WO2021208696A1 - Procédé d'analyse d'intention d'utilisateur, appareil, dispositif électronique et support de stockage informatique - Google Patents

Procédé d'analyse d'intention d'utilisateur, appareil, dispositif électronique et support de stockage informatique Download PDF

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Publication number
WO2021208696A1
WO2021208696A1 PCT/CN2021/082893 CN2021082893W WO2021208696A1 WO 2021208696 A1 WO2021208696 A1 WO 2021208696A1 CN 2021082893 W CN2021082893 W CN 2021082893W WO 2021208696 A1 WO2021208696 A1 WO 2021208696A1
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text
feature extraction
vector
intention
semantic
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PCT/CN2021/082893
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English (en)
Chinese (zh)
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李志韬
王健宗
程宁
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平安科技（深圳）有限公司
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Publication of WO2021208696A1 publication Critical patent/WO2021208696A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • This application relates to the field of data analysis technology, and in particular to a user intention analysis method, device, electronic equipment, and computer-readable storage medium.
  • a user intention analysis method provided by this application includes:
  • a preset number of target intents are selected from the plurality of target intents as user intents according to the priority.
  • the present application also provides a user intention analysis device, which includes:
  • the vector conversion module is used to obtain the input text of the user and convert the input text into a semantic vector
  • the intention prediction module is used to perform intention prediction on the semantic vector to obtain the predicted intention label of the input text
  • the feature extraction module is used to perform feature extraction on the predicted intent tag by using a feature extraction network, and generate multiple target intents based on the extracted features;
  • a priority calculation module for calculating the priority of each target intent among the multiple target intents
  • the intention screening module is used to select a preset number of target intentions as user intentions according to the priority.
  • This application also provides an electronic device, which includes:
  • Memory storing at least one instruction
  • the processor executes the instructions stored in the memory to implement the following steps:
  • a preset number of target intents are selected from the plurality of target intents as user intents according to the priority.
  • the present application also provides a computer-readable storage medium in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the following steps:
  • a preset number of target intents are selected from the plurality of target intents as user intents according to the priority.
  • FIG. 1 is a schematic flowchart of a user intention analysis method provided by an embodiment of this application
  • FIG. 2 is a functional module diagram of a user intention analysis device provided by an embodiment of the application
  • FIG. 3 is a schematic structural diagram of an electronic device that implements the user intention analysis method provided by an embodiment of the application.
  • the embodiment of the present application provides a user intention analysis method.
  • the execution subject of the user intention analysis method includes, but is not limited to, a server, a terminal, etc., which can be configured to execute at least one of the electronic devices of the method provided in the embodiments of the present application.
  • the user intention analysis method may be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform.
  • the server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.
  • the user intention analysis method includes:
  • the user's input text may be any text provided by the user that contains the user's intention, for example, the text for the user to inquire about certain business information, the text for the user to inquire about a certain product, and so on.
  • a python sentence with a data capture function can be used to obtain input text stored in advance by a user from a blockchain node, and the high throughput of the blockchain node for data can be used to improve the efficiency of obtaining input text.
  • the converting the input text into a semantic vector includes:
  • the input text is transformed using the trained text vectorization model to obtain the semantic vector of the input text.
  • the deep learning network model is used to construct the initial vectorization model; in detail, the Bert base network model is used as the initial vectorization model, and the initial vectorization model is followed by a layer of serialized annotation algorithm network to obtain the The text vectorization model, wherein the serialized annotation algorithm network is used to constrain the sequence of characters input to the initial vectorization model to obtain the entity text vectorization model.
  • the performing preset entity marking on the historical text to obtain the training text includes:
  • the preset entities construct a label set including non-preset entity character tags, preset entity beginning character tags, and preset entity middle character tags;
  • the tag set includes a plurality of preset tags, such as a non-preset entity character tag, a preset entity start character tag, and a preset entity intermediate character tag, wherein the non-preset entity character tag is used for Mark the characters that are not preset entities in the historical text, the preset entity start character tag is used to mark the beginning characters of the preset entity in the historical text, and the preset entity middle character tag is used to mark the preset entities in the historical text except Characters other than the start character.
  • the non-preset entity character tag is used for Mark the characters that are not preset entities in the historical text
  • the preset entity start character tag is used to mark the beginning characters of the preset entity in the historical text
  • the preset entity middle character tag is used to mark the preset entities in the historical text except Characters other than the start character.
  • the historical text contains the text information "a financial company provides a zero interest loan", the default entity is a financial entity, and the label entity set includes: non-financial entity character label, financial entity start character label, financial entity middle character label, then Use the label entity set to mark the text message "a financial company provides a zero interest loan", the character "a financial” is marked with the financial entity start character label as the financial entity start character, and the "company” character is marked with the financial entity middle character label as financial Intermediate characters of the entity, "provided” characters are marked as non-financial entity characters using non-financial entity character tags, "zero interest rate” characters are marked as non-financial entity characters using non-financial entity character tags, and "loan” characters are marked as non-financial entity character tags Mark as a non-financial entity character.
  • the iterative training of the text vectorization model by using the training text until the text vectorization model converges includes:
  • a loss value between the predicted text vector and the standard text vector is calculated, and when the loss value is less than a preset loss threshold, it is determined that the text vectorization model converges.
  • a python sentence with a data capture function may be used to obtain a pre-stored standard text vector from the database.
  • the embodiment of the present application may use a preset loss function to calculate the loss value between the predicted text vector and the standard text vector.
  • the loss function includes, but is not limited to, a cross-entropy loss function, a square error loss function, and Regular loss function.
  • the embodiment of the present application converts the input text into a semantic vector, which can realize the digitization of the text information, which is beneficial to improve the efficiency of subsequent analysis of the input text.
  • the intent prediction performed on the semantic vector to obtain the predicted intent label of the input text includes:
  • the fused semantic feature is used as the predicted intent label of the input text.
  • the embodiment of the application adopts an LSTM network (Long Short-Term Memory Net, long short-term memory network) to construct an intent prediction network including multiple down-sampling layers, and uses the multi-layer down-sampling layer structure in the LSTM network to perform semantic analysis.
  • the vector is down-sampled multiple times, which is beneficial to extract more accurate semantic features and improve the accuracy of the generated predicted intent label.
  • the front down-sampling layer and the back down-sampling layer are relative terms.
  • the intention prediction network includes 4 down-sampling layers, and the first down-sampling layer that down-samples the semantic vector is relatively
  • the second down-sampling layer that down-samples the semantic vector, the third down-sampling layer that down-samples the semantic vector, and the fourth down-sampling layer that down-samples the semantic vector are the pre-down-sampling layer ;
  • the second down-sampling layer that down-samples the standard semantic vector is a post-down-sampling layer relative to the first down-sampling layer that down-samples the semantic vector, and so on.
  • the later down-sampling layer down-samples the result (forward semantic feature) obtained by the previous down-sampling layer to obtain the backward semantic feature.
  • the later down-sampling layer down-samples the result (forward semantic feature) obtained by the previous down-sampling layer to obtain the backward semantic feature.
  • the thoracic cavity image is down-sampled in the first down-sampling layer to obtain the first semantic feature
  • Feature fusion is performed on the first semantic feature, the second semantic feature, the third semantic feature, and the fourth semantic feature to obtain a fused semantic feature, and the fused semantic feature is used as a predicted intention label of the input text.
  • the embodiment of the present application performs intention prediction on the semantic vector to obtain the predicted intention label of the input text, which can reduce the amount of data in the semantic vector containing a large amount of semantics, and is beneficial to improve the efficiency of subsequent analysis of user intent.
  • the feature extraction network includes multiple visual layers and multiple hidden layers, wherein the visual layer includes multiple visual units, the hidden layer includes multiple hidden units, and The number of visible layers corresponds to the number of multiple hidden layers, and the number of multiple visible units corresponds to the number of multiple hidden units.
  • said using a feature extraction network to perform feature extraction on said predicted intent label includes:
  • the hidden layer of the feature extraction network uses a machine learning algorithm to perform feature extraction on the data representation of the visible layer label.
  • each visible unit in the visible layer of the feature extraction network is used to mark a data representation in the intent tag, and each hidden unit in the hidden layer of the feature extraction network is extracted.
  • each hidden unit in the hidden layer extracts a data characterization marked by a visible unit matching the hidden unit based on a machine learning algorithm.
  • the states of the visible unit and the hidden unit are represented by Boolean values, such as 0 and 1, where 0 represents an inactive state and 1 represents an activated state. Only after the visible unit and/or the hidden unit are activated by the activation function can the data contained in the visible unit be transmitted to the hidden unit matching the visible unit.
  • the activation function of the visible unit and/or the hidden unit is as follows:
  • E(v, h, ⁇ ) is the activation value
  • I is the number of visible units in the visible layer
  • J is the number of hidden units in the hidden layer
  • a is the bias of the visible layer Vector
  • b is the bias vector of the hidden layer
  • w is the direct weight matrix between the visible layer and the hidden layer
  • v is any visible unit in the visible layer
  • h is the hidden layer
  • ⁇ is the preset error parameter.
  • the visible unit and/or the hidden unit are activated by the activation function.
  • the data contained in the visible unit and/or the hidden unit is transmitted to the hidden unit matching the visible unit .
  • the visible unit in the visible layer is matched with the hidden unit in the hidden layer through the following matching algorithm:
  • P(v, h, ⁇ ) is a matching value
  • v is any visible unit in the visible layer
  • h is any hidden unit in the hidden layer
  • ⁇ is a preset error parameter
  • Z is The normalization factor of the feature extraction network, exp(-E(v, h, ⁇ )) is the expectation that the visible unit v matches the hidden unit h.
  • the activated visible layer can transmit data to and match the visible layer.
  • the hidden layer that has been activated is not limited to
  • v j is the jth hidden unit in the hidden layer
  • h is any hidden unit in the hidden layer
  • ⁇ is a preset error parameter
  • J is the number of hidden units in the hidden layer
  • w is the direct weight matrix between the visible layer and the hidden layer
  • b is the bias vector of the hidden layer
  • ⁇ is the preset probability coefficient
  • h i is the i-th visible unit in the visible layer
  • v is any visible unit in the visible layer
  • ⁇ is a preset error parameter
  • I is the visible unit in the visible layer.
  • w is the direct weight matrix between the visible layer and the hidden layer
  • a is the bias vector of the visible layer
  • ⁇ is the preset probability coefficient.
  • multiple visible layers and hidden layers in multiple feature extraction networks are used to superimpose, so as to achieve more accurate feature extraction of the predicted intent tag.
  • using the hidden layer of the feature extraction network to use a machine learning algorithm to perform feature extraction on the data representation marked by the visible layer includes:
  • h is the data feature obtained by feature extraction of the data representation of the visible layer mark
  • Y is the data representation
  • w is the weight matrix between the visible layer and the hidden layer
  • b is The bias vector of the hidden layer.
  • the generating multiple target intents based on the extracted features includes: calculating the similarity between the extracted features and multiple preset standard intents, and determining that the multiple standard intents with the similarity greater than the similarity threshold are the target intents.
  • the target intention is described, wherein the embodiment of the present application may use the cosine similarity algorithm to calculate the similarity between the extracted feature and a plurality of preset standard intentions.
  • the feature extraction network is used to perform feature extraction on the predicted intention label, and multiple target intentions are generated based on the extracted features, and the user intention is predicted by using the extracted features, which improves the accuracy of predicting user intentions.
  • the calculation of the priority of each target intention among the multiple target intentions includes:
  • Pir is the priority
  • ⁇ is a preset weight coefficient
  • M k is the kth target intention among the multiple target intentions.
  • the selection of a preset number of target intentions as user intentions according to the priority includes:
  • a preset number of target intents are selected as user intents from the multiple target intents that have been sorted in a front-to-back order.
  • the multiple target intentions include: intention A, intention B, intention C, and intention D, where the priority of intention A is 50, the priority of intention B is 40, the priority of intention C is 60, and the priority of intention D is 60.
  • the priority of is 30. Therefore, the multiple target intentions are sorted as follows: Intention C, Intention A, Intent B, and Intent D in the order of priority; Select the intention C and the intention A as the user's intentions from the sorted multiple target intentions in the order of.
  • the embodiment of the application obtains the input text of the user, converts the input text into a semantic vector, and converts the input text into a semantic vector, which can realize the digitization of the text information, which is beneficial to improve the efficiency of subsequent analysis of the input text;
  • the vector is used to predict the intent to obtain the predicted intent label of the input text, which can reduce the amount of data in the semantic vector containing a large amount of semantics, which is beneficial to improve the efficiency and accuracy of subsequent analysis of user intent;
  • the feature extraction network is used to perform the prediction of the intent label.
  • Feature extraction and generating multiple target intents based on the extracted features realizes the use of extracted features to predict the user's intent, and improves the accuracy of predicting user intent; by calculating the priority, and according to the priority Sorting and screening of individual target intentions helps to improve the accuracy of the selected target intentions. Therefore, the user intention analysis method proposed in this application can solve the problem of low accuracy in identifying user intentions.
  • FIG. 2 it is a functional module diagram of a user intention analysis device provided by an embodiment of the present application.
  • the user intention analysis apparatus 100 described in this application may be installed in an electronic device.
  • the user intention analysis device 100 may include an electronic seal generation module 101, a two-dimensional code generation module 102, a two-dimensional code analysis module 103, a seal record search module 104, and an electronic seal verification module 105.
  • the module described in this application can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.
  • each module/unit is as follows:
  • the vector conversion module 101 is configured to obtain user input text, and convert the input text into a semantic vector
  • the intention prediction module 102 is configured to perform intention prediction on the semantic vector to obtain the predicted intention label of the input text
  • the feature extraction module 103 is configured to perform feature extraction on the predicted intent tag by using a feature extraction network, and generate multiple target intents based on the extracted features;
  • the priority calculation module 104 is configured to calculate the priority of each target intention among the multiple target intentions
  • the intention screening module 105 is configured to select a preset number of target intentions as user intentions according to the priority.
  • each module of the user intention analysis device is as follows:
  • the vector conversion module 101 is used to obtain user input text, and convert the input text into a semantic vector.
  • the user's input text may be any text provided by the user that contains the user's intention, for example, the text for the user to inquire about certain business information, the text for the user to inquire about a certain product, and so on.
  • a python sentence with a data capture function can be used to obtain input text stored in advance by a user from a blockchain node, and the high data throughput of the blockchain node can be used to improve the efficiency of obtaining input text.
  • vector conversion module 101 is specifically used for:
  • the input text is transformed using the trained text vectorization model to obtain the semantic vector of the input text.
  • the deep learning network model is used to construct the initial vectorization model; in detail, the Bert base network model is used as the initial vectorization model, and the initial vectorization model is followed by a layer of serialized annotation algorithm network to obtain the The text vectorization model, wherein the serialized annotation algorithm network is used to constrain the sequence of characters input to the initial vectorization model to obtain the entity text vectorization model.
  • the performing preset entity marking on the historical text to obtain the training text includes:
  • the preset entities construct a label set including non-preset entity character tags, preset entity beginning character tags, and preset entity middle character tags;
  • the tag set includes a plurality of preset tags, such as a non-preset entity character tag, a preset entity start character tag, and a preset entity intermediate character tag, wherein the non-preset entity character tag is used for Mark the characters that are not preset entities in the historical text, the preset entity start character tag is used to mark the beginning characters of the preset entity in the historical text, and the preset entity middle character tag is used to mark the preset entities in the historical text except Characters other than the start character.
  • the non-preset entity character tag is used for Mark the characters that are not preset entities in the historical text
  • the preset entity start character tag is used to mark the beginning characters of the preset entity in the historical text
  • the preset entity middle character tag is used to mark the preset entities in the historical text except Characters other than the start character.
  • the historical text contains the text information "a financial company provides a zero interest loan", the default entity is a financial entity, and the label entity set includes: non-financial entity character label, financial entity start character label, financial entity middle character label, then Use the label entity set to mark the text message "a financial company provides a zero interest loan", the character "a financial” is marked with the financial entity start character label as the financial entity start character, and the "company” character is marked with the financial entity middle character label as financial Intermediate characters of the entity, "provided” characters are marked as non-financial entity characters using non-financial entity character tags, "zero interest rate” characters are marked as non-financial entity characters using non-financial entity character tags, and "loan” characters are marked as non-financial entity character tags Mark as a non-financial entity character.
  • the iterative training of the text vectorization model by using the training text until the text vectorization model converges includes:
  • a loss value between the predicted text vector and the standard text vector is calculated, and when the loss value is less than a preset loss threshold, it is determined that the text vectorization model converges.
  • a python sentence with a data capture function may be used to obtain a pre-stored standard text vector from the database.
  • the embodiment of the present application may use a preset loss function to calculate the loss value between the predicted text vector and the standard text vector.
  • the loss function includes, but is not limited to, a cross-entropy loss function, a square error loss function, and Regular loss function.
  • the embodiment of the present application converts the input text into a semantic vector, which can realize the digitization of the text information, which is beneficial to improve the efficiency of subsequent analysis of the input text.
  • the intention prediction module 102 is configured to perform intention prediction on the semantic vector to obtain the predicted intention label of the input text.
  • the intention prediction module 102 is specifically configured to:
  • the fused semantic feature is used as the predicted intent label of the input text.
  • the embodiment of the application adopts an LSTM network (Long Short-Term Memory Net, long short-term memory network) to construct an intent prediction network including multiple down-sampling layers, and uses the multi-layer down-sampling layer structure in the LSTM network to perform semantic analysis.
  • the vector is down-sampled multiple times, which is beneficial to extract more accurate semantic features and improve the accuracy of the generated predicted intent label.
  • the pre-down-sampling layer and the post-down-sampling layer are relative terms.
  • the intention prediction network includes 4 down-sampling layers.
  • the second down-sampling layer that down-samples the semantic vector, the third down-sampling layer that down-samples the semantic vector, and the fourth down-sampling layer that down-samples the semantic vector are the pre-down-sampling layer ;
  • the second down-sampling layer that down-samples the standard semantic vector is a post-down-sampling layer relative to the first down-sampling layer that down-samples the semantic vector, and so on.
  • the later down-sampling layer down-samples the result (forward semantic feature) obtained by the previous down-sampling layer to obtain the backward semantic feature.
  • the later down-sampling layer down-samples the result (forward semantic feature) obtained by the previous down-sampling layer to obtain the backward semantic feature.
  • the thoracic cavity image is down-sampled in the first down-sampling layer to obtain the first semantic feature
  • Feature fusion is performed on the first semantic feature, the second semantic feature, the third semantic feature, and the fourth semantic feature to obtain a fused semantic feature, and the fused semantic feature is used as a predicted intention label of the input text.
  • the embodiment of the present application performs intention prediction on the semantic vector to obtain the predicted intention label of the input text, which can reduce the amount of data in the semantic vector containing a large amount of semantics, and is beneficial to improve the efficiency of subsequent analysis of user intent.
  • the feature extraction module 103 is configured to perform feature extraction on the predicted intent tag by using a feature extraction network, and generate multiple target intents based on the extracted features.
  • the feature extraction network includes multiple visual layers and multiple hidden layers, wherein the visual layer includes multiple visual units, the hidden layer includes multiple hidden units, and The number of visible layers corresponds to the number of multiple hidden layers, and the number of multiple visible units corresponds to the number of multiple hidden units.
  • the feature extraction module 103 is specifically configured to:
  • each visible unit in the visible layer of the feature extraction network is used to mark a data representation in the intent tag, and each hidden unit in the hidden layer of the feature extraction network is extracted.
  • each hidden unit in the hidden layer extracts a data characterization marked by a visible unit matching the hidden unit based on a machine learning algorithm.
  • the states of the visible unit and the hidden unit are represented by Boolean values, such as 0 and 1, where 0 represents an inactive state and 1 represents an activated state. Only after the visible unit and/or the hidden unit are activated by the activation function can the data contained in the visible unit be transmitted to the hidden unit matching the visible unit.
  • the activation function of the visible unit and/or the hidden unit is as follows:
  • E(v, h, ⁇ ) is the activation value
  • I is the number of visible units in the visible layer
  • J is the number of hidden units in the hidden layer
  • a is the bias of the visible layer Vector
  • b is the bias vector of the hidden layer
  • w is the direct weight matrix between the visible layer and the hidden layer
  • v is any visible unit in the visible layer
  • h is the hidden layer
  • ⁇ is the preset error parameter.
  • the visible unit and/or the hidden unit are activated by the activation function.
  • the data contained in the visible unit and/or the hidden unit is transmitted to the hidden unit matching the visible unit .
  • the visible unit in the visible layer is matched with the hidden unit in the hidden layer through the following matching algorithm:
  • P(v, h, ⁇ ) is a matching value
  • v is any visible unit in the visible layer
  • h is any hidden unit in the hidden layer
  • ⁇ is a preset error parameter
  • Z is The normalization factor of the feature extraction network, exp(-E(v, h, ⁇ )) is the expectation that the visible unit v matches the hidden unit h.
  • the activated visible layer can transmit data to and match the visible layer.
  • the hidden layer that has been activated is not limited to
  • v j is the jth hidden unit in the hidden layer
  • h is any hidden unit in the hidden layer
  • ⁇ is a preset error parameter
  • J is the number of hidden units in the hidden layer
  • w is the direct weight matrix between the visible layer and the hidden layer
  • b is the bias vector of the hidden layer
  • ⁇ is the preset probability coefficient
  • h i is the i-th visible unit in the visible layer
  • v is any visible unit in the visible layer
  • ⁇ is a preset error parameter
  • I is the visible unit in the visible layer.
  • w is the direct weight matrix between the visible layer and the hidden layer
  • a is the bias vector of the visible layer
  • ⁇ is the preset probability coefficient.
  • multiple visible layers and hidden layers in multiple feature extraction networks are used to superimpose, so as to achieve more accurate feature extraction of the predicted intent tag.
  • using the hidden layer of the feature extraction network to use a machine learning algorithm to perform feature extraction on the data representation marked by the visible layer includes:
  • h is the data feature obtained by feature extraction of the data representation of the visible layer mark
  • Y is the data representation
  • w is the weight matrix between the visible layer and the hidden layer
  • b is The bias vector of the hidden layer.
  • the generating multiple target intents based on the extracted features includes: calculating the similarity between the extracted features and multiple preset standard intents, and determining that the multiple standard intents with the similarity greater than the similarity threshold are the target intents.
  • the target intention is described, wherein the embodiment of the present application may use the cosine similarity algorithm to calculate the similarity between the extracted feature and a plurality of preset standard intentions.
  • the feature extraction network is used to perform feature extraction on the predicted intention label, and multiple target intentions are generated based on the extracted features, and the user intention is predicted by using the extracted features, which improves the accuracy of predicting user intentions.
  • the priority calculation module 104 is configured to calculate the priority of each target intention among the multiple target intentions.
  • the priority calculation module 104 is specifically configured to:
  • Pir is the priority
  • ⁇ is a preset weight coefficient
  • M k is the kth target intention among the multiple target intentions.
  • the intention screening module 105 is configured to select a preset number of target intentions as user intentions according to the priority.
  • the intention screening module 105 is specifically configured to:
  • a preset number of target intents are selected as user intents from the multiple target intents that have been sorted in a front-to-back order.
  • the multiple target intentions include: intention A, intention B, intention C, and intention D, where the priority of intention A is 50, the priority of intention B is 40, the priority of intention C is 60, and the priority of intention D is 60.
  • the priority of is 30. Therefore, the multiple target intentions are sorted as follows: Intention C, Intention A, Intent B, and Intent D in the order of priority; Select the intention C and the intention A as the user's intentions from the sorted multiple target intentions in the order of.
  • the embodiment of the application obtains the input text of the user, converts the input text into a semantic vector, and converts the input text into a semantic vector, which can realize the digitization of the text information, which is beneficial to improve the efficiency of subsequent analysis of the input text;
  • the vector is used to predict the intent to obtain the predicted intent label of the input text, which can reduce the amount of data in the semantic vector containing a large amount of semantics, which is beneficial to improve the efficiency and accuracy of subsequent analysis of user intent;
  • the feature extraction network is used to perform the prediction of the intent label.
  • the user intention analysis device proposed in the present application can solve the problem of low accuracy in recognizing user intentions.
  • FIG. 3 it is a schematic structural diagram of an electronic device that implements a user intention analysis method provided by an embodiment of the present application.
  • the electronic device 1 may include a processor 10, a memory 11, and a bus, and may also include a computer program stored in the memory 11 and running on the processor 10, such as a user intention analysis program 12.
  • the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, CD etc.
  • the memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, for example, a mobile hard disk of the electronic device 1.
  • the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), and a secure digital (Secure Digital) equipped on the electronic device 1. , SD) card, flash card (Flash Card), etc.
  • the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device.
  • the memory 11 can be used not only to store application software and various data installed in the electronic device 1, such as the code of the user intent analysis program 12, etc., but also to temporarily store data that has been output or will be output.
  • the processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or more Combinations of central processing unit (CPU), microprocessor, digital processing chip, graphics processor, and various control chips, etc.
  • the processor 10 is the control unit of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and runs or executes programs or modules (such as user Intention analysis program, etc.), and call data stored in the memory 11 to execute various functions of the electronic device 1 and process data.
  • the bus may be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc.
  • PCI peripheral component interconnect standard
  • EISA extended industry standard architecture
  • the bus can be divided into address bus, data bus, control bus and so on.
  • the bus is configured to implement connection and communication between the memory 11 and at least one processor 10 and the like.
  • FIG. 3 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure. Components, or a combination of certain components, or different component arrangements.
  • the electronic device 1 may also include a power source (such as a battery) for supplying power to various components.
  • the power source may be logically connected to the at least one processor 10 through a power management device, thereby controlling power
  • the device implements functions such as charge management, discharge management, and power consumption management.
  • the power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators.
  • the electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.
  • the electronic device 1 may also include a network interface.
  • the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.
  • the electronic device 1 may also include a user interface.
  • the user interface may be a display (Display) and an input unit (such as a keyboard (Keyboard)).
  • the user interface may also be a standard wired interface or a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc.
  • the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visualized user interface.
  • the user intention analysis program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:
  • a preset number of target intents are selected from the plurality of target intents as user intents according to the priority.
  • the integrated module/unit of the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the computer-readable storage medium may be volatile or non-volatile.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).
  • the present application also provides a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor of an electronic device, it can realize:
  • a preset number of target intents are selected from the plurality of target intents as user intents according to the priority.
  • modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.
  • the blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

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Abstract

L'invention concerne un procédé d'analyse d'intention d'utilisateur, un appareil, un dispositif électronique et un support de stockage lisible par ordinateur, ledit procédé consistant à : obtenir un texte d'entrée d'un utilisateur, et convertir ledit texte d'entrée en un vecteur sémantique (S1) ; réaliser une prédiction d'intention sur le vecteur sémantique pour obtenir une étiquette d'intention prédite du texte d'entrée (S2) ; utiliser un réseau d'extraction de caractéristiques pour effectuer une extraction de caractéristiques sur l'étiquette d'intention prédite, et générer une pluralité d'intentions cibles selon les caractéristiques extraites (S3) ; calculer la priorité de chaque intention cible parmi la pluralité d'intentions cibles (S4) ; selon ladite priorité, sélectionner comme intentions d'utilisateur un nombre prédéfini d'intentions cibles parmi la pluralité d'intentions cibles (S5). L'invention concerne également une technologie de chaîne de blocs ; le texte d'entrée de l'utilisateur peut être stocké dans un nœud de la chaîne de blocs. Le procédé améliore la précision de reconnaissance d'intention d'un utilisateur.
PCT/CN2021/082893 2020-11-19 2021-03-25 Procédé d'analyse d'intention d'utilisateur, appareil, dispositif électronique et support de stockage informatique WO2021208696A1 (fr)

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