WO2020057023A1 - Procédé d'analyse sémantique de langage naturel, appareil, dispositif informatique et support d'informations - Google Patents

Procédé d'analyse sémantique de langage naturel, appareil, dispositif informatique et support d'informations Download PDF

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Publication number
WO2020057023A1
WO2020057023A1 PCT/CN2019/071251 CN2019071251W WO2020057023A1 WO 2020057023 A1 WO2020057023 A1 WO 2020057023A1 CN 2019071251 W CN2019071251 W CN 2019071251W WO 2020057023 A1 WO2020057023 A1 WO 2020057023A1
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semantic
natural language
language information
semantic parsing
initial
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PCT/CN2019/071251
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English (en)
Chinese (zh)
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江琳
杨镭
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深圳壹账通智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

Definitions

  • the present application relates to a method, an apparatus, a computer device, and a storage medium for semantic analysis of natural language.
  • a method, an apparatus, a computer device, and a storage medium for a semantic analysis of a natural language are provided.
  • a natural language semantic analysis method includes:
  • the initial semantic parsing result is filtered using the screening value to obtain a semantic parsing result that matches the filtered value, and the semantic parsing result is sent to a terminal.
  • a semantic analysis device for natural language includes:
  • the initial semantic parsing result is filtered using the screening value to obtain a semantic parsing result that matches the filtered value, and the semantic parsing result is sent to a terminal.
  • a computer device includes a memory and one or more processors.
  • the memory stores computer-readable instructions.
  • the one or more processors are executed. The following steps:
  • the initial semantic parsing result is filtered using the screening value to obtain a semantic parsing result that matches the filtered value, and the semantic parsing result is sent to a terminal.
  • One or more non-volatile computer-readable storage media storing computer-readable instructions.
  • the one or more processors execute the following steps:
  • the initial semantic parsing result is filtered using the screening value to obtain a semantic parsing result that matches the filtered value, and the semantic parsing result is sent to a terminal.
  • FIG. 1 is an application scenario diagram of a semantic parsing method of natural language according to one or more embodiments.
  • FIG. 2 is a schematic flowchart of a semantic parsing method of natural language according to one or more embodiments.
  • FIG. 3 is a schematic flowchart of a semantic scenario for acquiring natural language information according to one or more embodiments.
  • FIG. 4 is a block diagram of a semantic parsing apparatus for natural language according to one or more embodiments.
  • FIG. 5 is a block diagram of a computer device according to one or more embodiments.
  • the semantic parsing method provided by the present application can be applied to the application environment shown in FIG. 1.
  • the terminal 102 and the server 104 communicate through a network.
  • the server 104 receives a semantic analysis request for natural language information sent by the terminal 102, and acquires a semantic scene corresponding to the natural language information.
  • the server 104 parses natural language information by using a preset semantic parsing method to obtain an initial semantic parsing result.
  • obtain filtered values corresponding to natural language information obtain filtered values corresponding to natural language information, and use the filtered values to filter the initial semantic parsing results to obtain semantic parsing results that match the filtered values.
  • the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers.
  • the server 104 may be implemented by an independent server or a server cluster composed of multiple servers.
  • a method for semantic parsing of natural language is provided.
  • the method is applied to the server in FIG. 1 as an example, and includes the following steps:
  • the server receives a semantic parsing request for natural language information sent by the terminal.
  • the semantic parsing request means a request for semantic parsing of the natural language information input by the user.
  • the server obtains the natural language information input by the user at the terminal, and performs semantic parsing of the corresponding natural language information according to the semantic parsing request sent by the terminal.
  • the server receives the natural language information input by the user at the terminal, and receives a semantic analysis request for the natural language information sent by the terminal, and performs semantic analysis on the received natural language information according to the semantic analysis request.
  • natural language information has various ambiguities and ambiguities in different scenarios or different contexts. Before semantic analysis of natural language information, it needs to be sorted and analyzed. According to the corresponding scene and Context eliminates its ambiguity, and transforms it into a format that meets the internal storage requirements of the computer, and performs semantic analysis on the natural language information that meets the preset requirements in the computer.
  • the natural language information input by the user is "the loan distribution of Shenzhen university students" as an example.
  • the user enters "Shenzhen University Student Loan Distribution” in the terminal, and the terminal sends to the server a semantic analysis request for semantic analysis of the "Shenzhen University Student Loan Distribution".
  • the server receives the semantic analysis request, and according to the semantic analysis request, the "Shenzhen University Student Loan Distribution” "For semantic parsing.
  • the semantics of the natural language information “Shenzhen University Student Loan Distribution” can include: “Shenzhen University Student Loan Education Institution Distribution”, “Shenzhen University Student Loan Situation”, “Shenzhen University Student Loan” Amount distribution "and” Shenzhen university student loan types distribution "and other situations.
  • the natural language information is analyzed and arranged in a certain context and application scenario, and the semantics of the corresponding natural language information representation in different contexts or application scenarios are obtained.
  • the server obtains a semantic scene corresponding to the natural language information.
  • the server separately calculates the relevance value between the natural language information and different semantic scenes, sorts the semantic scenes according to the magnitude of the relevance value, and obtains the semantic scene corresponding to the maximum relevance value.
  • the relevance value is used to judge the relevance between the natural language information input by the user and multiple semantic scenes.
  • the server obtains the degree of association between the natural language information and the semantic scene by calculating the correlation value between the natural language information input by the user and the different semantic scenes.
  • the calculated relevance values are sorted in ascending order, and the semantic scene corresponding to the largest relevance value is obtained as the semantic scene most relevant to the natural language information input by the user, that is, the semantic scene with the highest degree of relevance. .
  • semantic scenarios include: small loans, loans, and large loans, etc., respectively, to calculate "Shenzhen small borrower regional distribution" ,
  • the degree of relevance to small loans, loans, and large loans, and the semantic scenes are sorted according to the value of the relevance degree, and the semantic scene corresponding to the obtained maximum relevance value is a small loan.
  • S206 The server parses the natural language information by using a preset semantic parsing method in a corresponding semantic scene to obtain an initial semantic parsing result.
  • the server can obtain the correspondence between the preset semantic scenes and semantic parsing methods, and obtain according to the obtained correspondence between the preset semantic scenes and semantic parsing methods.
  • the semantic parsing method corresponding to the semantic scene The server uses the semantic parsing method corresponding to the semantic scene to semantically parse the natural language information input by the user under the corresponding semantic scene.
  • the server obtains the semantic parsing mode corresponding to the semantic scene corresponding to the maximum relevance value according to the correspondence between the semantic scene corresponding to the maximum relevance value and the semantic parsing mode. Parse natural language information according to the semantic parsing method to obtain the original semantic parsing result. Initial inspection is performed on the original semantic parsing result by using a preset inspection rule, and the original semantic parsing result that conforms to the preset inspection rule is obtained, and the initial semantic parsing result is obtained according to the original semantic parsing result that conforms to the preset inspection rule.
  • the preset inspection rules are used for initial inspection of the original semantic parsing results, including checking the integrity and validity of the original semantic parsing results, and determining whether the original semantic parsing results are complete and valid. If the original semantic parsing result includes the semantic parsing of all keywords of the natural language information input by the user, it means that the original semantic parsing result passes the integrity check performed by the inspection rule. If the original semantic parsing result parses the keywords of natural language information semantically, it can effectively express the original input of natural language information, indicating that the original semantic parsing result has passed the validity check by the inspection rules.
  • the server obtains the natural language information “Shenzhen university student micro-loan distribution” input by the user, and according to the keyword category to which the keywords in the natural language information entered by the user, obtains the corresponding semantic scene as “micro-loan”, according to the preset
  • the corresponding relationship between the semantic scene and the semantic parsing method to obtain the semantic parsing method corresponding to the semantic scene as "small loan”, and perform a semantic parsing on the natural language information "Shenzhen university student small loan distribution” to obtain the initial semantic analysis
  • the results can include the following situations: "Shenzhen university students' small loan amount distribution", "Shenzhen university students' small loan area distribution" and "Shenzhen university students' small loan".
  • the server obtains a filtering value corresponding to the natural language information according to a preset correspondence between the keywords and the filtering value.
  • the server obtains a filtering value corresponding to the natural language information by acquiring a filtering mechanism corresponding to the keywords and according to a preset relationship between the preset filtering mechanism and the filtering value.
  • the screening mechanism There is a correspondence between keywords and filtering mechanisms, and different keywords correspond to different filtering mechanisms.
  • the filtering value corresponding to the keywords can be obtained according to the correspondence between the keywords and the filtering mechanism and the correspondence between the filtering mechanism and the filtering value. Furthermore, the obtained filtering value is a filtering value corresponding to natural language information.
  • the server obtains the natural language information input by the user, and extracts the keyword "small loan” in the natural language information, obtains a filtering mechanism "loan screening” corresponding to the keyword “small loan”, and according to the filtering mechanism and the filter value.
  • the preset correspondence relationship between them is obtained by the screening value "amount” corresponding to the screening mechanism "debit screening”.
  • the server uses the filtering value to filter the initial semantic parsing result, obtains a semantic parsing result that matches the filtering value, and sends the semantic parsing result to the terminal.
  • the server obtains the initial parsing data corresponding to the initial semantic parsing result, and uses the filtering value corresponding to the keyword to perform a filtering operation on the initial parsing data to obtain the initial parsing data through the filtering operation.
  • the server obtains the initial semantic parsing result corresponding to the initial parsing data that passed the filtering operation, obtains the semantic parsing result according to the initial semantic parsing result corresponding to the initial parsing data that passes the filtering operation, and sends the semantic parsing result to the terminal.
  • the filtering value is used to filter the initial parsing data corresponding to the initial semantic parsing result. Different natural language information corresponds to different filtering values.
  • the filtering value can be used to obtain the semantic parsing result consistent with the natural language information.
  • the initial parsing data corresponding to the initial semantic parsing result includes the initial data for parsing natural language information.
  • the server performs a semantic analysis on the natural language information “Shenzhen University Students’ Small Loan Distribution ”according to the semantic analysis method in the context of small loans.
  • the initial semantic analysis results obtained include the following situations:“ Shenzhen University Students ’Small Loan Amount Distribution "," Regional distribution of Shenzhen university students 'small loans "and” Shenzhen university students' small loans ".
  • the server uses the obtained filtering value to filter a plurality of possible initial semantic parsing results, and obtains the initial semantic results that match the filtering value.
  • the available filtering value corresponding to the natural language information is “amount”, that is, for the natural language information “Shenzhen University Student Small Loan Distribution”, using the filtering value “amount” to analyze a plurality of possible initial semantics
  • the results were screened, and the initial semantic parsing result that matched the screened value "amount” was "the distribution of the amount of small loans of Shenzhen university students".
  • the server receives a semantic parsing request for natural language information sent by the terminal, and takes a semantic scene corresponding to the natural language information.
  • natural language information is parsed by using a preset semantic parsing method to obtain an initial semantic parsing result.
  • a preset semantic parsing method According to the preset correspondence between keywords and filtered values, obtain filtered values corresponding to natural language information, and use the filtered values to filter the initial semantic parsing results to obtain the semantic parsing results that match the filtered values.
  • a step for acquiring a semantic scene of natural language information including:
  • the server extracts keywords in the natural language information, and obtains keyword attributes corresponding to different keywords.
  • the server can obtain the natural language information input by the user on the terminal, and extract keywords in the natural language information. Since keywords in natural language information correspond to different keyword attributes, the server can obtain the correspondence between keywords and keyword attributes, and then obtain the correspondence with keywords according to the correspondence between keywords and keyword attributes. Keyword attributes.
  • the server obtains a preset keyword category, and obtains a category attribute corresponding to the keyword category.
  • the server classifies keywords in the natural language information according to the category attribute according to a preset correspondence relationship between the category attribute and the keyword attribute.
  • the server sets a plurality of keyword categories in advance, and respectively sets corresponding category attributes for the plurality of keyword categories. Because there is a correspondence relationship between the keyword attribute and the category attribute corresponding to the keyword category, the keyword category to which each keyword belongs can be obtained according to the preset correspondence relationship between the category attribute and the keyword attribute, and each key Words are classified according to the corresponding category.
  • S308 The server obtains semantic scenes corresponding to different keyword categories according to a preset correspondence relationship between the keyword categories and semantic scenes.
  • the server may obtain a semantic scenario corresponding to a keyword category to which different keywords belong in the natural language information according to a preset correspondence relationship between the keyword categories and the semantic scenarios. .
  • the natural language information input by the user at the terminal is "how is the education distribution of male borrowers in Shanghai” as an example.
  • the server obtains the natural language information input by the user on the terminal, and receives a semantic parsing request for the natural language information sent by the terminal.
  • the natural language information "how is the distribution of the education of male borrowers in Shanghai”
  • the keywords "Shanghai”, “male” "Borrowers” and “educational distribution” to obtain the keyword category to which each keyword belongs.
  • “Shanghai” represents the geographical category and is used to restrict the geographical scope of the executing subject.
  • “Male borrower” belongs to the category of behavior subject and is used to limit execution The subject of operations in natural language information, "educational distribution” represents a further definition of the question to be retrieved.
  • the server classifies each keyword in the natural language information according to the keyword category, it obtains a semantic scene corresponding to the keyword category.
  • the semantic scene corresponding to the keyword category is the loan scene.
  • the server extracts keywords in the natural language information and obtains keyword attributes corresponding to different keywords. Obtain a preset keyword category, and obtain category attributes corresponding to the keyword category. Furthermore, according to the preset correspondence between the category attribute and the keyword attribute, the keywords in the natural language information are classified according to the category attribute, and according to the preset correspondence between the keyword category and the semantic scene, the difference is obtained. Semantic scenes corresponding to keyword categories. By obtaining the semantic scene corresponding to the keywords in the natural language information, the targeted semantic parsing in the corresponding semantic scene can be achieved, which further improves the accuracy of the semantic parsing.
  • a step of obtaining a semantic scene corresponding to natural language information including:
  • the server calculates the relevance value between the natural language information and different semantic scenes respectively; sorts the semantic scenes according to the magnitude of the relevance value, and obtains the semantic scene corresponding to the maximum relevance value.
  • the relevance value is used to judge the relevance between the natural language information input by the user and multiple semantic scenes.
  • the server obtains the degree of association between the natural language information and the semantic scene by calculating the correlation value between the natural language information input by the user and the different semantic scenes.
  • the calculated relevance values are sorted in ascending order, and the semantic scene corresponding to the largest relevance value is obtained as the semantic scene most relevant to the natural language information input by the user, that is, the semantic scene with the highest degree of relevance. .
  • semantic scenarios include: small loans, loans, and large loans, etc., respectively, to calculate "Shenzhen small borrower regional distribution" ,
  • the degree of relevance to small loans, loans, and large loans, and the semantic scenes are sorted according to the value of the relevance degree, and the semantic scene corresponding to the obtained maximum relevance value is a small loan.
  • the server calculates the correlation degree values between the natural language information and different semantic scenes separately, and sorts the semantic scenes according to the magnitude of the correlation degree value, so as to obtain the maximum correlation value corresponding Semantic scene. Since the semantic scene with the highest degree of relevance to natural language information is obtained, it is possible to determine whether the semantic scene meets the requirements of the corresponding natural language information before performing semantic parsing, and further improve the accuracy of semantic parsing.
  • a step of parsing natural language information to obtain an initial semantic parsing result by using a preset semantic parsing method in a corresponding semantic scene including:
  • the server obtains the correspondence between the semantic scene corresponding to the maximum relevance value and the semantic parsing mode, and obtains the semantic parsing mode corresponding to the semantic scene corresponding to the maximum relevance value; parses the natural language information according to the semantic parsing mode to obtain the original Semantic analysis results; use the preset inspection rules to perform initial inspection on the original semantic analysis results to obtain the original semantic analysis results that meet the preset inspection rules; and obtain the initial semantic analysis results based on the original semantic analysis results that meet the preset inspection rules .
  • the preset inspection rules are used for initial inspection of the original semantic parsing results, including checking the integrity and validity of the original semantic parsing results, and determining whether the original semantic parsing results are complete and valid. If the original semantic parsing result includes the semantic parsing of all keywords of the natural language information input by the user, it means that the original semantic parsing result passes the integrity check performed by the inspection rule. If the original semantic parsing result parses the keywords of natural language information semantically, it can effectively express the original input of natural language information, indicating that the original semantic parsing result has passed the validity check by the inspection rules.
  • the server obtains the natural language information “Shenzhen university student micro-loan distribution” input by the user, and according to the keyword category to which the keywords in the natural language information entered by the user, obtains the corresponding semantic scene as “micro-loan”, according to the preset
  • the corresponding relationship between the semantic scenes and the semantic parsing methods is used to obtain the semantic parsing method corresponding to the semantic scene as "small loans", and the natural language information "Shenzhen university students' small loan distribution" is semantically analyzed.
  • the server obtains the original semantic parsing result by obtaining the semantic parsing method corresponding to the semantic scene corresponding to the maximum relevance value, and analyzing the natural language information according to the semantic parsing method. Furthermore, a preset inspection rule is used to perform initial inspection on the original semantic parsing result to obtain an initial semantic parsing result that conforms to the preset inspection rule. It can test the completeness and validity of the original semantic parsing result, and obtain the initial semantic parsing result that meets the preset requirements. It further realizes the multi-faceted detection of the initial semantic parsing result, and improves the accuracy of the initial semantic parsing result.
  • a step of obtaining a filtering value corresponding to natural language information according to a preset correspondence between a keyword and a filtering value including:
  • the server obtains a filtering mechanism corresponding to the keywords; and obtains a filtering value corresponding to the natural language information according to the preset relationship between the filtering mechanism and the filtering value.
  • the screening mechanism There is a correspondence between keywords and filtering mechanisms, and different keywords correspond to different filtering mechanisms.
  • the filtering value corresponding to the keywords can be obtained according to the correspondence between the keywords and the filtering mechanism and the correspondence between the filtering mechanism and the filtering value. Furthermore, the obtained filtering value is a filtering value corresponding to natural language information.
  • the server obtains the filtering value corresponding to the natural language information by acquiring the filtering mechanism corresponding to the keywords and according to the preset relationship between the filtering mechanism and the filtering value. It is possible to further determine the association relationship between keywords and filter values, ensure that the obtained filter values correspond to natural language information input by the user, and improve work efficiency.
  • a step of filtering an initial semantic parsing result by using a filtering value to obtain a semantic parsing result that meets the filtering value, and sending the semantic parsing result to a terminal includes:
  • the server obtains the initial parsing data corresponding to the initial semantic parsing result; uses the filtering value corresponding to the keyword to perform the filtering operation on the initial parsing data; obtains the initial parsing data that passes the filtering operation, and obtains the corresponding parsing data that passes the filtering operation
  • the initial semantic parsing result of the based on the initial semantic parsing result corresponding to the initial parsing data through the filtering operation to obtain the semantic parsing result and send the semantic parsing result to the terminal.
  • the filtering value is used to filter the initial parsing data corresponding to the initial semantic parsing result. Different natural language information corresponds to different filtering values.
  • the filtering value can be used to obtain the semantic parsing result consistent with the natural language information.
  • the initial parsing data corresponding to the initial semantic parsing result includes the initial data for parsing natural language information.
  • the server obtains the initial parsing data corresponding to the initial semantic parsing result and uses the filtering value corresponding to the keyword to perform a filtering operation on the initial parsing data. Therefore, by implementing the screening of the initial semantic parsing results, the initial semantic parsing results matching the filtered values, and generating the semantic parsing results according to the initial semantic parsing results matching the filtered values, further ensuring the accuracy of the semantic parsing results.
  • steps in the flowchart of FIG. 2-3 are sequentially displayed in accordance with the directions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated in this document, the execution of these steps is not strictly limited, and these steps can be performed in other orders. Moreover, at least a part of the steps in Figure 2-3 may include multiple sub-steps or stages. These sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. These sub-steps or stages The execution order of is not necessarily performed sequentially, but may be performed in turn or alternately with at least a part of another step or a sub-step or stage of another step.
  • a natural language semantic parsing device including: a receiving module 402, a semantic scene acquisition module 404, an initial semantic parsing result acquisition module 406, a filtering value acquisition module 408, and The semantic parsing result obtaining module 410, wherein:
  • the receiving module 402 is configured to receive a semantic parsing request for natural language information sent by a terminal.
  • the semantic scene acquisition module 404 is configured to acquire a semantic scene corresponding to natural language information.
  • the initial semantic parsing result acquisition module 406 is configured to parse natural language information by using a preset semantic parsing method in a corresponding semantic scene to obtain an initial semantic parsing result.
  • the filtering value obtaining module 408 is configured to obtain a filtering value corresponding to natural language information according to a preset correspondence between a keyword and a filtering value.
  • the semantic parsing result acquisition module 410 is configured to filter the initial semantic parsing result by using the screening value to obtain a semantic parsing result that matches the screening value, and send the semantic parsing result to the terminal.
  • the server receives a semantic analysis request for natural language information sent by the terminal, and takes a semantic scene of the natural language information.
  • natural language information is parsed by using a preset semantic parsing method to obtain an initial semantic parsing result.
  • a preset semantic parsing method According to the preset correspondence between keywords and filtered values, obtain filtered values corresponding to natural language information, and use the filtered values to filter the initial semantic parsing results to obtain the semantic parsing results that match the filtered values.
  • a semantic scene acquisition module is provided for:
  • Extract keywords in natural language information and obtain keyword attributes corresponding to different keywords; obtain preset keyword categories, and obtain category attributes corresponding to keyword categories; according to the preset category attributes and keyword attributes
  • the corresponding relationship is used to classify keywords in natural language information according to category attributes; according to a preset correspondence relationship between keyword categories and semantic scenes, semantic scenes corresponding to different keyword categories are obtained.
  • the server extracts keywords in natural language information and obtains keyword attributes corresponding to different keywords. Obtain a preset keyword category, and obtain category attributes corresponding to the keyword category. Furthermore, according to the preset correspondence between the category attribute and the keyword attribute, the keywords in the natural language information are classified according to the category attribute, and according to the preset correspondence between the keyword category and the semantic scene, the difference is obtained. Semantic scenes corresponding to keyword categories. By obtaining the semantic scene corresponding to the keywords in the natural language information, the targeted semantic parsing in the corresponding semantic scene can be achieved, which further improves the accuracy of the semantic parsing.
  • a semantic scene acquisition module is provided, which is further used for:
  • the server calculates the correlation degree values between the natural language information and different semantic scenes separately, and sorts the semantic scenes according to the magnitude of the correlation degree values, so as to obtain the semantic scene corresponding to the maximum correlation degree value. Since the semantic scene with the highest degree of relevance to natural language information is obtained, it is possible to determine whether the semantic scene meets the requirements of the corresponding natural language information before performing semantic parsing, and further improve the accuracy of semantic parsing.
  • an initial semantic parsing result acquisition module is provided for:
  • the server obtains the original semantic parsing result by acquiring the semantic parsing method corresponding to the semantic scene corresponding to the maximum relevance value, and analyzing the natural language information according to the semantic parsing method. Furthermore, a preset inspection rule is used to perform initial inspection on the original semantic parsing result to obtain an initial semantic parsing result that conforms to the preset inspection rule. It can test the completeness and validity of the original semantic parsing result, and obtain the initial semantic parsing result that meets the preset requirements. It further realizes the multi-faceted detection of the initial semantic parsing result, and improves the accuracy of the initial semantic parsing result.
  • a filtering value obtaining module is provided, which is used for:
  • the server obtains a filtering value corresponding to natural language information by acquiring a filtering mechanism corresponding to a keyword, and according to a correspondence relationship between a preset filtering mechanism and the filtering value. It is possible to further determine the relationship between keywords and filtered values, ensure that the obtained filtered values correspond to natural language information input by the user, and improve work efficiency.
  • a semantic parsing result acquisition module is provided for:
  • initial parsing data corresponding to the initial semantic parsing result use the filtering value corresponding to the keyword to perform the filtering operation on the initial parsing data; obtain the initial parsing data that passes the filtering operation, and obtain the parsing data that corresponds to the initial parsing data that passes the filtering operation
  • Initial semantic parsing result obtain the semantic parsing result according to the initial semantic parsing result corresponding to the initial parsing data through the filtering operation, and send the semantic parsing result to the terminal.
  • the server obtains the initial parsing data corresponding to the initial semantic parsing result, and uses the filtering value corresponding to the keyword to perform a filtering operation on the initial parsing data. Therefore, by implementing the screening of the initial semantic parsing results, the initial semantic parsing results matching the filtered values, and generating the semantic parsing results according to the initial semantic parsing results matching the filtered values, further ensuring the accuracy of the semantic parsing results.
  • Each module in the above-mentioned natural language semantic parsing device may be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the hardware form 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 calls and performs the operations corresponding to the above modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 5.
  • the computer device includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile computer-readable storage medium and an internal memory.
  • the non-volatile computer-readable storage medium stores an operating system, computer-readable instructions, and a database.
  • the internal memory provides an environment for operating systems and computer-readable instructions in a non-volatile computer-readable storage medium.
  • the database of the computer equipment is used to store semantic parsing data of natural language.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer-readable instructions are executed by a processor to implement a natural language semantic parsing method.
  • FIG. 5 is only a block diagram of a part of the structure related to the solution of the application, and does not constitute a limitation on the computer equipment to which the solution of the application is applied.
  • the specific computer equipment may be Include more or fewer parts than shown in the figure, or combine certain parts, or have a different arrangement of parts.
  • a computer device includes a memory and one or more processors.
  • the memory stores computer-readable instructions.
  • the method for preprocessing the imbalanced sample data provided in any one of the embodiments of the present application is implemented. A step of.
  • One or more non-volatile computer-readable storage media storing computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors implement one of the embodiments of the present application Provides steps for pre-processing methods for unbalanced sample data.
  • 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 can include random access memory (RAM) or external cache memory.
  • RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM dual data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Synchlink DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

L'invention concerne un procédé basé sur des ressources de données destiné à une analyse sémantique de langage naturel, consistant : à recevoir une demande d'analyse sémantique d'informations de langage naturel, envoyées par un terminal, et à obtenir un contexte sémantique correspondant aux informations de langage naturel. Dans le contexte sémantique correspondant, à l'aide d'un procédé d'analyse sémantique prédéfini, destiné à l'analyse des informations de langage naturel permettant d'obtenir un résultat initial d'analyse sémantique, et selon une corrélation entre un mot-clé prédéfini et une valeur de filtre, une valeur de filtre correspondant aux informations de langage naturel est obtenue. La valeur de filtre sert à filtrer le résultat initial d'analyse sémantique, afin d'obtenir un résultat d'analyse sémantique concordant avec la valeur de filtre, et le résultat d'analyse sémantique est envoyé au terminal.
PCT/CN2019/071251 2018-09-18 2019-01-11 Procédé d'analyse sémantique de langage naturel, appareil, dispositif informatique et support d'informations WO2020057023A1 (fr)

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CN201811089544.4A CN109359295A (zh) 2018-09-18 2018-09-18 自然语言的语义解析方法、装置、计算机设备和存储介质
CN201811089544.4 2018-09-18

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