WO2020232864A1 - Procédé de traitement de données et appareil associé - Google Patents

Procédé de traitement de données et appareil associé Download PDF

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WO2020232864A1
WO2020232864A1 PCT/CN2019/102348 CN2019102348W WO2020232864A1 WO 2020232864 A1 WO2020232864 A1 WO 2020232864A1 CN 2019102348 W CN2019102348 W CN 2019102348W WO 2020232864 A1 WO2020232864 A1 WO 2020232864A1
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text
text data
type
data
preset
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PCT/CN2019/102348
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Chinese (zh)
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郭鸿程
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/34—Browsing; Visualisation therefor
    • G06F16/345—Summarisation for human users
    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/14—Image acquisition
    • G06V30/148—Segmentation of character regions
    • G06V30/153—Segmentation of character regions using recognition of characters or words
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition

Definitions

  • This application relates to the field of intelligent decision-making, and in particular to a data processing method and related devices.
  • the method for parents or teachers to check the reading effect is to confirm through homework.
  • children or students often need to do after-school exercises after reading, and parents or teachers pass after-class Practice to test the effect of reading.
  • the embodiments of the present application provide a data processing method and related devices to improve the efficiency of checking reading effects.
  • the first aspect of this application provides a data processing method, including:
  • the summary vector of the text data is input to a neural network decoder to obtain a summary of the text data, wherein the neural network decoder is used to predict the summary vector of the text data through a neural network to obtain multiple predictions Words, the plurality of predicted words are connected as a summary of the text data;
  • a neural network semantic representation model is used to calculate the degree of semantic relevance between the question of the text data and the text in the text data, and the text with the highest degree of semantic relevance is determined as the answer corresponding to the question of the text data.
  • the second aspect of the present application provides a data processing device, including:
  • the acquisition module is used to acquire the image data of the book sent by the terminal;
  • a character recognition module for performing character recognition processing on the image data to obtain text data corresponding to the image data
  • the detection module is configured to perform text type detection on the text data to determine whether the text type of the text data meets the preset text type;
  • the encoding module is used to input the text data into a neural network encoder to obtain a summary vector of the text data when the text type meets the preset text type, wherein the neural network encoder is used to The text data is compressed and encoded;
  • the decoding module is configured to input the summary vector of the text data into a neural network decoder to obtain a summary of the text data, wherein the neural network decoder is used to predict the summary vector of the text data through a neural network Obtaining a plurality of predicted words, and the plurality of predicted words are connected as a summary of the text data;
  • the extraction module is configured to perform word segmentation processing on the abstract of the text data, and extract N keywords in the abstract of the text data in the order of word frequency from large to small, where N is a positive integer;
  • a combination module configured to classify the N keywords by part of speech, and combine the N keywords according to the part of speech of the N keywords in a preset question sentence order to obtain the text data question;
  • the processing module is used to calculate the semantic correlation degree between the text data question and the text in the text data through the neural network semantic representation model, and determine the text with the highest semantic correlation degree as the answer corresponding to the text data question.
  • a third aspect of the present application provides an electronic device for data processing.
  • the electronic device includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory , And configured to be executed by the processor, and the program includes instructions for executing the steps in any method of the first aspect of the present application.
  • the fourth aspect of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the part described in any method of the first aspect of the present application Or all steps.
  • FIG. 1 is a flowchart of a data processing method provided by an embodiment of this application.
  • FIG. 3 is a flowchart of another data processing method provided by an embodiment of the application.
  • FIG. 4 is a schematic diagram of a system structure provided by an embodiment of this application.
  • FIG. 5 is a schematic diagram of performing character recognition processing on image data according to an embodiment of the application.
  • FIG. 6 is a schematic diagram of a data processing device provided by an embodiment of this application.
  • FIG. 7 is a schematic structural diagram of an electronic device in a hardware operating environment involved in an embodiment of the application.
  • the data processing method and related devices provided in the embodiments of the present application can improve the efficiency of checking the reading effect.
  • the artificial intelligence server obtains the image data sent by the terminal, then processes the image data to obtain text data corresponding to the image data, and then processes the text data to obtain a summary of the text data, text data problems, and The answers to the text data questions are returned to the terminal.
  • FIG. 1 is a flowchart of a data processing method according to an embodiment of the application.
  • a data processing method provided by an embodiment of the present application may include:
  • the terminal can be a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a mobile Internet device, or other types of terminals.
  • the paper books are scanned first to obtain scanned images of the paper books, and then the terminal sends the scanned images to the artificial intelligence server.
  • the image data is a scanned image
  • the scanned image is scanned and generated by a scanning tool
  • the method for standardizing the image data can be:
  • the image data is processed by an image correction algorithm, where the image correction algorithm includes any one of Radon algorithm, Hough transform, and linear regression algorithm.
  • the image data is processed by an image enhancement algorithm, where the image enhancement algorithm includes any one of histogram equalization, image smoothing, and image sharpening.
  • the image data is processed through an image correction algorithm and an image enhancement algorithm.
  • an artificial intelligence server is required to perform character recognition processing on the image data to obtain text data corresponding to the image data, and the text data can be directly recognized.
  • the method for the artificial intelligence server to perform character recognition processing on the image data to obtain the text data corresponding to the image data may be:
  • Character cutting is performed on the image data to obtain M characters, where M is a positive integer.
  • Perform feature extraction on M characters to obtain M character features, where M characters correspond to M character features one-to-one.
  • the text type includes language type and style type
  • language type includes Chinese, English, Japanese, etc.
  • style type includes modern style (including novel, prose, fairy tale, narrative, explanatory, argumentative, etc.) and ancient style (Including poems, words, songs, fu etc.).
  • the method for the artificial intelligence server to perform text type detection on the text data to determine whether the text type of the text data meets the preset text type may be:
  • Performing language type detection on the text data to obtain the language type of the text data and performing style type detection on the text data to obtain the style type of the text data.
  • the language type of the text data satisfies the preset language type and the style type of the text data satisfies the preset style type
  • the preset style includes modern style.
  • the language type of the text data does not meet the preset language type, or the style type of the text data does not meet the preset style type, or the language type of the text data does not meet the preset language type and the style of the text data
  • the type does not meet the preset text type, it is determined that the text type of the text data does not meet the preset text type.
  • the method includes:
  • the artificial intelligence server sends a language type error message to the terminal, where the language type error message is used to instruct the terminal to generate a pop-up window or interface prompting that the language type of the book is wrong. For example, if the artificial intelligence server recognizes that the language type of the text data sent by the terminal is English, the artificial intelligence server sends a language type error message to the terminal, and when the terminal receives the language type error message, it generates a pop-up window indicating that the language type of the book cannot be English Or interface.
  • a stylistic type error message is sent to the terminal, where the stylistic type error message is used to instruct the terminal to generate a pop-up window or interface indicating that the book’s stylistic type is wrong, for example, manual
  • the smart server recognizes that the style type of the text data sent by the terminal is ancient style, then the artificial intelligence server sends a style type error message to the terminal.
  • the terminal receives the style type error message, it generates a pop-up window indicating that the style of the book cannot be ancient style or interface.
  • a language and style type error message is sent to the terminal, where the language and style type error message is used to indicate the terminal Generate a pop-up window or interface prompting that the language and style of the book are wrong.
  • the artificial intelligence server recognizes that the language type of the text data sent by the terminal is Japanese, and the style of the image data is ancient style, the artificial intelligence server sends to the terminal Language and style type error messages.
  • the terminal receives the language and style type error messages, it generates a pop-up window or interface that prompts that the language type of the book cannot be Japanese and the style type of the book cannot be ancient style.
  • the neural network encoder includes the first recurrent neural network
  • the method of inputting text data into the neural network encoder to obtain the summary vector of the text data may be:
  • the first text in the text data is input into the first recurrent neural network to obtain the first encoding vector; the first encoding vector is passed into the next moment; the first encoding vector and the second in the text data are sent to the next moment
  • the text is input into the first recurrent neural network to obtain the second encoding vector; the second encoding vector is passed into the next moment, until all the text in the text data is input into the first recurrent neural network, and the final encoding vector is determined to be Abstract vector of text data.
  • the neural network encoder is used to compress and encode the text data, and is implemented by a recurrent neural network (RNN).
  • the neural network encoder receives the input text data, and inputs the words in the original text data into the neural network at the beginning , Compress this word into a vector, and then pass the compressed vector to the next moment.
  • the code vector obtained after compressing all the text data is the summary vector of the text data.
  • the neural network decoder includes a second recurrent neural network
  • the method of inputting the summary vector of the text data into the neural network decoder to obtain the summary of the text data may be: input the summary vector of the text data into the first Second recurrent neural network to predict the first output text; pass the first output text into the next moment; at the next moment, input the summary vector of the first output text and text data into the second recurrent neural network to predict the second Output text; the second output text is passed into the next moment until the second recurrent neural network predicts the summary vector of the text data, and the final combination of all output texts is determined as the summary of the text data.
  • the neural network decoder is used to decode the summary vector of the text data, and is also implemented by a recurrent neural network (RNN).
  • RNN recurrent neural network
  • the neural network decoder After the summary vector of the text data is input to the neural network decoder, the neural network decoder The summary vector of the data is predicted to get the output word at one moment, and then the neural network decoder predicts the output word at the next moment according to the output word and summary vector at that moment, and so on, the output word at the previous moment will affect The next output word and all the output words obtained by the neural network decoder are connected together to form the summary of the text data.
  • performing word segmentation processing on the abstract of the text data, and extracting the N keywords in the abstract of the text data in the order of word frequency in descending order may be:
  • the word segmentation method for the abstract of the text data includes a word segmentation method based on string matching, a word segmentation method based on understanding, and a word segmentation method based on statistics.
  • the word segmentation method based on string matching is to match the Chinese character string to be segmented with an entry in a dictionary according to a certain strategy. If a string is found in the dictionary, the matching is successful, that is, a word is recognized.
  • the word segmentation method based on comprehension achieves the effect of word recognition by letting the computer simulate human's understanding of the sentence.
  • the statistical-based word segmentation method should use the basic word segmentation dictionary for string matching and segmentation, and at the same time use statistical methods to identify some new words, that is, the combination of string frequency statistics and string matching, which not only exerts the characteristics of fast matching segmentation speed and high efficiency, It also uses the advantages of no dictionary word segmentation combined with context to identify new words and automatically eliminate ambiguity.
  • the problem of calculating the text data through the neural network semantic representation model and the semantic correlation degree of the text in the text data include:
  • the method for calculating the degree of semantic relevance between the question of the text data and the text in the text data may be a vocabulary overlap method, a string method, a cosine similarity method or a maximum common subsequence method.
  • the specific process is to search for Q segments of text matching the N keywords in the text data, where Q is a positive integer.
  • the question of calculating the text data is related to the Q semantic relevance degrees of the Q segment text, where the Q segment text corresponds to the Q semantic relevance degrees one-to-one. Obtain the highest first semantic relevance degree among the Q semantic relevance degrees, and determine that the text corresponding to the first semantic relevance degree is the answer corresponding to the question of the text data.
  • FIG. 2 is a flowchart of another data processing method provided by another embodiment of the application.
  • another data processing method provided by another embodiment of the present application may include:
  • the terminal sends the image data of the book to the artificial intelligence server.
  • the terminal can be a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a mobile Internet device, or other types of terminals.
  • the paper books are scanned first to obtain scanned images of the paper books, and then the terminal sends the scanned images to the artificial intelligence server.
  • the artificial intelligence server performs character recognition processing on the image data to obtain text data corresponding to the image data.
  • the image data is a scanned image
  • the scanned image is scanned and generated by a scanning tool
  • the method for standardizing the image data can be:
  • the image data is processed by an image correction algorithm, where the image correction algorithm includes any one of Radon algorithm, Hough transform, and linear regression algorithm.
  • the image data is processed by an image enhancement algorithm, where the image enhancement algorithm includes any one of histogram equalization, image smoothing, and image sharpening.
  • the image data is processed through an image correction algorithm and an image enhancement algorithm.
  • an artificial intelligence server is required to perform character recognition processing on the image data to obtain text data corresponding to the image data, and the text data can be directly recognized.
  • Character cutting is performed on the image data to obtain M characters, where M is a positive integer.
  • the artificial intelligence server recognizes whether the language type of the text data meets the preset language type.
  • the language types include Chinese, English, Japanese, etc.
  • the preset language types include Chinese.
  • the artificial intelligence server recognizes whether the style type of the text data meets the preset style type.
  • the stylistic types include modern styles (including novels, prose, fairy tales, narratives, explanatory essays, argumentative essays, etc.) and ancient styles (including poems, words, songs, fu, etc.), and the preset styles include modern styles.
  • the artificial intelligence server sends a language and style type error message to the terminal.
  • the terminal generates a pop-up window or interface prompting that the language and style of the book are wrong.
  • the artificial intelligence server recognizes that the language type of the text data is Japanese and the style is ancient style, then the artificial intelligence server sends a language and style type error message to the terminal, and when the terminal receives the language and style type error message, it generates a language that prompts the book
  • the type cannot be Japanese and the style cannot be the pop-up window or interface of the ancient style.
  • FIG. 3 is a flowchart of another data processing method provided by another embodiment of the application.
  • another data processing method provided by another embodiment of the present application may include:
  • the terminal sends the image data of the book to the artificial intelligence server.
  • the terminal can be a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a mobile Internet device, or other types of terminals.
  • the books read by children or students are paper books.
  • the paper books are scanned through the terminal to obtain scanned images of the paper books, and then the terminal sends the scanned images to the artificial intelligence server.
  • the artificial intelligence server processes the image data by using an image correction algorithm.
  • the image correction algorithm is required to The image data is processed, and the image correction algorithm includes any one of Radon algorithm, Hough transform and linear regression algorithm.
  • the artificial intelligence server processes the image data by using an image enhancement algorithm.
  • the image enhancement algorithm includes any of histogram equalization, image smoothing, and image sharpening.
  • the artificial intelligence server performs character cutting on the image data to obtain M characters, where M is a positive integer.
  • the artificial intelligence server performs feature extraction on the M characters to obtain M character features.
  • M characters correspond to M character features one-to-one, and feature extraction can be divided into two categories: one is statistical features, the ratio of the number of black points or the number of white points in the character area of the image data is obtained, when the character area is divided into When there are several areas, the black point ratio or white point ratio of each area is combined into a numerical vector of space, and the other type is structural feature.
  • the strokes of the characters are obtained The number and location of endpoints and intersections.
  • the artificial intelligence server compares the M character features with the character feature database to identify M text characters corresponding to the M character features.
  • M character features correspond to M text characters one-to-one.
  • the comparison methods include the comparison method of Euclidean space, relaxation comparison method (Relaxation), dynamic programming comparison method (Dynamic Programming, DP), neural Network database establishment and comparison method, HMM (Hidden Markov Model) and other methods.
  • the artificial intelligence server combines M text characters to obtain text data corresponding to the image data.
  • the artificial intelligence server performs text type detection on the text data to determine whether the text type of the text data meets the preset text type.
  • the text type includes language type and style type
  • language type includes Chinese, English, Japanese, etc.
  • style type includes modern style (including novel, prose, fairy tale, narrative, explanatory, argumentative, etc.) and ancient style (Including poems, words, songs, fu, etc.).
  • the method for the artificial intelligence server to perform text type detection on the text data to determine whether the text type of the text data meets the preset text type may be:
  • the language type of the text data satisfies the preset language type and the style type of the text data satisfies the preset style type
  • the preset style includes modern style.
  • the language type of the text data does not meet the preset language type, or the style type of the text data does not meet the preset style type, or the language type of the text data does not meet the preset language type and the style of the text data
  • the type does not meet the preset text type, it is determined that the text type of the text data does not meet the preset text type.
  • the neural network encoder is used to compress and encode the text data, which is implemented by a recurrent neural network (RNN).
  • the neural network encoder receives the input text data, and inputs the words in the original text data into the neural network at the beginning. Compress this word into a vector, and then pass the compressed vector to the next moment. In the next moment, input the compressed vector at the previous moment and the word in the original text data to the neural network, and then transfer the compressed new vector
  • the code vector obtained after compressing all the text data is the summary vector of the text data.
  • the neural network decoder is used to decode the summary vector of the text data, and it is also implemented by a recurrent neural network (RNN).
  • RNN recurrent neural network
  • the neural network decoder Predicts the output word at a moment by using the summary vector of the, and then the neural network decoder predicts the output word at the next moment according to the output word and summary vector at that moment, and so on, the output word at the previous moment will affect the next An output word, and finally all the output words obtained by the neural network decoder are connected to form a summary of the text data.
  • the method for extracting the N keywords in the abstract of the text data may be:
  • the word segmentation method for the abstract of the text data includes a word segmentation method based on string matching, a word segmentation method based on understanding, and a word segmentation method based on statistics.
  • the word segmentation method based on string matching is to match the Chinese character string to be segmented with an entry in a dictionary according to a certain strategy. If a string is found in the dictionary, the matching is successful, that is, a word is recognized.
  • the word segmentation method based on comprehension achieves the effect of word recognition by letting the computer simulate human's understanding of the sentence.
  • the statistical-based word segmentation method should use the basic word segmentation dictionary for string matching and segmentation, and at the same time use statistical methods to identify some new words, that is, the combination of string frequency statistics and string matching, which not only exerts the characteristics of fast matching segmentation speed and high efficiency, It also uses the advantages of no dictionary word segmentation combined with context to identify new words and automatically eliminate ambiguity.
  • the problem of calculating the text data through the neural network semantic representation model and the semantic correlation degree of the text in the text data include:
  • the method for calculating the degree of semantic relevance between the question of the text data and the text in the text data may be a vocabulary overlap method, a string method, a cosine similarity method or a maximum common subsequence method.
  • the specific process is to search for Q segments of text matching the N keywords in the text data, where Q is a positive integer.
  • the question of calculating the text data is related to the Q semantic relevance degrees of the Q segment text, where the Q segment text corresponds to the Q semantic relevance degrees one-to-one.
  • the system includes an artificial intelligence server and a terminal.
  • the artificial intelligence server communicates with the terminal.
  • the terminal includes a mobile phone and a computer.
  • the user accesses the artificial intelligence server through the terminal.
  • the terminal is a mobile phone
  • the user can use the mobile phone Take photos of the books to be processed, send the photos to the artificial intelligence server, the artificial intelligence server processes the photos, obtains the processing results, and then returns the processing results to the user’s mobile phone.
  • the terminal is a computer
  • the user can connect to the computer through Scanning equipment, such as printers, scans the book, and then sends the scanned image to the artificial intelligence server.
  • the artificial intelligence server processes the scanned image to obtain the processing result, and then returns the processing result to the user's computer.
  • FIG. 5 is a schematic diagram of performing character recognition processing on image data according to an embodiment of the application.
  • the image data is displayed as ABCDE.
  • the image data is cut into characters, and five characters can be obtained, namely A, B, C, D, and E, and then feature extraction of the obtained characters ,
  • feature a, feature b, feature c, feature d, and feature e are feature a, feature b, feature c, feature d, and feature e.
  • the features compare and recognize to determine the text characters corresponding to the features, which are text character A, text
  • the character B, the text character C, the text character D and the text character E are obtained, all the text characters are combined to obtain the text ABCDE.
  • FIG. 6 is a schematic diagram of a data processing apparatus provided by another embodiment of the application.
  • a data processing apparatus provided by another embodiment of the present application may include:
  • the obtaining module 601 is used to obtain image data of books sent by the terminal;
  • the character recognition module 602 is configured to perform character recognition processing on the image data to obtain text data corresponding to the image data;
  • the detection module 603 is configured to perform text type detection on the text data to determine whether the text type of the text data meets the preset text type;
  • the encoding module 604 is configured to input the text data into a neural network encoder to obtain a summary vector of the text data when the text type meets the preset text type, wherein the neural network encoder is used to Compressing and encoding the text data;
  • the decoding module 605 is configured to input the summary vector of the text data into a neural network decoder to obtain a summary of the text data, wherein the neural network decoder is used to predict the summary vector of the text data through a neural network To obtain a plurality of predicted words, and the plurality of predicted words are connected as a summary of the text data;
  • the extraction module 606 is configured to perform word segmentation processing on the abstract of the text data, and extract N keywords in the abstract of the text data in the order of word frequency from large to small, where N is a positive integer;
  • the combination module 607 is configured to classify the N keywords by part of speech, and combine the N keywords according to the part of speech of the N keywords according to a preset question order to obtain the text data question;
  • the processing module 608 is configured to calculate the degree of semantic relevance between the question of the text data and the text in the text data through the neural network semantic representation model, and determine the text with the highest degree of semantic relevance as the answer corresponding to the question of the text data.
  • FIG. 7 is a schematic structural diagram of an electronic device in a hardware operating environment involved in an embodiment of the application.
  • the electronic device of the hardware operating environment involved in the embodiment of the present application may include:
  • the processor 701 is, for example, a CPU.
  • the memory 702 may be a high-speed RAM memory, or a stable memory, such as a disk memory.
  • the communication interface 703 is used to implement connection and communication between the processor 701 and the memory 702.
  • FIG. 7 does not constitute a limitation on the data processing electronic device, and may include more or less components than shown in the figure, or a combination of certain components , Or different component arrangements.
  • the memory 702 may include an operating system, a network communication module, and data processing programs.
  • the operating system is a program that manages and controls the hardware and software resources of an electronic device for data processing, a program that supports data processing, and the operation of other software or programs.
  • the network communication module is used to implement communication between various components in the memory 702, and communication with other hardware and software in the data processing electronic device.
  • the processor 701 is configured to execute the data processing program stored in the memory 702, and implement the following steps:
  • the summary vector of the text data is input to a neural network decoder to obtain a summary of the text data, wherein the neural network decoder is used to predict the summary vector of the text data through a neural network to obtain multiple predictions Words, the plurality of predicted words are connected as a summary of the text data;
  • a neural network semantic representation model is used to calculate the degree of semantic correlation between the question of the text data and the text in the text data, and the text with the highest degree of semantic correlation is determined as the answer corresponding to the question of the text data.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, and the computer program is processed. Execute to achieve the following steps:
  • the summary vector of the text data is input to a neural network decoder to obtain a summary of the text data, wherein the neural network decoder is used to predict the summary vector of the text data through a neural network to obtain multiple predictions Words, the plurality of predicted words are connected as a summary of the text data;
  • a neural network semantic representation model is used to calculate the degree of semantic relevance between the question of the text data and the text in the text data, and the text with the highest degree of semantic relevance is determined as the answer corresponding to the question of the text data.

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Abstract

La présente invention se rapporte au domaine des décisions intelligentes. L'invention concerne un procédé de traitement de données et un appareil associé. Le procédé de traitement de données consiste à : acquérir des données d'image, d'un livre, envoyées par un terminal; effectuer un traitement d'identification de caractères sur les données d'image pour obtenir des données de texte correspondant aux données d'image; exécuter une détection de type de texte sur les données de texte afin de déterminer si un type de texte des données de texte satisfait ou non un type de texte prédéfini; lorsque le type de texte satisfait le type de texte prédéfini, entrer les données de texte dans un codeur de réseau neuronal pour obtenir un vecteur de résumé des données de texte; entrer le vecteur de résumé des données de texte dans un décodeur de réseau neuronal pour obtenir un résumé des données de texte; extraire N mots-clés dans le résumé des données de texte; combiner les N mots-clés pour obtenir une question des données de texte; et déterminer une réponse correspondant à la question des données de texte au moyen d'un modèle de représentation sémantique de réseau neuronal. La solution technique des modes de réalisation de la présente invention améliore l'efficacité de vérification de l'effet de lecture.
PCT/CN2019/102348 2019-05-20 2019-08-23 Procédé de traitement de données et appareil associé WO2020232864A1 (fr)

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