WO2019085118A1 - Procédé d'analyse de mot associé basé sur un modèle de sujet, et appareil électronique et support d'informations - Google Patents

Procédé d'analyse de mot associé basé sur un modèle de sujet, et appareil électronique et support d'informations Download PDF

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Publication number
WO2019085118A1
WO2019085118A1 PCT/CN2017/113720 CN2017113720W WO2019085118A1 WO 2019085118 A1 WO2019085118 A1 WO 2019085118A1 CN 2017113720 W CN2017113720 W CN 2017113720W WO 2019085118 A1 WO2019085118 A1 WO 2019085118A1
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word
topic
words
queried
text
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PCT/CN2017/113720
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English (en)
Chinese (zh)
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赵清源
吕梓燊
韦邕
徐亮
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
    • G06F16/33—Querying
    • G06F16/332—Query formulation

Definitions

  • the present application relates to the field of information retrieval, and in particular, to a method for analyzing related words based on a topic model, an electronic device, and a storage medium.
  • the calculation of related words is a very important step. Through the calculation of related words, we can estimate the user's possible thoughts when the user types less, and expand the content of the search. On the other hand, we can also find the synonyms of the user input, and look for similar meanings in the database. Other words for association matching.
  • the present application proposes a method, device and computer readable medium for analyzing related words based on a topic model.
  • the topic model-based related word analysis method, device and computer readable medium proposed in the present application are applicable to an information retrieval system of any professional field, and can quickly and accurately calculate related words corresponding to the feature words to be retrieved.
  • the present application proposes a method for analyzing related words based on a topic model, and the method includes the following steps:
  • the text to be queried in the technical field is obtained from a predetermined database corresponding to the technical field, and the acquired text to be queried is subjected to topic modeling to obtain each text to be queried.
  • Corresponding theme model ;
  • obtaining the topic vector corresponding to each word from the probability distribution matrix comprises:
  • the parameters corresponding to each column in the probability distribution matrix are normalized to obtain a topic vector corresponding to each word in the dimension of the word.
  • the preset associated word weight analysis rule comprises:
  • the other words corresponding to the smallest Euclidean distance found are used as the related words of the word to be searched.
  • the other words refer to words other than the words to be retrieved among the words corresponding to the probability distribution matrix.
  • step A the following steps are further included:
  • the corpus corresponding to the technical domain to which the acquired text to be queried belongs is determined, and the determined corpus is used as a corpus of the subject model of the technical field.
  • the present application further provides an electronic device based on a topic model, the device comprising: a memory, a processor, and a related word analysis system based on a topic model, where the topic model based related words are stored
  • the analysis system is executed by the processor, the following operations are implemented:
  • the performing, by the processor, the topic model-based related-word analysis system, the obtaining the topic vector corresponding to each word from the topic feature probability distribution matrix comprises:
  • the parameters corresponding to each column in the probability distribution matrix are normalized to obtain a topic vector corresponding to each word in the dimension of the word.
  • the performing, by the processor, the topic model-based related word analysis system to implement the preset related word weight analysis rule comprises:
  • the other words corresponding to the smallest Euclidean distance found are used as the related words of the word to be searched.
  • the processor performs the following operations before the step S1 is implemented by the topic model-based related word analysis system:
  • the corpus corresponding to the technical domain to which the acquired text to be queried belongs is determined, and the determined corpus is used as a corpus of the subject model of the technical field.
  • the present application further provides a computer readable storage medium,
  • the computer readable medium stores a topic model-based related word analysis program, and the topic model-based related word analysis program is executed by the processor to implement the step of the above-described topic model-based related word analysis method.
  • the topic model based related word analysis method, the electronic device and the computer readable storage medium proposed by the present application firstly, when the subject model needs to be modeled in a technical field, the corresponding advance in the technical field
  • the determined database obtains the text to be queried in the technical field, and performs subject modeling on the acquired text to be acquired to obtain a topic model corresponding to each text to be queried; and then, based on the theme model, the text to be queried is trained to train the text to be queried to include The subject, and the probability distribution matrix of the probabilities of the words in the topics included in each topic; then, the subject vectors corresponding to the respective words are obtained from the probability distribution matrix, and the corresponding words are analyzed according to the preset related words weight analysis rules The relationship between the topic vectors to analyze the related words corresponding to the words to be retrieved.
  • FIG. 1 is a schematic diagram of a hardware architecture of a subject model based electronic device of the present application
  • FIG. 2 is a schematic diagram of program modules of a topic model-based related word analysis system of the present application
  • FIG. 3 is a schematic diagram of a hardware architecture of the analysis module of FIG. 2;
  • FIG. 4 is a schematic diagram of an implementation process of a method for analyzing related words based on a topic model in the present application
  • FIG. 5 is a schematic flow chart of the implementation of step S403 in FIG. 4.
  • FIG. 1 it is a schematic diagram of a hardware architecture of an electronic device of the present application.
  • the electronic device 1 includes, but is not limited to, the memory 11, the processor 12, and the network interface 13 can be connected to each other through a system bus. It is to be noted that FIG. 1 only shows an electronic device having components 11-13, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, SD or DX memory, etc.), a random access memory (RAM), and a static random access.
  • Memory SRAM
  • ROM read only memory
  • EEPROM electrically erasable programmable read only memory
  • PROM programmable read only memory
  • magnetic memory Storage disk, CD, etc.
  • the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1.
  • the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), and a secure digital (Secure Digital, SD). ) cards, flash cards, etc.
  • the memory 11 can also include both an internal storage unit of the electronic device 1 and an external storage device thereof.
  • the memory 11 is generally used to store an operating system installed in the electronic device 1 and various types of application software, such as program code of the related-word analysis system 200 based on the theme model. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 12 is generally used to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication with the electronic device 1.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running a topic model based correlation word analysis system 200 or the like.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 1 and other electronic devices.
  • the electronic device 1 further includes a display (not shown in FIG. 1 ), and the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode). Diode) Toucher, etc.
  • the display is used to display information processed in the electronic device 1 and a user interface or the like for displaying the visualization.
  • the present application proposes a topic model based related word analysis system 200.
  • the topic model based related word analysis system 200 can be divided into one or more modules, wherein one or more modules are stored in the memory 11 and are composed of one or more processors (in this embodiment Executed for the processor 12) to complete the application.
  • the topic model based related word analysis system 200 can be segmented into a modeling module 201, a training module 202, and an analysis 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 topic model based related word analysis system 200 in the electronic device 1.
  • the function of each program module 201-203 will be described in detail below.
  • the modeling module 201 is configured to acquire a predetermined database corresponding to the technical field (for example, a corresponding paper library of the technical field, a blog article library, etc.) when the subject model needs to be modeled in a technical field.
  • the text to be queried is subjected to topic modeling to obtain the topic model corresponding to each text to be queried.
  • the query may be processed according to an application scenario in the technical field. Edit the text to get good quality text to be queried.
  • the text in the predetermined database in the medical field is used as the text to be queried.
  • the text with no practical meaning is deleted according to the core keyword language in the medical field (for example, analyzing each query to be queried)
  • the text contains a predetermined type of core keyword language in the medical field and a corresponding number. If the type of the core keyword language contained in a text to be queried is less than the first threshold (for example, 2), the total number of core keyword words is included. Less than the second threshold (eg, 2), the text is determined to be meaningless text), and interference is eliminated.
  • the first threshold for example, 2
  • the second threshold eg, 2
  • the text is determined to be meaningless text
  • a topic represents a concept, an aspect, expressed as a series of related words, and is the conditional probability of these words.
  • the theme is a bucket with words with high probability of occurrence. These words have a strong correlation with this theme.
  • a corpus is needed.
  • the corpus corresponding to the technical domain to which the acquired text belongs is determined, and the determined corpus is used as the technical field.
  • the corpus of the topic modeling is used as the technical field.
  • the training module 202 is configured to train the to-be-queried text based on the topic model to train a probability distribution matrix of the topics included in the text to be queried and the probability that the words in the included topics appear in the respective topics.
  • the process of generating each word depends on the topic to which the word belongs, that is, usually there is a conditional probability relationship between a word and a topic.
  • the conditional probability relationship is usually expressed as: P ( Words
  • P Words
  • To represent such a relationship by a matrix the number of rows of the matrix is equal to the number of topics, and the number of columns is equal to the number of all words.
  • each row of the matrix is a probability distribution of generating different words under a certain topic. That is, when training a certain text to be queried based on the topic model, the probability distribution matrix between different words corresponding to the topic included in the text to be queried is usually trained.
  • each column of the probability distribution matrix represents the probability of occurrence of a certain word in a certain topic, and normalizing each parameter corresponding to each column to obtain The topic vector corresponding to each word in the dimension of the word.
  • the analyzing module 203 is configured to analyze the relationship between the topic vectors corresponding to the respective words according to the preset related word weight analysis rules, so as to analyze the related words corresponding to the words to be searched.
  • the preset associated word weight analysis rules include:
  • each of the other words corresponding to the subject vector corresponding to the word to be searched and the probability distribution matrix (the other words refer to: words other than the words to be searched in the words corresponding to the probability distribution matrix) Euclidean distance between corresponding theme vectors;
  • the other words corresponding to the found Euclidean distance are used as the related words of the word to be searched.
  • other words refer to words other than the words to be retrieved among the words corresponding to the probability distribution matrix.
  • the training result of the corpus to be queried by the topic model LDA (Latent Dirichlet Allocation) is used, and the potential probability distribution of words in each topic is abstracted into the probability of words to topics.
  • the distribution is used to calculate the Euclidean distance between the topic vectors corresponding to each word, and then the relationship between the words in the entire corpus is calculated according to the Euclidean distance between the topic vectors corresponding to each word.
  • the features in this embodiment are words.
  • FIG. 3 is a schematic diagram of the hardware architecture of the analysis module 203 of FIG.
  • the analysis module 203 includes a calculation unit 301, a comparison unit 302, and a related word determination unit 303.
  • the calculating unit 301 is configured to separately calculate the Euclidean distance between the topic vector corresponding to the word to be searched and the topic vector corresponding to each other word corresponding to the probability distribution matrix.
  • the parsing unit 302 is configured to analyze and calculate a size relationship between each Euclidean distance to find a minimum Euclidean distance.
  • the related word determining unit 303 is configured to use other words corresponding to the found smallest Euclidean distance as related words of the to-be-searched word.
  • the text to be queried in the technical field is obtained from a predetermined database corresponding to the technical field, and the acquired text to be queried is acquired.
  • topic modeling to obtain a topic model corresponding to each text to be queried; then, training the text to be queried based on the topic model to train the theme included in the text to be queried, and the probability that the words in the included topic appear in each topic
  • the probability distribution matrix is obtained.
  • the topic vector corresponding to each word is obtained from the probability distribution matrix, and the relationship between the topic vectors corresponding to each word is analyzed according to the preset related word weight analysis rules, so as to analyze the related words corresponding to the words to be searched.
  • the online open source synonym synonym lexicon be directly used in a specific professional field, but also the retrieval method based on the topic model-based related word analysis method is more accurate and the search result is more comprehensive than the existing related word analysis method. .
  • the present application also proposes a method for analyzing related words based on a topic model.
  • FIG. 4 it is a schematic diagram of an implementation process of a related-word analysis method based on a topic model in the present application.
  • the method for analyzing related words based on the topic model of the present application includes the following steps S401 to S403.
  • Step S401 when subject modeling is required in a technical field, obtaining a to-be-queried text in the technical field from a predetermined database corresponding to the technical domain (for example, a paper library corresponding to the technical field, a blog article library, etc.)
  • the subject of the acquired text to be queried is modeled to obtain a topic model corresponding to each text to be queried.
  • the query text may be edited according to an application scenario in the technical field to obtain a high-quality text to be queried.
  • the text in the predetermined database in the medical field is used as the text to be queried.
  • the text with no practical meaning is deleted according to the core keyword language in the medical field (for example, analyzing each query to be queried)
  • the text contains a predetermined type of core keyword language in the medical field and a corresponding number. If the type of the core keyword language contained in a text to be queried is less than the first threshold (for example, 2), the total number of core keyword words is included. Less than the second threshold (eg, 2), the text is determined to be meaningless text), and interference is eliminated.
  • the first threshold for example, 2
  • the second threshold eg, 2
  • a topic In the process of cutting words, only retain nouns and verbs, delete some adjectives, auxiliary words, etc., such as deleting "", " ⁇ ", " ⁇ ”, etc. interference.
  • a topic In a topic model, a topic represents a concept, an aspect, expressed as a series of related words, and is the conditional probability of these words.
  • the theme In terms of image, the theme is a bucket with words with high probability of occurrence. These words have a strong correlation with this theme.
  • a corpus is needed.
  • the corpus corresponding to the technical domain to which the acquired text belongs is determined, and the determined corpus is used as the technical field.
  • the corpus of the topic modeling is used as the technical field.
  • Step S402 the text to be queried is trained based on the topic model to train a probability distribution matrix of the subject included in the text to be queried and the probability that the words in the included subject appear in each topic.
  • the process of generating each word depends on the topic to which the word belongs, that is, usually there is a conditional probability relationship between a word and a topic.
  • the conditional probability relationship is usually expressed as: P ( Words
  • P Words
  • To represent such a relationship by a matrix the number of rows of the matrix is equal to the number of topics, and the number of columns is equal to the number of all words.
  • each row of the matrix is a probability distribution of generating different words under a certain topic. That is, when training a certain text to be queried based on the theme model, the probability distribution matrix between different words corresponding to the theme of the theme fish brother included in the text to be queried is usually trained.
  • each column of the probability distribution matrix represents the probability that a certain word appears in a certain topic, and further, after normalizing the parameters corresponding to each column , to obtain the theme vector corresponding to each word in the dimension of the word.
  • Step S403 analyzing the relationship between the topic vectors corresponding to each word according to the preset related word weight analysis rule, and analyzing the related words corresponding to the words to be searched.
  • the preset associated word weight analysis rules include:
  • each of the other words corresponding to the subject vector corresponding to the word to be searched and the probability distribution matrix (the other words refer to: words other than the words to be searched in the words corresponding to the probability distribution matrix) Euclidean distance between corresponding theme vectors;
  • the other words corresponding to the found Euclidean distance are used as the related words of the word to be searched.
  • other words refer to words other than the words to be retrieved among the words corresponding to the probability distribution matrix.
  • the training result of the corpus to be queried by the topic model LDA (Latent Dirichlet Allocation) is used, and the potential probability distribution of the words in each topic is abstracted into a probability distribution of words to topics, and the distribution is used to calculate the topic vector corresponding to each word.
  • the Euclidean distance between the Euclidean distances is used to calculate the relationship between the words in the entire corpus according to the Euclidean distance between the subject vectors corresponding to each word. It should be noted that the features in this embodiment are words.
  • step S403 in FIG. 5, it is a schematic flowchart of the implementation of step S403 in FIG. As shown in FIG. 5, step S403 specifically includes the following steps S501 to S503 in an embodiment.
  • Step S501 respectively calculating the Euclidean distance between the topic vector corresponding to the word to be retrieved and the topic vector corresponding to each other word corresponding to the probability distribution matrix.
  • Step S502 analyzing and calculating the size relationship between the various Euclidean distances, and finding the smallest Euclidean distance.
  • Step S503 the other words corresponding to the found smallest Euclidean distance are used as related words of the to-be-searched word.
  • the text to be queried in the technical field is obtained from a predetermined database corresponding to the technical field, and the acquired text to be queried is subjected to topic modeling to obtain each text to be queried.
  • Corresponding topic model training the text to be queried based on the topic model to train the subject included in the text to be queried, and the probability distribution matrix of the probability that the words in the included subject appear in each topic; and then, from the probability distribution matrix Obtaining the topic vector corresponding to each word, and analyzing the relationship between the topic vectors corresponding to each word according to the preset related word weight analysis rule, so as to analyze the related words corresponding to the word to be searched.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, 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.

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Abstract

La présente invention concerne un procédé d'analyse de mot associé basé sur un modèle de sujet, le procédé comprenant les étapes suivantes : A, lorsqu'un champ technique doit être soumis à une modélisation de sujet, à acquérir, à partir d'une base de données prédéterminée correspondant au champ technique, des textes à interroger dans le domaine technique, et à réaliser une modélisation de sujet sur les textes acquis à interroger, de façon à acquérir des modèles de sujets correspondant à divers textes à interroger; B, sur la base des modèles de sujets, à former les textes à interroger, de façon à former des sujets inclus dans les textes à interroger et une matrice de distribution de probabilités de probabilités, se produisant dans divers sujets, de mots dans les sujets inclus; et C, à acquérir, à partir de la matrice de distribution de probabilités, des vecteurs de sujets correspondant à différents mots, et à analyser une relation entre les vecteurs de sujets correspondant à différents mots conformément à une règle d'analyse de poids de mots associée prédéfinie, de façon à analyser des mots associés correspondant à des mots à extraire. En conséquence, un calcul relativement précis et complet de mots associés est réalisé lorsqu'un champ professionnel spécifique est soumis à une extraction d'informations.
PCT/CN2017/113720 2017-11-01 2017-11-30 Procédé d'analyse de mot associé basé sur un modèle de sujet, et appareil électronique et support d'informations WO2019085118A1 (fr)

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