CN108170708B - Vehicle entity identification method, electronic equipment, storage medium and system - Google Patents
Vehicle entity identification method, electronic equipment, storage medium and system Download PDFInfo
- Publication number
- CN108170708B CN108170708B CN201711185953.XA CN201711185953A CN108170708B CN 108170708 B CN108170708 B CN 108170708B CN 201711185953 A CN201711185953 A CN 201711185953A CN 108170708 B CN108170708 B CN 108170708B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- role
- entity
- roles
- sequence
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2452—Query translation
- G06F16/24522—Translation of natural language queries to structured queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/232—Orthographic correction, e.g. spell checking or vowelisation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Machine Translation (AREA)
- Character Discrimination (AREA)
Abstract
The invention discloses a vehicle entity identification method, which comprises the steps of generating a role set, extracting roles, identifying vehicle entities, updating the role set, generating the vehicle role set by adopting vehicle roles of an extraction standard vehicle type library and a vehicle language library, extracting the vehicle roles of an original vehicle text according to the vehicle role set, mapping the original vehicle roles into vehicle role sequences, and comparing vehicle entity character strings of the vehicle role sequences with the vehicle entities of the standard vehicle type library to obtain the vehicle entities with the highest similarity; the invention relates to an electronic device and a readable storage medium for executing a vehicle entity identification method; the invention also relates to a vehicle entity identification system; the method and the system realize accurate vehicle entity identification on the original vehicle text input by the user, update the original vehicle role to the vehicle role set, greatly reduce the workload of manual mapping, do not need to make a large number of rules manually, and have good expansibility, high accuracy and good compatibility.
Description
Technical Field
The invention relates to the technical field of vehicle entity identification, in particular to a vehicle entity identification method, electronic equipment, a storage medium and a system.
Background
In the vehicle information search, a user inputs text information according to personal habits, the text input forms are various, such as standard vehicle entities like 'Baoma' and 'Audi', and the text input forms also include the text input which cannot be processed by a search engine like 'how much money the Baoma' and 'Audi' A615, or the input of information including wrongly written characters, such as 'Baoma' and 'Audi', and the like; in this application scenario, it is necessary to be able to accurately identify the vehicle entity; however, since the vehicle entity has no obvious boundary vocabulary, the traditional named entity recognition is triggered by scanning the boundary vocabulary, for example, people have first names with surnames as the boundary, and places have names with words such as hospital, company, county, village, etc. as the boundary, so the traditional entity recognition method is difficult to effectively recognize the vehicle entity; the text input by the user contains Chinese and English information and numbers, such as price, automobile configuration and the like, which bring great interference to vehicle entity extraction, and the compatibility of the existing method is poor.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the purposes of the invention is to provide a vehicle entity identification method, which generates a vehicle role set by extracting a small amount of vehicle roles, extracts roles of original vehicle texts according to the vehicle role set, identifies the vehicle entities of the extracted roles, and updates the original vehicle roles to the vehicle role set, thereby greatly reducing the workload of manual mapping, avoiding the need of manually making a large number of rules, and having good expansibility, high accuracy and good compatibility.
The invention provides a vehicle entity identification method, which comprises the following steps:
extracting roles, namely extracting vehicle roles from an original vehicle text to obtain a plurality of original vehicle roles, and mapping the original vehicle roles into a vehicle role sequence;
and identifying the vehicle entity, acquiring the vehicle entity character string of the vehicle role sequence, and comparing the vehicle entity character string with the vehicle entities in the standard vehicle type library to acquire the vehicle entity with the highest similarity.
Further, the method also comprises the steps of generating a role set and updating the role set, wherein the role set generated in the step is a vehicle role set generated for vehicle roles of the standard vehicle type library and the vehicle corpus; the step updates the set of roles to add the original vehicle role to the set of vehicle roles.
Further, the step of generating the set of roles includes: and extracting vehicle entity roles, vehicle component element roles, upper roles of the vehicle entities and lower roles of the vehicle entities from the standard vehicle type library and the vehicle corpus, and generating the vehicle role set according to the vehicle entity roles, the vehicle component element roles, the upper roles of the vehicle entities and the lower roles of the vehicle entities.
Further, the step of extracting roles includes: performing word segmentation processing on the original vehicle text, performing vehicle entity role, vehicle component element role, vehicle entity upper role and vehicle entity lower role extraction on word segmentation processing results according to the vehicle role set, and mapping role extraction results into the vehicle role sequence.
Further, the step of vehicle entity identification comprises: judging whether the vehicle role sequence contains a vehicle entity role, if so, extracting the vehicle entity role of the vehicle role sequence, and mapping the vehicle entity role of the vehicle role sequence into a vehicle entity; otherwise, matching the vehicle role sequence with a vehicle dictionary tree to obtain a vehicle entity character string of the vehicle role sequence, mapping the vehicle entity character string into a vehicle entity text, and comparing the vehicle entity text with the vehicle entities in the standard vehicle type library to obtain the vehicle entity with the highest similarity.
An electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing a vehicle entity identification method.
A computer-readable storage medium, on which a computer program is stored, which computer program is executed by a processor to perform a vehicle entity identification method as described above.
A vehicle entity identification system comprises a vehicle role extraction module and a vehicle entity identification module, wherein the vehicle role extraction module performs vehicle role extraction on an original vehicle text to obtain a plurality of original vehicle roles, and the original vehicle roles are mapped into a vehicle role sequence; and the vehicle entity identification module acquires the vehicle entity character string of the vehicle role sequence, compares the vehicle entity character string with the vehicle entities in a standard vehicle type library and acquires the vehicle entity with the highest similarity.
The vehicle role set generation module extracts vehicle roles of the standard vehicle type library and the vehicle corpus to generate a vehicle role set; the vehicle entity identification module further comprises a vehicle role set update module that adds the original vehicle role to the vehicle role set.
Further, the vehicle role set generating module extracts vehicle entity roles, vehicle component element roles, vehicle entity upper roles, and vehicle entity lower roles of the standard vehicle type library and the vehicle corpus, and generates the vehicle role set according to the vehicle entity roles, vehicle component element roles, vehicle entity upper roles, and vehicle entity lower roles.
Further, the vehicle role extraction module further comprises a word segmentation module, the word segmentation module performs word segmentation on the original vehicle text, and the vehicle role extraction module performs vehicle entity role, vehicle component element role, vehicle entity upper role and vehicle entity lower role extraction on word segmentation processing results according to the vehicle role set, and maps the role extraction results into the vehicle role sequence.
Further, the vehicle role extraction module further comprises a vehicle entity character string extraction module and a similarity comparison module, the vehicle entity character string extraction module matches the vehicle role sequence with a vehicle dictionary tree to obtain a vehicle entity character string of the vehicle role sequence, the similarity comparison module maps the vehicle entity character string into a vehicle entity text, and the vehicle entity text is compared with the vehicle entities in the standard vehicle type library to obtain the vehicle entity with the highest similarity.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the vehicle role set is generated by extracting the vehicle roles of the standard vehicle type library and the vehicle corpus, the vehicle roles are extracted from the original vehicle text according to the vehicle role set to obtain the original vehicle roles, the original vehicle roles are mapped into the vehicle role sequence, the vehicle entity character strings of the vehicle role sequence are obtained, the vehicle entity character strings are compared with the vehicle entities of the standard vehicle type library to obtain the vehicle entities with the highest similarity, and the original vehicle roles are added to the vehicle role set, so that the workload of manual mapping is greatly reduced, a large number of rules do not need to be manually formulated, and the method is good in expansibility, high in accuracy and good in compatibility.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a vehicle entity identification method of the present invention;
FIG. 2 is a block diagram of a vehicle entity identification system according to the present invention;
fig. 3 is a schematic diagram of a vehicle entity identification system according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
A vehicle entity identification method, as shown in fig. 1, comprising the steps of:
generating a role set, extracting vehicle roles of a standard vehicle type library and a vehicle language library to generate a vehicle role set, wherein the standard vehicle type library stores standard vehicle entities such as 'galloping', 'BMW', 'Audi' and the like, and the vehicle language library stores user input data such as 'consider BMW', 'Audi price' and the like; preferably, the step of generating the set of roles comprises: extracting vehicle entity roles, vehicle composition element roles, upper roles of the vehicle entities and lower roles of the vehicle entities from the standard vehicle type library and the vehicle corpus, wherein the upper roles of the vehicle entities are roles before the vehicle entities in the vehicle text, the lower roles of the vehicle entities are roles after the vehicle entities in the vehicle text, generating a vehicle role set according to the vehicle entity role, the vehicle composition element role, the upper role of the vehicle entity and the lower role of the vehicle entity, only counting a small number of vehicle roles in the step, other vehicle roles are expanded to the role set through the step updating role set, the workload of manual mapping is greatly reduced, a large number of rules do not need to be made manually, the expansibility is good, and the vehicle entities in the standard vehicle type library are added to the role set, so that the accuracy and the identification efficiency of vehicle entity identification are improved.
In one embodiment, the extracted vehicle entity role is coded as "C", such as gallop, bmw, audi, the vehicle component element role is coded as "D", such as gallop, edi, audi, the above role of the vehicle entity is coded as "K", the below role of the vehicle entity is coded as "L", such as price, offer, considering, replacing, asking, and the other roles besides the above are coded as "a".
Extracting roles, namely extracting the roles of the vehicles from the original vehicle text according to the vehicle role set to obtain a plurality of original vehicle roles, and mapping the original vehicle roles into a vehicle role sequence; preferably, the step of role extraction comprises: performing word segmentation on an original vehicle text, extracting vehicle entity roles, vehicle component element roles, upper roles of vehicle entities and lower roles of vehicle entities from word segmentation processing results according to a vehicle role set, and mapping the role extraction results into a vehicle role sequence.
Identifying the vehicle entity, acquiring a vehicle entity character string of the vehicle role sequence, comparing the vehicle entity character string with the vehicle entities in the standard vehicle type library, and acquiring the vehicle entity with the highest similarity; preferably, the step of vehicle entity identification comprises: judging whether the vehicle role sequence contains a vehicle entity role, if so, extracting the vehicle entity role of the vehicle role sequence, and mapping the vehicle entity role of the vehicle role sequence into a vehicle entity; otherwise, matching the vehicle role sequence with the vehicle dictionary tree to obtain a vehicle entity character string of the vehicle role sequence, mapping the vehicle entity character string into a vehicle entity text, and comparing the vehicle entity text with vehicle entities in a standard vehicle type library to obtain a vehicle entity with the highest similarity.
Updating the role set, adding the original vehicle role to the vehicle role set, and adding the upper role of the vehicle entity and the lower role of the vehicle entity to the vehicle role set when the vehicle role sequence contains the vehicle entity role; when the vehicle role sequence does not contain the vehicle entity roles, the original vehicle roles are added to the vehicle role set, only a small number of vehicle roles are needed to be counted to generate the vehicle role set, new vehicle roles are continuously added to the vehicle role set, the expansion of the vehicle role set is realized, the high-quality vehicle role set is obtained, and the labor cost is greatly reduced.
In one embodiment, a vehicle entity role 'BMW' and vehicle composition element roles 'Bao' and 'Ma' of a vehicle entity 'BMW' in a standard vehicle type library are extracted and added to a vehicle role set, the vehicle entity role 'BMW' is coded as 'C', the vehicle composition element roles 'Bao' and 'Ma' are both coded as 'D', an original vehicle text input by a user is 'consulted BMW price', the word segmentation processing is carried out on the 'consulting the BMW price' to obtain 'consulting/BMW/price', extracting roles of consultation/BMW/price according to the vehicle role set, mapping a role extraction result into a vehicle role sequence, wherein the vehicle role sequence is ACA, and the vehicle role sequence ACA comprises a vehicle entity role C, extracting the vehicle entity role C of the vehicle role sequence ACA, and mapping a vehicle entity role 'C' of the vehicle role sequence 'ACA' into a vehicle entity 'BMW'; adding the above roles 'consultation' and the below roles 'price' of the vehicle entity to the vehicle role set, and counting the use frequency of the above roles of the vehicle entity and the below roles of the vehicle entity in the vehicle role set, wherein the use frequency of the roles is used for filtering the above roles of the vehicle entity and the below roles of the vehicle entity which are not commonly used.
In one embodiment, an original vehicle text input by a user is 'consider holding 30 thousands of horses', the 'consider holding 30 thousands' is participled into 'consider/hold/horse/30/ten thousand', the 'consider/hold/horse/30/ten thousand' is subjected to role extraction according to a vehicle role set, a role extraction result is mapped into a vehicle role sequence, the vehicle role sequence is 'KADAA', the vehicle role sequence does not contain vehicle entity roles, maximum pattern matching is carried out on the sequence 'KADAA', specifically, the content of an existing entity word dictionary is constructed into a vehicle dictionary tree by utilizing a trie, the sequence 'KADAA' is matched with the vehicle dictionary tree to obtain a vehicle entity character string 'KAD', the vehicle entity character string 'KAD' is mapped into a vehicle entity text 'consider holding horses', the vehicle entity text 'consider holding horses' and carry out similarity calculation with vehicle entities in a standard vehicle type library, the vehicle entity with the similarity larger than the threshold value and the first ranking is output, the result is that the similarity of a vehicle entity text 'consider holding horse' and a vehicle entity 'BMW' of a standard vehicle type library is highest, the vehicle entity recognition result is 'BMW', a new vehicle composition element role 'holding' is added to a vehicle role set, if a new original vehicle text exists, if a user mistakenly outputs 'BMW' into 'holding', the system can automatically recognize that the vehicle entity is 'BMW', the workload of manual mapping is greatly reduced due to the update of the vehicle role set, the expansibility is good, and the compatibility is good.
An electronic device, comprising: a processor; a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing a vehicle entity identification method; a computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor to perform a vehicle entity identification method as described above.
A vehicle entity identification system is shown in figure 2 and comprises a vehicle role extraction module and a vehicle entity identification module, wherein the vehicle role extraction module performs vehicle role extraction on an original vehicle text to obtain a plurality of original vehicle roles, and the original vehicle roles are mapped into a vehicle role sequence; the vehicle entity recognition module obtains a vehicle entity character string of the vehicle role sequence, compares the vehicle entity character string with vehicle entities in a standard vehicle type library, and obtains a vehicle entity with the highest similarity.
In an embodiment, as shown in fig. 2-3, it is preferable that the vehicle role collection system further includes a vehicle role collection generation module, and the vehicle role collection generation module extracts vehicle roles in the standard vehicle type library and the vehicle corpus to generate a vehicle role collection; the vehicle entity identification module further comprises a vehicle role set updating module, and the vehicle role set updating module adds the original vehicle role to the vehicle role set.
In an embodiment, preferably, the vehicle role set generating module extracts vehicle entity roles, vehicle component element roles, upper roles of the vehicle entity, and lower roles of the vehicle entity from the standard vehicle type library and the vehicle corpus, and generates the vehicle role set according to the vehicle entity roles, the vehicle component element roles, the upper roles of the vehicle entity, and the lower roles of the vehicle entity.
In an embodiment, as shown in fig. 2 to fig. 3, preferably, the vehicle role extraction module further includes a word segmentation module, the word segmentation module performs word segmentation on the original vehicle text, and the vehicle role extraction module performs extraction of a vehicle entity role, a vehicle component element role, an upper role of the vehicle entity, and a lower role of the vehicle entity on a word segmentation processing result according to the vehicle role set, and maps a role extraction result to a vehicle role sequence.
In an embodiment, as shown in fig. 2 to fig. 3, preferably, the vehicle role extraction module further includes a vehicle entity character string extraction module and a similarity comparison module, the vehicle entity character string extraction module matches the vehicle role sequence with the vehicle dictionary tree to obtain a vehicle entity character string of the vehicle role sequence, the similarity comparison module maps the vehicle entity character string into a vehicle entity text, and compares the vehicle entity text with vehicle entities in the standard vehicle type library to obtain a vehicle entity with the highest similarity.
In one embodiment, when the vehicle character sequence includes a vehicle entity character, the character set update module adds an above character of the vehicle entity and a below character of the vehicle entity to the vehicle character set; when the vehicle character sequence does not contain a vehicle entity character, the character set update module adds the original vehicle character to the vehicle character set.
According to the method, the vehicle role set is generated by extracting the vehicle roles of the standard vehicle type library and the vehicle corpus, the vehicle roles are extracted from the original vehicle text according to the vehicle role set to obtain the original vehicle roles, the original vehicle roles are mapped into the vehicle role sequence, the vehicle entity character strings of the vehicle role sequence are obtained, the vehicle entity character strings are compared with the vehicle entities of the standard vehicle type library to obtain the vehicle entities with the highest similarity, and the original vehicle roles are added to the vehicle role set, so that the workload of manual mapping is greatly reduced, a large number of rules do not need to be manually formulated, and the method is good in expansibility, high in accuracy and good in compatibility.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can readily practice the invention as shown and described in the drawings and detailed description herein; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (8)
1. A vehicle entity identification method, characterized by comprising the steps of:
generating a role set, extracting vehicle entity roles, vehicle component element roles, upper roles of vehicle entities and lower roles of vehicle entities of a standard vehicle type library and a vehicle corpus, and generating the vehicle role set according to the vehicle entity roles, the vehicle component element roles, the upper roles of the vehicle entities and the lower roles of the vehicle entities;
extracting roles, namely extracting vehicle roles from an original vehicle text according to the vehicle role set to obtain a plurality of original vehicle roles, and mapping the original vehicle roles into a vehicle role sequence;
vehicle entity recognition, namely acquiring a vehicle entity character string of the vehicle role sequence under the condition that the vehicle role sequence does not contain the vehicle entity role, comparing the vehicle entity character string with vehicle entities in a standard vehicle type library, and acquiring a vehicle entity with the highest similarity;
updating a set of roles, in the event that the sequence of vehicle roles includes a vehicle entity role, adding an above role for the vehicle entity and an below role for the vehicle entity to the set of vehicle roles; in the event that the sequence of vehicle roles does not include the vehicle entity role, adding the original vehicle role to the set of vehicle roles.
2. The vehicle entity identification method of claim 1, wherein the role extraction comprises:
performing word segmentation processing on the original vehicle text, performing vehicle entity role, vehicle component element role, vehicle entity upper role and vehicle entity lower role extraction on word segmentation processing results according to the vehicle role set, and mapping role extraction results into the vehicle role sequence.
3. The vehicle entity identification method of claim 2, wherein the vehicle entity identification comprises: judging whether the vehicle role sequence contains a vehicle entity role, if so, extracting the vehicle entity role of the vehicle role sequence, and mapping the vehicle entity role of the vehicle role sequence into a vehicle entity; otherwise, matching the vehicle role sequence with a vehicle dictionary tree to obtain a vehicle entity character string of the vehicle role sequence, mapping the vehicle entity character string into a vehicle entity text, and comparing the vehicle entity text with the vehicle entities in the standard vehicle type library to obtain the vehicle entity with the highest similarity.
4. An electronic device, characterized by comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for carrying out the method according to any one of claims 1-3.
5. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor for performing the method according to any of claims 1-3.
6. A vehicle entity identification system, characterized by: the vehicle role extraction module extracts vehicle entity roles, vehicle component element roles, upper roles of vehicle entities and lower roles of the vehicle entities from a standard vehicle type library and a vehicle corpus, generates a vehicle role set according to the vehicle entity roles, the vehicle component element roles, the upper roles of the vehicle entities and the lower roles of the vehicle entities, extracts the vehicle roles from an original vehicle text according to the vehicle role set to obtain a plurality of original vehicle roles, and maps the original vehicle roles into a vehicle role sequence; the vehicle entity identification module acquires a vehicle entity character string of the vehicle role sequence under the condition that the vehicle role sequence does not contain the vehicle entity role, compares the vehicle entity character string with vehicle entities in a standard vehicle type library and acquires a vehicle entity with the highest similarity; the vehicle entity identification module further comprises a vehicle role set update module that adds an above role of the vehicle entity and an below role of the vehicle entity to the vehicle role set if the vehicle role sequence includes a vehicle entity role; in the event that the sequence of vehicle roles does not include the vehicle entity role, adding the original vehicle role to the set of vehicle roles.
7. A vehicle entity identification system as claimed in claim 6, wherein: the vehicle role extraction module also comprises a word segmentation module, the word segmentation module carries out word segmentation on the original vehicle text, and the vehicle role extraction module carries out vehicle entity role, vehicle component element role, vehicle entity upper role and vehicle entity lower role extraction on word segmentation processing results according to the vehicle role set and maps the role extraction results into the vehicle role sequence.
8. A vehicle entity identification system as claimed in claim 7, wherein: the vehicle role extraction module further comprises a vehicle entity character string extraction module and a similarity comparison module, the vehicle entity character string extraction module matches the vehicle role sequence with a vehicle dictionary tree to obtain a vehicle entity character string of the vehicle role sequence, the similarity comparison module maps the vehicle entity character string into a vehicle entity text, and the vehicle entity text is compared with vehicle entities in the standard vehicle type library to obtain a vehicle entity with the highest similarity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711185953.XA CN108170708B (en) | 2017-11-23 | 2017-11-23 | Vehicle entity identification method, electronic equipment, storage medium and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711185953.XA CN108170708B (en) | 2017-11-23 | 2017-11-23 | Vehicle entity identification method, electronic equipment, storage medium and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108170708A CN108170708A (en) | 2018-06-15 |
| CN108170708B true CN108170708B (en) | 2021-03-30 |
Family
ID=62527603
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711185953.XA Active CN108170708B (en) | 2017-11-23 | 2017-11-23 | Vehicle entity identification method, electronic equipment, storage medium and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108170708B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109389064B (en) * | 2018-09-27 | 2021-02-23 | 东软睿驰汽车技术（沈阳）有限公司 | Vehicle feature acquisition method and device |
| CN111612015B (en) * | 2020-05-26 | 2023-10-31 | 创新奇智(西安)科技有限公司 | Vehicle identification method and device and electronic equipment |
| CN111930775A (en) * | 2020-08-26 | 2020-11-13 | 明觉科技（北京）有限公司 | Vehicle information identification method, device, terminal and computer readable storage medium |
| CN113886385A (en) * | 2021-09-18 | 2022-01-04 | 中国银行保险信息技术管理有限公司 | New energy automobile insurance identification method and device based on rule engine |
| CN115759097B (en) * | 2022-11-08 | 2023-07-21 | 广东数鼎科技有限公司 | Vehicle model name recognition method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102033879A (en) * | 2009-09-27 | 2011-04-27 | 腾讯科技（深圳）有限公司 | Method and device for identifying Chinese name |
| CN103268339A (en) * | 2013-05-17 | 2013-08-28 | 中国科学院计算技术研究所 | Recognition method and system of named entities in microblog messages |
-
2017
- 2017-11-23 CN CN201711185953.XA patent/CN108170708B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102033879A (en) * | 2009-09-27 | 2011-04-27 | 腾讯科技（深圳）有限公司 | Method and device for identifying Chinese name |
| CN103268339A (en) * | 2013-05-17 | 2013-08-28 | 中国科学院计算技术研究所 | Recognition method and system of named entities in microblog messages |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108170708A (en) | 2018-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108170708B (en) | Vehicle entity identification method, electronic equipment, storage medium and system | |
| CN107291783B (en) | Semantic matching method and intelligent equipment | |
| CN108287858B (en) | Semantic extraction method and device for natural language | |
| CN109522557B (en) | Training method and device of text relation extraction model and readable storage medium | |
| CN109933785B (en) | Method, apparatus, device and medium for entity association | |
| CN109388795B (en) | Named entity recognition method, language recognition method and system | |
| WO2020232861A1 (en) | Named entity recognition method, electronic device and storage medium | |
| CN110781276A (en) | Text extraction method, device, equipment and storage medium | |
| CN110909548A (en) | Chinese named entity recognition method and device and computer readable storage medium | |
| CN109446521B (en) | Named entity recognition method, named entity recognition device, electronic equipment and machine-readable storage medium | |
| CN109284374B (en) | Method, apparatus, device and computer readable storage medium for determining entity class | |
| CN114036930A (en) | Text error correction method, device, equipment and computer readable medium | |
| CN109522740B (en) | Health data privacy removal processing method and system | |
| CN108399157B (en) | Dynamic extraction method of entity and attribute relationship, server and readable storage medium | |
| CN111651986A (en) | Event keyword extraction method, device, equipment and medium | |
| CN113468339B (en) | Label extraction method and system based on knowledge graph, electronic equipment and medium | |
| US11520835B2 (en) | Learning system, learning method, and program | |
| CN109522396B (en) | Knowledge processing method and system for national defense science and technology field | |
| CN111104801A (en) | Text word segmentation method, system, device and medium based on website domain name | |
| CN110750984A (en) | Command line character string processing method, terminal, device and readable storage medium | |
| CN113961768A (en) | Sensitive word detection method and device, computer equipment and storage medium | |
| CN110705285A (en) | Government affair text subject word bank construction method, device, server and readable storage medium | |
| CN110956043A (en) | Domain professional vocabulary word embedding vector training method, system and medium based on alias standardization | |
| CN111144345A (en) | Character recognition method, device, equipment and storage medium | |
| CN112071304B (en) | Semantic analysis method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 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 |