WO2019041524A1 - Procédé, appareil électronique et support de stockage lisible par ordinateur permettant de générer une étiquette de grappe - Google Patents

Procédé, appareil électronique et support de stockage lisible par ordinateur permettant de générer une étiquette de grappe Download PDF

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Publication number
WO2019041524A1
WO2019041524A1 PCT/CN2017/108807 CN2017108807W WO2019041524A1 WO 2019041524 A1 WO2019041524 A1 WO 2019041524A1 CN 2017108807 W CN2017108807 W CN 2017108807W WO 2019041524 A1 WO2019041524 A1 WO 2019041524A1
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WIPO (PCT)
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cluster
keywords
keyword
calculation formula
extracting
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PCT/CN2017/108807
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English (en)
Chinese (zh)
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罗傲雪
汪伟
肖京
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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
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • 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
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques

Definitions

  • the present application relates to the field of computer information technology, and in particular, to a cluster label generation method, an electronic device, and a computer readable storage medium.
  • the present application proposes a cluster label generation method, an electronic device, and a computer readable storage medium, and optimizes a clustering keyword extraction process on a semantic level by a preset naive Bayesian calculation formula, and The label extraction of clustered text is optimized.
  • the present application provides an electronic device including a memory and a processor, on which is stored a cluster label generation system executable on the processor, the The class tag generation system is implemented by the processor to implement the following steps:
  • the most discriminating keywords are extracted from the keywords of each cluster, and are recorded as labels of each cluster.
  • the extracting the representative keywords comprises: extracting keywords of each cluster according to the conditional probability value size of the words.
  • the extracting representative keywords includes:
  • conditional probability values of each word calculated by each cluster are sorted in descending order, and a preset number of keywords are extracted and recorded as cluster keywords.
  • the extracting the most distinguishing keyword comprises: extracting the most discriminating keyword from the keywords of each cluster according to the transition probability value between the words and the preset naive Bayes calculation formula.
  • the extracting the most distinguishing keywords comprises:
  • conditional probability values of each keyword recalculated for each cluster are sorted in descending order, and the keyword with the highest conditional probability value is extracted and recorded as a clustering label.
  • the preset naive Bayesian calculation formula is set to formula 1:
  • Equation 1 S represents a piece of text consisting of n words W1, W2, ... Wn, and Wi represents a word in the semantic network relationship constructed by the piece of text;
  • the preset transition probability calculation formula is set to Equation 2:
  • Wi) Pt(Wj
  • Equation 2 m represents the number of clusters after text clustering, t represents one of the clusters, Wi and Wj represent keywords extracted by each cluster, and Pt(Wj
  • the present application further provides a cluster label generation method, which is applied to an electronic device, and the method includes:
  • the most discriminating keywords are extracted from the keywords of each cluster, and are recorded as labels of each cluster.
  • the extracting the representative keywords includes: extracting keywords of each cluster according to the conditional probability value size of the words, specifically including:
  • conditional probability values of each word calculated by each cluster are sorted in descending order, and a preset number of keywords are extracted and recorded as cluster keywords.
  • the extracting the most distinguishing keyword comprises: extracting the most distinctive keyword from each cluster of keywords according to a transition probability value between words and a preset naive Bayesian calculation formula, Specifically include:
  • conditional probability values of each keyword recalculated for each cluster are sorted in descending order, and the keyword with the highest conditional probability value is extracted and recorded as a clustering label.
  • the present application further provides a computer readable storage medium storing a cluster label generation system, the cluster label generation system
  • the step of causing the at least one processor to perform the clustering label generation method as described above may be performed by at least one processor.
  • the electronic device, the cluster label generation method and the computer readable storage medium proposed by the present application optimize the extraction of clustering keywords on the semantic level by using a preset naive Bayesian calculation formula. process. Further, the label extraction of the clustered text is also optimized, so that the extracted cluster labels have high discrimination and recognition.
  • 1 is a schematic diagram of an optional hardware architecture of an electronic device of the present application
  • FIG. 2 is a schematic diagram of a program module of an embodiment of a cluster label generation system in an electronic device of the present application
  • FIG. 3 is a schematic diagram of an implementation process of an embodiment of a method for generating a cluster label according to the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the electronic device 2 of the present application.
  • the electronic device 2 may include, but is not limited to, a memory 21, a processor 22, and a network interface 23 that can communicate with each other through a system bus. It is pointed out that FIG. 1 only shows the electronic device 2 with the components 21-23, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.
  • the electronic device 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the electronic device 2 may be an independent server or a server cluster composed of multiple servers. .
  • the memory 21 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2.
  • the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk equipped on the electronic device 2, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • the memory 21 may also include both an internal storage unit of the electronic device 2 and an external storage device thereof.
  • the memory 21 is generally used to store an operating system installed in the electronic device 2 and various types of application software, such as program codes of the cluster tag generation system 20. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 22 is typically used to control the overall operation of the electronic device 2, such as performing control and processing related to data interaction or communication with the electronic device 2.
  • the processor 22 is configured to run program code or process data stored in the memory 21, for example, to run the cluster.
  • the tag generation system 20 and the like.
  • the network interface 23 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 2 and other electronic devices.
  • the network interface 23 is configured to connect the electronic device 2 to an external data platform through a network, and establish a data transmission channel and a communication connection between the electronic device 2 and an external data platform.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.
  • the cluster tag generation system 20 may be divided into one or more program modules, the one or more program modules being stored in the memory 21 and being processed by one or more processors. (Processing in the present embodiment for the processor 22) to complete the application.
  • the cluster tag generation system 20 can be divided into a construction module 201, an extraction module 202, and a generation module 203.
  • the program module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function, and is more suitable than the program to describe the execution process of the cluster tag generation system 20 in the electronic device 2. The function of each program module 201-203 will be described in detail below.
  • the building module 201 is configured to construct a semantic network relationship between words in each cluster for the text clustering result.
  • the text clustering is performed for the unsupervised corpus
  • the clustering method may adopt the Text-rank clustering algorithm
  • the text clustering result may be the text summary information or the like.
  • the semantic network relationship is used to describe the concept and state of an object and its relationship. It consists of an arc between a node and a node. The node represents a concept (event, thing, etc.), and the arc represents the relationship between concepts.
  • the extracting module 202 is configured to extract a representative keyword from the semantic network relationship constructed by each cluster, and record it as a clustering keyword.
  • the extracting the representative keywords comprises: extracting keywords of each cluster according to the conditional probability value size of the words.
  • S represents a piece of text
  • Wi represents a word in the semantic network relationship constructed by the piece of text
  • P the conditional probability value of each word in the semantic network relationship constructed by each cluster
  • the conditional probability values of each word calculated by each cluster are sorted in descending order, and a preset number (for example, three) of keywords is extracted and recorded as a clustering keyword.
  • the clustering keyword is a word that best represents the semantics of the piece of text.
  • the conditional probability value is calculated according to a preset naive Bayesian Drawn.
  • the preset naive Bayesian calculation formula can be set as the following formula 1 (LaTex version).
  • Equation 1 can also be expressed as follows:
  • Equation 1 represents the probability that the text S appears in the case where the given word Wi appears, the right half of the equation is the product calculation formula, and n represents the number of words in the text S.
  • the generating module 203 is configured to extract, from the keywords of each cluster, the most distinctive keywords, and record the labels of each cluster.
  • the extracting the most distinguishing keyword comprises: according to the transition probability value between the words and the preset naive Bayes calculation formula, from each cluster of keywords Extract the most discriminating keywords. Specifically, first, according to a preset transition probability calculation formula, a transition probability value between the keywords in the total document aggregated by all the documents of each cluster is calculated.
  • the preset transition probability calculation formula may be set to the following formula 2.
  • Wi) Representation The transition probability of the keywords Wi to Wj in the total document in which all the documents of the t-th cluster are aggregated.
  • the formula for calculating the transition probability between keywords in the first cluster is:
  • Wi) P1(Wj
  • transition probability value between the keywords in each cluster is substituted into the preset naive Bayesian calculation formula (the above formula 1), and the conditional probability value of each keyword is recalculated (final result) Is a multiplication of a transfer matrix).
  • the conditional probability values of each keyword recalculated for each cluster are sorted in descending order, and the keyword with the highest conditional probability value is extracted and recorded as a clustering label.
  • the recalculated conditional probability value represents the discriminative level of each keyword. The higher the conditional probability value recalculated by a keyword, the higher the discriminativeness, and the more suitable for clustering labels.
  • multiple keywords with higher discrimination may be selected from the keywords of each cluster, as each cluster. label.
  • the cluster label generation system 20 optimizes the extraction process of cluster keywords on the semantic level by using a preset naive Bayesian calculation formula. Further, the label extraction of the clustered text is also optimized, so that the extracted cluster labels have high discrimination and recognition.
  • the present application also proposes a cluster label generation method.
  • FIG. 3 it is a schematic flowchart of an implementation process of an embodiment of a method for generating a cluster label of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 3 may be changed according to different requirements, and some steps may be omitted.
  • Step S31 constructing a semantic network relationship between words in each cluster for the text clustering result.
  • the text clustering is performed for the unsupervised corpus
  • the clustering method may adopt the Text-rank clustering algorithm
  • the text clustering result may be the text summary information or the like.
  • the semantic network relationship is used to describe the concept and state of an object and its relationship. It consists of an arc between a node and a node. The node represents a concept (event, thing, etc.), and the arc represents the relationship between concepts.
  • step S32 a representative keyword is extracted from the semantic network relationship constructed by each cluster, and is recorded as a clustering keyword.
  • the extracting the representative keywords comprises: extracting keywords of each cluster according to the conditional probability value size of the words.
  • S represents a piece of text
  • Wi represents a word in the semantic network relationship constructed by the piece of text
  • P the conditional probability value of each word in the semantic network relationship constructed by each cluster
  • the conditional probability values of each word calculated by each cluster are sorted in descending order, and a preset number (for example, three) of keywords is extracted and recorded as a clustering keyword.
  • the clustering keyword is a word that best represents the semantics of the piece of text.
  • the conditional probability value is obtained according to a preset naive Bayesian calculation formula.
  • the preset naive Bayesian calculation formula can be set as the following formula 1 (LaTex version).
  • Equation 1 can also be expressed as follows:
  • Equation 1 represents the probability that the text S appears in the case where the given word Wi appears, the right half of the equation is the product calculation formula, and n represents the number of words in the text S.
  • step S33 the most discriminating keywords are extracted from the keywords of each cluster, and are recorded as labels of each cluster.
  • the extracting the most distinguishing keyword comprises: according to the transition probability value between the words and the preset naive Bayes calculation formula, from each cluster of keywords Extract the most discriminating keywords. Specifically, first, according to a preset transition probability calculation formula, a transition probability value between the keywords in the total document aggregated by all the documents of each cluster is calculated.
  • the preset transition probability calculation formula may be set to the following formula 2.
  • m represents the number of clusters after text clustering
  • t represents one of the clusters (eg, the first cluster)
  • Wi and Wj represent keywords extracted by each cluster
  • the formula for calculating the transition probability between keywords in the first cluster is:
  • Wi) P1(Wj
  • transition probability value between the keywords in each cluster is substituted into the preset naive Bayesian calculation formula (the above formula 1), and the conditional probability value of each keyword is recalculated (final result) Is a multiplication of a transfer matrix).
  • the conditional probability values of each keyword recalculated for each cluster are sorted in descending order, and the keyword with the highest conditional probability value is extracted and recorded as a clustering label.
  • the recalculated conditional probability value represents the discriminative level of each keyword. The higher the conditional probability value recalculated by a keyword, the higher the discriminativeness, and the more suitable for clustering labels.
  • multiple keywords with higher discrimination may be selected from the keywords of each cluster, as each cluster. label.
  • the cluster label generation method proposed by the present application optimizes the extraction process of the clustering keywords on the semantic level by the preset naive Bayesian calculation formula. Further, the label extraction of the clustered text is also optimized, so that the extracted cluster labels have high discrimination and recognition.
  • the present application further provides a computer readable storage medium (such as a ROM/RAM, a magnetic disk, an optical disk), where the computer readable storage medium stores a cluster label generation system 20, the aggregation
  • the class tag generation system 20 can be executed by at least one processor 22 to cause the at least one processor 22 to perform the steps of the cluster tag generation method as described above.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and can also be implemented by hardware, but in many cases, the former is A better implementation.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

Abstract

L'invention concerne un procédé permettant de générer une étiquette de grappe, ledit procédé consistant à : construire, pour des résultats de regroupement de texte, une relation de réseau sémantique pour des mots dans chaque grappe (S31); extraire des mots-clés représentatifs à partir de la relation de réseau sémantique construite à partir de chacune des grappes, puis marquer ceux-ci en tant que mots-clés de grappe (S32); et extraire le mot-clé le plus discriminant à partir des mots-clés de chaque grappe et marquer celui-ci en tant qu'étiquette de ladite grappe (S33). L'invention améliore ainsi la capacité de discrimination et d'identification d'une étiquette d'une grappe.
PCT/CN2017/108807 2017-08-31 2017-10-31 Procédé, appareil électronique et support de stockage lisible par ordinateur permettant de générer une étiquette de grappe WO2019041524A1 (fr)

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CN113204653A (zh) * 2021-06-04 2021-08-03 中国银行股份有限公司 需求价值的标注方法、装置、计算机设备及可读存储介质

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