CN109829041B - Question processing method and device, computer equipment and computer readable storage medium - Google Patents
Question processing method and device, computer equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN109829041B CN109829041B CN201811587301.3A CN201811587301A CN109829041B CN 109829041 B CN109829041 B CN 109829041B CN 201811587301 A CN201811587301 A CN 201811587301A CN 109829041 B CN109829041 B CN 109829041B
- Authority
- CN
- China
- Prior art keywords
- entity
- type
- question
- category
- answer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003672 processing method Methods 0.000 title description 5
- 238000000034 method Methods 0.000 claims abstract description 39
- 241000220225 Malus Species 0.000 description 19
- 235000013399 edible fruits Nutrition 0.000 description 15
- 235000021016 apples Nutrition 0.000 description 12
- 238000004891 communication Methods 0.000 description 9
- 235000013305 food Nutrition 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 240000000716 Durio zibethinus Species 0.000 description 6
- 235000006025 Durio zibethinus Nutrition 0.000 description 6
- 230000006870 function Effects 0.000 description 3
- 240000008790 Musa x paradisiaca Species 0.000 description 2
- 235000018290 Musa x paradisiaca Nutrition 0.000 description 2
- 244000107946 Spondias cytherea Species 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
Images
Landscapes
- User Interface Of Digital Computer (AREA)
Abstract
The present disclosure provides a problem handling method, comprising: receiving a question; judging the type of the problem, if the type of the problem is the target type, judging whether the problem has an entity, and if the type of the problem is not the target type and/or the problem does not have the entity, not processing the problem; identifying the entity in the target type problem and determining the category of the entity; and determining an answer to the question based on the category of the entity. The disclosure also provides a problem handling apparatus, a computer device and a computer readable storage medium.
Description
Technical Field
The disclosure relates to a problem handling method, a problem handling device, a computer device and a computer readable storage medium.
Background
In a human-computer interaction chatting system, a person setting (i.e., character setting) problem is a kind of problem that needs to be specially handled, such as what name you call, what you like to eat, and so on. In the process of processing human set questions, a relatively difficult question is human set consistency, the human set consistency is that for the same human set question, whether the robot can give answers meeting human set settings or not, for example, one of the human sets set by the robot is like eating apples, so for all questions asking what the robot likes, the robot should answer the like eating apples, and the human set consistency is the human set consistency. Examples of problems are: you like what you eat, what you like to eat, do you like to eat apples, do you like to durian, do you like to dislike eating, etc.
If we can exhaust all the questions of the user, we can allocate a correct answer to each question, and the consistency of human set is naturally solved, but we cannot exhaust the questions of the user, which is determined by the natural characteristics of language, and the methods in the prior art cannot well solve the consistency of human set.
The methods in the prior art are based on rules and generation. The rule-based method has high accuracy, but has low coverage rate and can only cover a small part of human-set problems. Although the coverage rate of the generation-based method is higher than that of the rule-based method, the generation model cannot specially solve the human set consistency, and the human set problem has too many characteristics and needs special treatment.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a problem processing method, apparatus, computer device, and computer-readable storage medium.
According to a first aspect of the present disclosure, there is provided a problem handling method, comprising: receiving a question; judging the type of the problem, if the type of the problem is the target type, judging whether the problem has an entity, and if the type of the problem is not the target type and/or the problem does not have the entity, not processing the problem; identifying entities in the target type problem and determining the category of the entities; based on the category of the entity, an answer to the question is determined.
According to at least one embodiment of the present disclosure, determining the category of the entity includes: determining the lowest layer category of the entity, and determining the uppermost layer category of the entity based on the lowest layer category of the entity; based on the top level category of the entity, an answer to the question is determined.
According to at least one embodiment of the present disclosure, the target type includes a hobby question, a skill question, or a basic information question.
According to at least one embodiment of the present disclosure, an entity is identified based on a knowledge-graph; the category of the entity is determined based on the knowledge-graph.
According to at least one embodiment of the present disclosure, the question is in the form of voice or text.
According to at least one embodiment of the present disclosure, the answer to the question is from a preset human-set answer.
According to at least one embodiment of the present disclosure, the knowledge-graph stores at least two levels of categories of entities.
According to a second aspect of the present disclosure, there is provided a question processing apparatus including: a receiving unit that receives the question; the judging part judges the type of the problem, judges whether the problem has an entity if the type of the problem is the target type, and does not process the problem if the type of the problem is not the target type and/or the problem does not have the entity; an entity type determining part which identifies the entity and determines the type of the entity; and an answer determination section that determines an answer to the question based on the category of the entity.
According to a third aspect of the present disclosure, a computer device comprises: a memory storing computer execution instructions; and a processor executing computer executable instructions stored by the memory to cause the processor to perform the problem-handling method as in the first aspect.
According to a fourth aspect of the present disclosure, a computer-readable storage medium has stored therein computer-executable instructions for implementing the problem-handling method as in the first aspect when executed by a processor.
According to a fifth aspect of the disclosure, a handheld device comprises: a memory to store instructions; and a processor executing the instructions stored by the memory to cause the processor to perform the problem-handling method as in the first aspect. The handheld device may be a cell phone, watch, or the like.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
FIG. 1 is a flow diagram of a problem handling method according to one embodiment of the present disclosure.
FIG. 2 is a flow diagram of a problem handling method according to one embodiment of the present disclosure.
FIG. 3 is a flow diagram of a problem handling method according to one embodiment of the present disclosure.
FIG. 4 is a flow diagram of a problem handling method according to one embodiment of the present disclosure.
FIG. 5 is a schematic diagram of a problem handling device according to one embodiment of the present disclosure.
FIG. 6 is a schematic diagram of a problem handling device according to one embodiment of the present disclosure.
FIG. 7 is a schematic structural diagram of a computer device or handheld device according to one embodiment of the present disclosure.
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In one embodiment of the present disclosure, a question processing method is provided, as shown in fig. 1, including a question receiving step S11, a question type determining step S12, a category determining step S13 of an entity in a question, and an answer determining step S14 of the question.
In more detail, as shown in fig. 2, when a question of a user is received, first, a determination is made as to the type of the question, whether the type of the question is a target type (which may be implemented using the classic text cnn model), and the target type includes a hobby question, a skill question, or a basic information question, and if the type of the question is the target type, whether there is an entity in the question (taking the hobby question as an example, the entity is an apple, a banana, or the like, i.e., a hobby subject), and if the type of the question is not the target type and/or there is no entity in the question, the question is not processed. An entity (e.g. apple) in a question of the target type (e.g. a hobby question) is identified (which may be implemented using a classical CRF model), a category of the entity (category e.g. fruit) is determined, and based on the category of the entity (e.g. fruit), it can be determined what entity, i.e. object, the user is hobbying the robot, and thus the answer to the user's question is determined from the human set answers.
When the entity is identified and the category of the entity is determined, the entity is identified based on the knowledge graph, and the category of the entity is determined based on the knowledge graph. Stored in the knowledge graph are various entities such as apples, durian and the like, and a category corresponding to each entity, such as apple category being fruits and piano category being instruments. Categories may also have a hierarchical relationship, such as the upper level of fruit being food.
In one embodiment of the present disclosure, a question processing method is provided, as shown in fig. 3, including a question receiving step S21, a question type determining step S22, a top category determining step S23 of an entity in a question, and an answer determining step S24.
In more detail, as shown in fig. 4, when a question of a user is received, first, a determination is made as to the type of the question, whether the type of the question is a target type (which may be implemented using the classic text cnn model), and the target type includes a hobby question, a skill question, or a basic information question, and if the type of the question is the target type, whether there is an entity in the question (taking the hobby question as an example, the entity is an apple, a banana, or the like, i.e., a hobby subject), and if the type of the question is not the target type and/or there is no entity in the question, the question is not processed. Identifying entities (e.g. apples) in a target type of question (e.g. a hobby question), which may be implemented using a classical CRF model, determining the lowest category (category, e.g. fruits) of the entities, determining the highest category (e.g. food) of the entities based on the lowest category (e.g. fruits) of the entities, determining what entities, i.e. objects, the user likes to the robot based on the highest category of the entities, and determining the answer to the user's question from the human set answers.
When the entity is identified and the category of the entity is determined, the entity is identified based on the knowledge graph, and the category of the entity is determined based on the knowledge graph. Stored in the knowledge graph are various entities such as apples, durian and the like, and a category corresponding to each entity, such as apple category being fruits and piano category being instruments. The categories may also have a hierarchical relationship, with fruit being the lowermost category of apples, the upper level of fruit being food, and food being the uppermost category of apples.
In one embodiment of the present disclosure, there is provided a question processing apparatus 300, as shown in fig. 5, including a receiving section 31, a judging section 32, an entity type determining section 33, and an answer determining section 34. Wherein the receiving section 31 receives the question; the judging part 32 judges the type of the problem, if the type of the problem is the target type, whether the problem has an entity, if the type of the problem is not the target type and/or the problem has no entity, the problem processing device does not process the problem; the entity type identification unit 33 identifies the entity and identifies the type of the entity; the answer determination section 34 determines an answer to the question based on the category of the entity.
In one embodiment of the present disclosure, the entity type determination unit 33 of the problem processing apparatus 300 identifies an entity, determines a lowest layer type of the entity, and determines an uppermost layer type of the entity based on the lowest layer type of the entity; the answer determination unit 34 determines an answer to the question based on the uppermost category of the entity.
In one embodiment of the present disclosure, as shown in fig. 6, the question processing apparatus 300 further includes a knowledge map storage 35 and a human-set answer storage 36. The knowledge graph storage unit 35 stores various entities, such as apples, durian, and the like, and the category corresponding to each entity, such as apple category is fruit and piano category is musical instrument. The categories may also have a hierarchical relationship, such as the upper level of fruit being food, fruit being the lowest category of apples, food being the uppermost category of apples. The human-set answer storage unit 36 stores answers to be set to the characters assigned to the robot, for example, if the robot is a twentieth-old man who likes to eat apples and plays pianos and is named kaiwen, the twentieth-old man and the name kaiwen are set, and the person who likes to eat apples and plays pianos are answers to be set to the characters assigned to the robot, that is, human-set answers.
In one embodiment of the present disclosure, the entity type determination unit 33 identifies an entity type (apple, durian, piano, or the like) in the question based on the knowledge graph stored in the knowledge graph storage unit 35, determines the type (type, e.g., fruit) of the entity, and can determine what entity the user likes (i.e., what object the user likes) in asking the robot based on the type (e.g., fruit) of the entity, so that the answer determination unit 34 determines the answer to the question of the user from the human-set answers stored in the human-set answer determination unit 36 based on the type of the entity.
In one embodiment of the present disclosure, the entity type determination unit 33 identifies an entity type (apple, durian, piano, or the like) in the question based on the knowledge graph stored in the knowledge graph storage unit 35, determines a lowest layer type (type such as fruit) of the entity, and determines a highest layer type (such as food) of the entity based on the lowest layer type of the entity, and based on the highest layer type (such as food) of the entity, it is possible to determine what entity the user likes (i.e., what object the robot likes) in question, and the answer determination unit 34 determines the answer to the question of the user from the human-set answers stored in the human-set answer determination unit 36 based on the highest layer type of the entity.
The present disclosure also provides a computer device or handheld device, as shown in fig. 7, the device comprising: a communication interface 1000, a memory 2000, and a processor 3000. The communication interface 1000 is used for communicating with an external device to perform data interactive transmission. The memory 2000 has stored therein a computer program that is executable on the processor 3000. The processor 3000 implements the method in the above-described embodiments when executing the computer program. The number of the memory 2000 and the processor 3000 may be one or more.
The memory 2000 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the communication interface 1000, the memory 2000 and the processor 3000 are implemented independently, the communication interface 1000, the memory 2000 and the processor 3000 may be connected to each other through a bus to complete communication therebetween. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not represent only one bus or one type of bus.
Optionally, in a specific implementation, if the communication interface 1000, the memory 2000, and the processor 3000 are integrated on a chip, the communication interface 1000, the memory 2000, and the processor 3000 may complete communication with each other through an internal interface.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present disclosure includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the implementations of the present disclosure. The processor performs the various methods and processes described above. For example, method embodiments in the present disclosure may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as a memory. In some embodiments, some or all of the computer software program may be loaded and/or installed via memory and/or a communication interface. When the computer software program is loaded into memory and executed by a processor, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the processor may be configured to perform one of the methods described above by any other suitable means (e.g., by means of firmware).
The logic and/or steps represented in the flowcharts or otherwise described herein may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps of the method implementing the above embodiments may be implemented by hardware instructions associated with a program, which may be stored in a computer-readable storage medium, and which, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
In the description herein, reference to the description of the terms "one embodiment/implementation," "some embodiments/implementations," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/implementation or example is included in at least one embodiment/implementation or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of illustration of the disclosure and are not intended to limit the scope of the disclosure. Other variations or modifications may occur to those skilled in the art, based on the foregoing disclosure, and are still within the scope of the present disclosure.
Claims (9)
1. A problem handling method, comprising:
receiving a question;
judging the type of a problem, if the type of the problem is a target type, judging whether an entity exists in the problem, and if the type of the problem is not the target type and/or the problem does not have the entity, not processing the problem;
identifying an entity in a target type problem, determining the lowest layer category of the entity, and determining the uppermost layer category of the entity based on the lowest layer category of the entity; and
determining an answer to the question based on the top level category of the entity.
2. The process of claim 1, wherein the target type comprises a hobby question, a skill question, or a basic information question.
3. The process of claim 1 or 2, wherein the entities are identified based on a knowledge graph; determining a category of the entity based on a knowledge graph.
4. The process of claim 3, wherein the knowledge-graph stores at least two levels of categories for the entity.
5. A process according to claim 1 or 2, characterized in that the question is in the form of speech or text.
6. A process according to claim 1 or 2, wherein the answer to the question is from a preset human answer.
7. A problem handling apparatus, comprising:
a receiving section that receives a question;
a determination unit that determines a type of the problem, determines whether the problem has an entity if the type of the problem is a target type, and does not process the problem if the type of the problem is not the target type and/or the problem has no entity;
an entity type determination unit that identifies the entity, determines a lowest type of the entity, and determines an uppermost type of the entity based on the lowest type of the entity; and
an answer determination section that determines an answer to the question based on an uppermost category of the entity.
8. A computer device, comprising:
a memory storing computer execution instructions; and
a processor executing computer-executable instructions stored by the memory to cause the processor to perform the issue handling method of any of claims 1 to 6.
9. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, implement the problem-handling method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811587301.3A CN109829041B (en) | 2018-12-25 | 2018-12-25 | Question processing method and device, computer equipment and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811587301.3A CN109829041B (en) | 2018-12-25 | 2018-12-25 | Question processing method and device, computer equipment and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109829041A CN109829041A (en) | 2019-05-31 |
| CN109829041B true CN109829041B (en) | 2021-06-29 |
Family
ID=66861150
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811587301.3A Active CN109829041B (en) | 2018-12-25 | 2018-12-25 | Question processing method and device, computer equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109829041B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112836027A (en) * | 2019-11-25 | 2021-05-25 | 京东方科技集团股份有限公司 | Method for determining text similarity, question answering method and question answering system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104731895A (en) * | 2015-03-18 | 2015-06-24 | 北京京东尚科信息技术有限公司 | Auto-answer method and device |
| CN104850539A (en) * | 2015-05-28 | 2015-08-19 | 宁波薄言信息技术有限公司 | Natural language understanding method and travel question-answering system based on same |
| CN104933204A (en) * | 2015-07-15 | 2015-09-23 | 武汉杰宇天成文化咨询服务有限公司 | Intelligent network response method and device |
| WO2017028723A1 (en) * | 2015-08-19 | 2017-02-23 | 阿里巴巴集团控股有限公司 | Information processing method and device |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012047541A1 (en) * | 2010-09-28 | 2012-04-12 | International Business Machines Corporation | Providing answers to questions using multiple models to score candidate answers |
| JP5710581B2 (en) * | 2012-12-18 | 2015-04-30 | 日本電信電話株式会社 | Question answering apparatus, method, and program |
| JP6480380B2 (en) * | 2016-05-17 | 2019-03-06 | 日本電信電話株式会社 | Table cell search apparatus, method, and program |
| US10628747B2 (en) * | 2017-02-13 | 2020-04-21 | International Business Machines Corporation | Cognitive contextual diagnosis, knowledge creation and discovery |
| CN107169105A (en) * | 2017-05-17 | 2017-09-15 | 北京品智能量科技有限公司 | Question and answer system and method for vehicle |
| CN107862000B (en) * | 2017-10-22 | 2018-10-30 | 北京市农林科学院 | A kind of agricultural technology consulting interactive method |
| CN107958091A (en) * | 2017-12-28 | 2018-04-24 | 北京贝塔智投科技有限公司 | A kind of NLP artificial intelligence approaches and interactive system based on financial vertical knowledge mapping |
| CN108153901B (en) * | 2018-01-16 | 2022-04-19 | 北京百度网讯科技有限公司 | Knowledge graph-based information pushing method and device |
| CN108415950B (en) * | 2018-02-01 | 2021-03-23 | 腾讯科技（深圳）有限公司 | Hypernym aggregation method and device |
-
2018
- 2018-12-25 CN CN201811587301.3A patent/CN109829041B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104731895A (en) * | 2015-03-18 | 2015-06-24 | 北京京东尚科信息技术有限公司 | Auto-answer method and device |
| CN104850539A (en) * | 2015-05-28 | 2015-08-19 | 宁波薄言信息技术有限公司 | Natural language understanding method and travel question-answering system based on same |
| CN104933204A (en) * | 2015-07-15 | 2015-09-23 | 武汉杰宇天成文化咨询服务有限公司 | Intelligent network response method and device |
| WO2017028723A1 (en) * | 2015-08-19 | 2017-02-23 | 阿里巴巴集团控股有限公司 | Information processing method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109829041A (en) | 2019-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200265843A1 (en) | Speech broadcast method, device and terminal | |
| US8909528B2 (en) | Method and system for prompt construction for selection from a list of acoustically confusable items in spoken dialog systems | |
| CN105893535B (en) | Intelligent answer method, knowledge base optimization method and device, Intelligence repository | |
| CN104718569A (en) | Improving phonetic pronunciation | |
| JP2018054850A (en) | Information processing system, information processor, information processing method, and program | |
| CN108549662A (en) | The supplement digestion procedure and device of semantic analysis result in more wheel sessions | |
| CN108519998B (en) | Problem guiding method and device based on knowledge graph | |
| US11526541B1 (en) | Method for collaborative knowledge base development | |
| JP2019133127A (en) | Voice recognition method, apparatus and server | |
| CN109003492B (en) | Topic selection device and terminal equipment | |
| CN113571196B (en) | Method and device for constructing medical training samples, and medical text retrieval method | |
| JP2018055422A (en) | Information processing system, information processor, information processing method, and program | |
| CN112686051A (en) | Semantic recognition model training method, recognition method, electronic device, and storage medium | |
| JP7352761B2 (en) | Expert answer prediction system | |
| CN110264255A (en) | Resource transfers, red packet distribution method, device and equipment | |
| KR20180105501A (en) | Method for processing language information and electronic device thereof | |
| CN109829041B (en) | Question processing method and device, computer equipment and computer readable storage medium | |
| CN112417107A (en) | Information processing method and device | |
| JP2022035167A (en) | robot | |
| CN117238532B (en) | Intelligent follow-up method and device | |
| CN111400484B (en) | Keyword extraction method and system | |
| CN115670459B (en) | Information processing device, method, terminal equipment and storage medium | |
| CN118364101A (en) | Text classification method and device | |
| CN114707823B (en) | Interactive content scoring method and device, electronic equipment and storage medium | |
| CN109543091A (en) | Method for pushing, device and the terminal of application program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220507 Address after: 210034 floor 8, building D11, Hongfeng Science Park, Nanjing Economic and Technological Development Zone, Jiangsu Province Patentee after: New Technology Co.,Ltd. Patentee after: Volkswagen (China) Investment Co., Ltd Address before: 100094 1001, 10th floor, office building a, 19 Zhongguancun Street, Haidian District, Beijing Patentee before: MOBVOI INFORMATION TECHNOLOGY Co.,Ltd. |