15 平安科技（深圳）有限公司 多标签识别方法、装置、电子设备及存储介质

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116910628A (zh) * 2023-09-12 2023-10-20 联通在线信息科技有限公司 创作者专业度画像评估方法及系统
CN116910628B (zh) * 2023-09-12 2024-02-06 联通在线信息科技有限公司 创作者专业度画像评估方法及系统
CN116910377A (zh) * 2023-09-14 2023-10-20 长威信息科技发展股份有限公司 一种网格事件分类搜索推荐方法及系统
CN116910377B (zh) * 2023-09-14 2023-12-08 长威信息科技发展股份有限公司 一种网格事件分类搜索推荐方法及系统

Also Published As

Publication number Publication date
CN114358007A (zh) 2022-04-15

Similar Documents

Publication Publication Date Title
WO2023134084A1 (fr) Procédé et appareil d'identification multi-étiquettes, dispositif électronique, et support d'enregistrement
EP3660733B1 (fr) Procédé et système d'extraction d'informations à partir d'images de documents à l'aide d'une interface de conversation et d'une interrogation de base de données
CN111582409B (zh) 图像标签分类网络的训练方法、图像标签分类方法及设备
CN108694225B (zh) 一种图像搜索方法、特征向量的生成方法、装置及电子设备
US20210382937A1 (en) Image processing method and apparatus, and storage medium
CN105354307B (zh) 一种图像内容识别方法及装置
CN110969020A (zh) 基于cnn和注意力机制的中文命名实体识别方法、系统及介质
CN112380435A (zh) 基于异构图神经网络的文献推荐方法及推荐系统
CN111651992A (zh) 命名实体标注方法、装置、计算机设备和存储介质
CN112613308A (zh) 用户意图识别方法、装置、终端设备及存储介质
CN111475622A (zh) 一种文本分类方法、装置、终端及存储介质
WO2023134088A1 (fr) Procédé et appareil de génération de résumé vidéo, dispositif électronique et support de stockage
CN111444715B (zh) 实体关系识别方法、装置、计算机设备和存储介质
CN111159485A (zh) 尾实体链接方法、装置、服务器及存储介质
WO2023179429A1 (fr) Procédé et appareil de traitement de données vidéo, dispositif électronique et support de stockage
CN112580328A (zh) 事件信息的抽取方法及装置、存储介质、电子设备
CN113486175B (zh) 文本分类方法、文本分类装置、计算机设备及存储介质
CN114612921B (zh) 表单识别方法、装置、电子设备和计算机可读介质
CN111191031A (zh) 一种基于WordNet和IDF的非结构化文本的实体关系分类方法
CN113836992A (zh) 识别标签的方法、训练标签识别模型的方法、装置及设备
CN108805280B (zh) 一种图像检索的方法和装置
US20200167655A1 (en) Method and apparatus for re-configuring neural network
CN112364166B (zh) 关系抽取模型的建立方法以及关系抽取方法
CN112527959B (zh) 基于无池化卷积嵌入和注意分布神经网络的新闻分类方法
CN113536784A (zh) 文本处理方法、装置、计算机设备和存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22919724

Country of ref document: EP

Kind code of ref document: A1