WO2020258506A1 - Procédé et appareil de détection de degré de correspondance d'informations de texte, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de détection de degré de correspondance d'informations de texte, dispositif informatique et support de stockage Download PDF

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WO2020258506A1
WO2020258506A1 PCT/CN2019/103650 CN2019103650W WO2020258506A1 WO 2020258506 A1 WO2020258506 A1 WO 2020258506A1 CN 2019103650 W CN2019103650 W CN 2019103650W WO 2020258506 A1 WO2020258506 A1 WO 2020258506A1
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text information
feature vector
vector
similarity
preset
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PCT/CN2019/103650
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English (en)
Chinese (zh)
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金戈
徐亮
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平安科技（深圳）有限公司
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Publication of WO2020258506A1 publication Critical patent/WO2020258506A1/fr

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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
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis
    • 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
    • G06F16/35—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures

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  • This application relates to the field of computer technology, and in particular to a method, device, computer equipment and non-volatile storage medium for detecting matching degree of text information.
  • Text matching degree refers to the degree of semantic relevance between different texts.
  • the determination of text matching degree is one of the core tasks of text mining and text retrieval. Therefore, how to better detect text matching degree has always been of great concern to those skilled in the art. The problem.
  • the main method for detecting text matching degree in the prior art is: mapping the text to a vector in the word space, and calculating the Euclidean distance or the cosine distance between the vectors.
  • the inventor realized that the existing text matching degree detection method only determines the text similarity in the word space, and does not consider the association and semantic information between text features, so the matching degree detection is not accurate enough.
  • the purpose of this application is to provide a text information matching degree detection method, device, computer equipment and readable non-volatile storage medium, so that the text information matching degree detection is more accurate.
  • the present application provides a method for detecting matching degree of text information.
  • the method includes: acquiring object text information and its corresponding reference text information; and converting the object text information into the first text information according to a preset self-encoding structure.
  • a implicit feature vector, and converting the reference text information into a second implicit feature vector wherein, the first implicit feature vector is used to represent feature information of the object text information; the second implicit feature vector The feature vector is used to represent the feature information of the reference text information; calculate the vector similarity between the first implicit feature vector and the second implicit feature vector; according to the object text information and the preset key
  • the word acquisition logistic regression model, the vector similarity is input into the logistic regression model, and the matching degree of the target text information between the target text information and the reference text information is obtained.
  • this application also provides a text information matching degree detection device.
  • the device includes: a text information acquisition module for acquiring object text information and its corresponding reference text information; a text information conversion module for The object text information is converted into a first implicit feature vector according to a preset self-encoding structure, and the reference text information is converted into a second implicit feature vector; wherein, the first implicit feature vector is used to represent The feature information of the object text information; the second implicit feature vector is used to represent feature information of the reference text information; the vector similarity acquisition module is used to calculate the first implicit feature vector and the first implicit feature vector 2.
  • the vector similarity between implicit feature vectors is used to obtain a logistic regression model according to the object text information and preset keywords, and input the vector similarity into the logistic regression model to obtain The degree of matching of the object text information between the object text information and the reference text information.
  • the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor implements a method for detecting the matching degree of text information when the computer program is executed.
  • the method for detecting the matching degree of the text information includes: obtaining object text information and its corresponding reference text information; converting the object text information into a first implicit feature vector according to a preset self-encoding structure, and converting the reference text information Is a second implicit feature vector; wherein, the first implicit feature vector is used to represent feature information of the object text information; the second implicit feature vector is used to represent feature information of the reference text information; Calculate the vector similarity between the first implicit feature vector and the second implicit feature vector; obtain a logistic regression model according to the object text information and preset keywords, and input the vector similarity into the The logistic regression model is used to obtain the matching degree of the object text information between the object text information and the reference text information.
  • the present application also provides a computer-readable non-volatile storage medium, on which a computer program is stored, and when the computer program is executed by a processor, a method for detecting matching degree of text information is implemented.
  • the text information matching degree detection method includes: acquiring object text information and its corresponding reference text information; converting the object text information into a first implicit feature vector according to a preset self-encoding structure, and converting the reference text information into A second implicit feature vector; wherein, the first implicit feature vector is used to represent feature information of the object text information; the second implicit feature vector is used to represent feature information of the reference text information; calculation The vector similarity between the first implicit feature vector and the second implicit feature vector; obtaining a logistic regression model according to the object text information and preset keywords, and inputting the vector similarity into the Logistic regression model to obtain the matching degree of the object text information between the object text information and the reference text information.
  • the present application provides a text information matching degree detection method, device, computer equipment, and non-volatile storage medium.
  • the vector similarity between the implicit semantic features between the object text information and the reference text information is input to the object
  • the logistic regression model corresponding to the text information can effectively improve the accuracy of the text information matching degree detection.
  • FIG. 1 is an application environment diagram of a method for detecting matching degree of text information in an embodiment
  • FIG. 2 is a schematic flowchart of a method for detecting matching degree of text information in an embodiment
  • FIG. 3 is a schematic flowchart of a method for detecting matching degree of text information in another embodiment
  • Figure 4 is a structural block diagram of a text information matching degree detection device in an embodiment
  • Fig. 5 is an internal structure diagram of a computer device in an embodiment.
  • the text information matching degree detection method provided in this application can be applied to the application environment shown in Figure 1.
  • the server in the figure can be implemented by a computer device.
  • the computer device includes a processor, a memory, and a network connected by a device bus. Interface and database.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the database of the computer device is used to store the data involved in the detection of the matching degree of text information.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the server obtains the object text information and its corresponding reference text information; the server converts the object text information into a first implicit feature vector, and converts the reference text information into a second implicit feature vector; the server calculates The vector similarity between the first implicit feature vector and the second implicit feature vector; the server obtains a logistic regression model according to the object text information and preset keywords, and inputs the vector similarity into the office
  • the logistic regression model is used to obtain the matching degree of the object text information between the object text information and the reference text information.
  • a method for detecting the matching degree of text information is provided.
  • the method is applied to the server in FIG. 1 as an example for description, including the following steps:
  • Step S201 Obtain object text information and its corresponding reference text information.
  • the object text information may be the answer text of the matching degree to be detected;
  • the reference text information may be the question text and standard text corresponding to the answer text.
  • the user s answer to the question is the target text information
  • the reference text information is the question and the standard answer corresponding to the question
  • the matching degree between the target text information and the reference text information is detected, that is, the answer and the question and The process of semantic relevance between standard answers.
  • the method further includes:
  • A1. Obtain a training feature vector associated with the object text information.
  • the text information can be transformed into implicit feature vectors through the self-encoding structure; among them, the self-encoding structure is a kind of neural network, which encodes the features of the input self-encoding structure, and then decodes, so that the input and output are different minimize.
  • the self-encoding structure is a kind of neural network, which encodes the features of the input self-encoding structure, and then decodes, so that the input and output are different minimize.
  • A3 Calculate the information loss of each training self-encoding structure, and select the training self-encoding structure with the smallest amount of information loss as the preset self-encoding structure.
  • the training process of the self-encoding structure is the process of minimizing the difference between input and output.
  • the training feature vector is input into multiple different self-encoding structures.
  • the difference between the different self-encoding structures lies in the number of hidden layers and the hidden layer.
  • adjust the parameters of multiple auto-encoding structures to minimize the difference between the output of each encoding structure and the training feature vector.
  • the difference value of the input and output of each training auto-encoding structure from multiple training auto-encoding structures Select the target self-encoding structure.
  • Step S202 Convert the object text information into a first implicit feature vector, and convert the reference text information into a second implicit feature vector.
  • the implicit feature vector is the feature vector obtained by encoding the features of the input self-encoding structure, which retains a large amount of information of the input vector of the original input self-encoding structure, and is used to represent the object text information of the input self-encoding structure and Refer to the feature information of the text information; the self-encoding structure decodes and restores the implicit feature vector to obtain the output feature code.
  • converting the object text information into a first implicit feature vector may include:
  • the preset learning algorithm is an algorithm for converting text into a corresponding vector.
  • the object text information is converted into an object input vector in the form of a bag of words model feature through the sklearn library in Python; where, Python is a computer programming language; sklearn, also known as scikit-learn, is a python-based machine learning library that can facilitate the implementation of machine learning algorithms, including: classification, regression, clustering, dimensionality reduction, model selection and Data mining related algorithms such as preprocessing.
  • the existing text 1 “I like to eat apples, apples are rich in nutrition”
  • the text 2 “I like to eat pears”
  • the sklearn library uses the sklearn library to establish the features of the bag of words model (features will include “I”, “like”, “eat”, “apple”, “nutrition”, “rich”, and “pear"), and determine each sample according to the frequency of word occurrence
  • the feature value of can be obtained, the feature vector of text one (1,1,1,2,1,1,0), the feature vector of text two is (1,1,1,0,0,0,1)) .
  • the jieba library is a Python Chinese word segmentation library.
  • the reference text information includes question text information and standard text information corresponding to the object text information;
  • the second implicit feature vector includes a question implicit feature vector and a standard implicit feature vector; for step S202, all The conversion of the reference text information into the second implicit feature vector includes:
  • the object text information and the reference text information are respectively converted into object input vectors and reference input vectors through a preset learning algorithm; then the object input vectors and reference input vectors are respectively input into the preset self-encoding structure and extracted from The first implicit feature vector corresponding to the object input vector and the second implicit feature vector corresponding to the reference input vector in the coding structure can effectively extract the implicit semantic features between the object text information and the reference text information.
  • Step S203 Calculate the vector similarity between the first implicit feature vector and the second implicit feature vector.
  • the calculation of vector similarity is usually to calculate the distance between two vectors. The closer the distance, the greater the similarity.
  • the cosine similarity calculation method can be used to calculate the first implicit feature vector and the said The second implied vector similarity between feature vectors.
  • the vector similarity includes question similarity and standard similarity; in step S203, calculating the vector similarity between the first implicit feature vector and the second implicit feature vector includes :
  • C2 Calculate the cosine of the angle between the first implicit feature vector and the standard implicit feature vector to obtain the standard similarity.
  • the cosine similarity calculation method is also called cosine similarity, which evaluates their similarity by calculating the cosine value of the angle between two vectors; the cosine value of an angle of 0 degrees is 1, while the cosine value of any other angle is not It is greater than 1, and its minimum value is -1, so the cosine of the angle between the two vectors determines whether the two vectors are roughly pointing in the same direction.
  • the cosine similarity value When two vectors have the same direction, the cosine similarity value is 1; when the angle between the two vectors is 90°, the cosine similarity value is 0; when the two vectors point in completely opposite directions, the cosine similarity value is Is -1; cosine similarity is usually used in positive space, so the value given is between 0 and 1.
  • Step S204 Obtain a logistic regression model according to the object text information and preset keywords, and input the vector similarity into the logistic regression model to obtain object text information between the object text information and the reference text information The matching degree.
  • the parameters of the logistic regression model are calculated through the object text information and preset keywords, and then the vector similarity is input into the logistic regression model, and a matching value value is output.
  • a series of parameters are calculated according to the answer text of the user's answer and preset keywords, and the corresponding logistic regression model is established based on the obtained parameters, and then the similarity between the answer text and the reference text is input into the logistic regression Model, you can get a matching score.
  • obtaining a logistic regression model according to the target text information and preset keywords in step S204 includes:
  • S420 Set the keyword similarity and the vector similarity as parameters of a preset initial regression model to obtain the logistic regression model corresponding to the object text information.
  • step S410 acquiring the keyword similarity between the preset keyword and the object text information includes:
  • D1 Calculate the information value of each keyword in the preset keyword library, and select keywords whose information value is greater than a preset threshold as the preset keywords;
  • D2 Split the object text information to obtain multiple object words, and calculate the similarity between the preset keywords and the object words;
  • the keyword with the greater information value indicates that the keyword can judge the semantic relevance of the target text information. For example, calculate the ten keywords with the highest information value in the preset thesaurus. These ten keywords are calculated for similarity with multiple target words, and then the target word with the highest similarity in the target text is selected to obtain the final ten similarity values. The ten similarity values and the vector The similarity is used as a parameter of the logistic regression model together.
  • the above method for detecting matching degree of text information is to obtain object text information and its corresponding reference text information; convert the object text information into a first implicit feature vector, and convert the reference text information into a second implicit feature Vector; calculating the vector similarity between the first implicit feature vector and the second implicit feature vector, which can effectively extract and match the implicit semantic features between the target text information and the reference text information;
  • the object text information and preset keywords are used to obtain a logistic regression model, and the vector similarity is input into the logistic regression model to obtain the degree of matching of the object text information between the object text information and the reference text information. Inputting the vector similarity between the implicit semantic features between the object text information and the reference text information into the logistic regression model corresponding to the object text information can effectively improve the accuracy of the text information matching degree detection.
  • a text information matching degree detection device is provided, and the device includes:
  • the text information obtaining module 401 is used to obtain object text information and its corresponding reference text information
  • the text information conversion module 402 is configured to convert the object text information into a first implicit feature vector, and convert the reference text information into a second implicit feature vector;
  • the vector similarity acquisition module 403 is configured to calculate the vector similarity between the first implicit feature vector and the second implicit feature vector;
  • the matching degree detection module 404 is configured to obtain a logistic regression model according to the target text information and preset keywords, and input the vector similarity into the logistic regression model to obtain the target text information and the reference text information The degree of match between the object text information.
  • Each module in the above-mentioned text information matching degree detection device can be implemented in whole or in part by software, hardware and a combination thereof.
  • the foregoing modules may be embedded in the form of hardware or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.
  • a server is provided.
  • the server may be implemented by computer equipment, and its internal structure diagram may be as shown in FIG. 5.
  • the computer equipment includes a processor, a memory, a network interface and a database connected by a device bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a non-volatile non-volatile storage medium and an internal memory.
  • the non-volatile non-volatile storage medium stores an operating device, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating device and the computer program in the non-volatile non-volatile storage medium.
  • the database of the computer device is used to store the data involved in the detection of the matching degree of text information.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer program is executed by the processor to realize a text information matching degree detection method.
  • FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device including a memory and a processor, and a computer program is stored in the memory.
  • the processor executes the computer program, the following steps are implemented: acquiring object text information and its corresponding reference text information; Converting the object text information into a first implicit feature vector, and converting the reference text information into a second implicit feature vector; calculating the difference between the first implicit feature vector and the second implicit feature vector.
  • the vector similarity of the; the logistic regression model is obtained according to the object text information and preset keywords, and the vector similarity is input into the logistic regression model to obtain the object between the object text information and the reference text information The matching degree of the text information.
  • acquiring the target self-encoding structure when the processor executes the computer program includes: inputting the object text information into a preset learning algorithm to obtain an object input vector; inputting the object input vector into the preset self-encoding structure An encoding structure, extracting the first implicit feature vector corresponding to the object input vector in the preset self-encoding structure.
  • the reference text information includes question text information and standard text information corresponding to the object text information;
  • the second implicit feature vector includes question implicit feature vector and Standard implicit feature vector;
  • said converting the reference text information into a second implicit feature vector includes: inputting the question text information into a preset learning algorithm to obtain a question input vector; inputting the question input vector into a pre- Set a self-encoding structure, extract the hidden feature vector of the question corresponding to the question input vector in the preset self-encoding structure; input the standard text information into a preset learning algorithm to obtain a standard input vector;
  • a standard input vector is input to the preset self-encoding structure, and the standard implicit feature vector corresponding to the standard input vector in the preset self-encoding structure is extracted.
  • the method further includes: obtaining a training feature vector associated with the object text information; Training feature vectors, train multiple pre-stored auto-encoding structures to obtain multiple training auto-encoding structures; calculate the information loss of each training auto-encoding structure, and select the training auto-encoding structure with the smallest amount of information loss as the preset Self-encoding structure.
  • the vector similarity when the processor executes the computer program, includes question similarity and standard similarity; the calculation of the difference between the first implicit feature vector and the second implicit feature vector The vector similarity includes: calculating the cosine of the angle between the first implicit feature vector and the problem implicit feature vector to obtain the problem similarity; calculating the first implicit feature vector and the The standard implies the cosine of the angle between the feature vectors to obtain the standard similarity.
  • acquiring a logistic regression model based on the target text information and preset keywords includes: acquiring a keyword between the preset keyword and the target text information Similarity; the keyword similarity and the vector similarity are set as parameters of a preset initial regression model to obtain the logistic regression model corresponding to the object text information.
  • the acquiring the keyword similarity between the preset keyword and the object text information when the processor executes the computer program includes: calculating the information value of each keyword in the preset keyword library, Select keywords whose information value is greater than a preset threshold value as the preset keywords; split the object text information to obtain multiple target words, and calculate the similarity between the preset keywords and the target words; select The maximum value in the similarity is set as the keyword similarity.
  • a computer-readable non-volatile storage medium is provided, and a computer program is stored thereon.
  • the computer program is executed by a processor, the following steps are implemented: acquiring object text information and its corresponding reference text information; Convert the object text information into a first implicit feature vector, and convert the reference text information into a second implicit feature vector; calculate the difference between the first implicit feature vector and the second implicit feature vector The vector similarity between the two; obtain a logistic regression model according to the object text information and preset keywords, and input the vector similarity into the logistic regression model to obtain the difference between the object text information and the reference text information The matching degree of the object text information.
  • the obtaining the target self-encoding structure when the computer program is executed by the processor includes: inputting the object text information into a preset learning algorithm to obtain an object input vector; inputting the object input vector into a preset
  • the self-encoding structure extracts the first implicit feature vector corresponding to the object input vector in the preset self-encoding structure.
  • the reference text information when the computer program is executed by the processor, the reference text information includes question text information and standard text information corresponding to the object text information; the second implicit feature vector includes a question implicit feature vector And the standard implicit feature vector; said converting the reference text information into a second implicit feature vector includes: inputting the question text information into a preset learning algorithm to obtain a question input vector; inputting the question input vector A preset self-encoding structure is used to extract the hidden feature vector of the question corresponding to the question input vector in the preset self-encoding structure; the standard text information is input into a preset learning algorithm to obtain a standard input vector; The standard input vector is input to the preset self-encoding structure, and the standard implicit feature vector corresponding to the standard input vector in the preset self-encoding structure is extracted.
  • the method further includes: obtaining a training feature vector associated with the object text information;
  • the training feature vector is used to train multiple pre-stored self-encoding structures to obtain multiple training self-encoding structures; calculate the information loss of each training self-encoding structure, and select the training self-encoding structure with the smallest amount of information loss as the prediction Set up a self-encoding structure.
  • the vector similarity when the computer program is executed by the processor, the vector similarity includes question similarity and standard similarity; the calculation of the difference between the first implicit feature vector and the second implicit feature vector The vector similarity of includes: calculating the cosine of the angle between the first implicit feature vector and the problem implicit feature vector to obtain the problem similarity; calculating the first implicit feature vector and the The cosine of the angle between the implicit feature vectors of the standard is used to obtain the similarity of the standard.
  • obtaining a logistic regression model based on the target text information and preset keywords includes: obtaining the key between the preset keywords and the target text information Word similarity; setting the keyword similarity and the vector similarity as the parameters of a preset initial regression model to obtain the logistic regression model corresponding to the target text information.
  • acquiring the keyword similarity between the preset keyword and the object text information includes: calculating the information value of each keyword in the preset keyword library Select a keyword with an information value greater than a preset threshold as the preset keyword; split the target text information to obtain multiple target words, and calculate the similarity between the preset keyword and the target word; The maximum value of the similarity is selected as the keyword similarity.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

L'invention concerne un procédé et un appareil de détection de degré de correspondance d'informations de texte, un dispositif informatique et un support de stockage. Le procédé comprend les étapes consistant à : acquérir des informations de texte d'objet et des informations de texte de référence correspondantes associées ; convertir les informations de texte d'objet en un premier vecteur de caractéristiques implicites et convertir les informations de texte de référence en un second vecteur de caractéristiques implicites ; calculer la similarité vectorielle entre le premier vecteur de caractéristiques implicites et le second vecteur de caractéristiques implicites ; et acquérir un modèle de régression logistique en fonction des informations de texte d'objet et d'un mot-clé prédéfini et entrer la similarité de vecteur dans le modèle de régression logistique pour obtenir un degré de correspondance des informations de texte d'objet entre les informations de texte d'objet et les informations de texte de référence.
PCT/CN2019/103650 2019-06-27 2019-08-30 Procédé et appareil de détection de degré de correspondance d'informations de texte, dispositif informatique et support de stockage WO2020258506A1 (fr)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113343987A (zh) * 2021-06-30 2021-09-03 北京奇艺世纪科技有限公司 文本检测处理方法、装置、电子设备及存储介质
CN114003305A (zh) * 2021-10-22 2022-02-01 济南浪潮数据技术有限公司 设备相似度计算方法、计算机设备和存储介质
CN116188091A (zh) * 2023-05-04 2023-05-30 品茗科技股份有限公司 造价清单自动匹配单价引用的方法、装置、设备及介质
CN117195860A (zh) * 2023-11-07 2023-12-08 品茗科技股份有限公司 智能巡检方法、系统、电子设备和计算机可读存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103870440A (zh) * 2012-12-12 2014-06-18 中国移动通信集团广西有限公司 一种文本数据处理方法及装置
CN108920654A (zh) * 2018-06-29 2018-11-30 泰康保险集团股份有限公司 一种问答文本语义匹配的方法和装置
CN109189931A (zh) * 2018-09-05 2019-01-11 腾讯科技（深圳）有限公司 一种目标语句的筛选方法及装置
CN109918663A (zh) * 2019-03-04 2019-06-21 腾讯科技（深圳）有限公司 一种语义匹配方法、装置及存储介质

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103870440A (zh) * 2012-12-12 2014-06-18 中国移动通信集团广西有限公司 一种文本数据处理方法及装置
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CN113343987B (zh) * 2021-06-30 2023-08-22 北京奇艺世纪科技有限公司 文本检测处理方法、装置、电子设备及存储介质
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