CN109446522B - Automatic test question classification system and method - Google Patents
Automatic test question classification system and method Download PDFInfo
- Publication number
- CN109446522B CN109446522B CN201811230070.0A CN201811230070A CN109446522B CN 109446522 B CN109446522 B CN 109446522B CN 201811230070 A CN201811230070 A CN 201811230070A CN 109446522 B CN109446522 B CN 109446522B
- Authority
- CN
- China
- Prior art keywords
- test
- test question
- questions
- classified
- module
- 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
- 238000012360 testing method Methods 0.000 title claims abstract description 210
- 238000000034 method Methods 0.000 title claims description 20
- 238000012937 correction Methods 0.000 claims abstract description 11
- 230000011218 segmentation Effects 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 4
- 238000013139 quantization Methods 0.000 description 4
- 230000010365 information processing Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 238000012550 audit Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Economics (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Educational Administration (AREA)
- Computational Linguistics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Educational Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to the technical field of network education, in particular to an automatic test question classification system, which comprises a test question database to be classified, an automatic classification module, a label library, a test question database to be checked, a manual checking module, a manual classification module, an automatic classification correction module, a classified test question database and a similar test question search module, wherein when the test questions are automatically classified, a label characteristic word set with high accuracy is replaced by a label characteristic word set with low accuracy, so that the classification accuracy is continuously improved; extracting test question feature words in test questions and label feature words in labels, and finding out corresponding labels by using a K nearest neighbor algorithm, so that the speed of classifying the test questions is greatly improved; a perfect manual auditing mechanism is established, and the accuracy of classifying the question bank is ensured.
Description
Technical Field
The invention relates to the technical field of network education, in particular to an automatic test question classification system and method.
Background
The continuous development of computer technology and network technology makes some paperless and random question practice and examination modes increasingly applied to various examination systems, and the realization of the application is not separated from an electronic question library. In the electronic test question bank, the test questions are manually input and can be used for a long time after one time input, however, with the continuous and large quantity supplement of the test questions, the problem of test question bank management is quite prominent, namely, the conventional manual classification and detection cannot be used for the difficult work. Therefore, how to effectively classify the questions is a problem to be solved in the test question bank Guan Lizhong.
The test question library management has very important significance and value, and the good test question library management system can provide effective and rich test question data for an examination system and also can provide a new teaching platform for teaching and learning activities of teachers and students. However, the sharing of information resources and the explosive growth of the amount of information data brought by the network make the traditional manual information processing method impractical, so that an information processing method with higher automation degree and better efficiency is needed to help people to perform test question classification more efficiently.
At present, the management of the test question library is classified manually, or the classification is known before the test questions are put in storage, and the test questions of the corresponding classification are imported into the database. At present, a machine learning and deep learning method is also utilized to realize text classification, but the accuracy rate meets the bottleneck.
Disclosure of Invention
The invention aims at overcoming the defects in the prior art and provides an automatic test question classification system and method.
The aim of the invention is achieved by the following technical scheme: the automatic test question classification system comprises a test question database to be classified, an automatic classification module, a tag library, a test question database to be checked, a manual checking module, a manual classification module, an automatic classification correction module, a classified test question database and a similar question searching module;
the automatic classification module is used for matching the test questions in the test question database to be classified with the labels of the label library and transmitting the matched test questions to the test question database to be checked;
the manual checking module is used for checking the correctness of automatic classification of the test questions, transmitting the test questions to the classified test question database if the automatic classification is correct, and transmitting the test questions to the manual classification module if the automatic classification is incorrect;
the manual classification module is used for matching the test questions with the labels of the label library, if the labels matched with the test questions exist in the label library, directly matching the test questions with the labels, if the labels matched with the test questions do not exist in the label library, creating new labels matched with the test questions, adding the new labels into the label library, and transmitting the matched test questions to the classified test question database;
the automatic classification correction module is used for improving the correctness of the automatic classification module;
the similar question searching module is used for further classifying the questions of the classified question database.
A classification method comprising the steps of:
step A: the automatic classification module matches the test questions in the test question database to be classified with the labels of the label library, and transmits the matched test questions to the test question database to be checked;
and (B) step (B): the manual examination module examines the automatic classification test questions of the test question database to be examined, if the automatic classification is correct, the test questions are transmitted to the classified test question database, and if the automatic classification is incorrect, the test questions are transmitted to the manual classification module;
step C: the manual classification module matches the test questions with the labels of the label library, if the labels matched with the test questions exist in the label library, the test questions are directly matched with the labels, if the labels matched with the test questions do not exist in the label library, new labels are created to be matched with the test questions, the new labels are added into the label library, and the matched test questions are transmitted to the classified test question database;
step D: the automatic classification correction module feeds the accuracy of the automatic classification and the accuracy of the manual classification back to the automatic classification module to improve the accuracy of the automatic classification module; .
Step E: the similar question searching module further classifies the questions of the classified question database.
The invention further provides that the step A comprises the following steps:
a1: extracting test question feature words from the test questions to be classified in the test question database to be classified;
a2: storing the test question feature words into a test question feature word set;
a3: extracting tag characteristic words from the existing tags in the tag library;
a4: storing the tag feature words into a tag feature word set;
a5: the test question feature word set and the label feature word set pass through a K nearest neighbor algorithm model;
a6: and matching the most similar labels with the test questions to be classified.
The invention further provides that the step A1 of extracting the test question feature words comprises the following steps:
a1: preprocessing the test questions to be classified of the test question database to be classified;
a2: one test question is put forward from the pretreated test questions;
a3: word segmentation is carried out on the test questions;
a4: obtaining a plurality of candidate words after word segmentation;
a5: calculating the weight of each candidate word;
a6: and obtaining the test question feature words of the test questions to be classified.
The invention further provides that the step D comprises the following steps:
b1: a new tag library is obtained through a manual classification module, and a new tag characteristic word set is manually extracted from the new tag library;
b1: counting the number x of correct matching times of the test question feature word set and the automatically extracted tag feature word set;
b2: counting the number y of correct matching of the test question feature word set and the manually extracted tag feature word set;
b3: if y is larger than x, replacing the automatically extracted tag feature word set with the manually extracted tag feature word set when the automatic classification module automatically classifies the tag feature word set next time; if y is smaller than x, the label feature word set extracted automatically last time is continuously used when the automatic classification module automatically classifies next time.
The invention further provides that the step E comprises the following steps:
c1: extracting a test question feature word set of each test question;
c2: sorting out the similarity of the test question feature word set;
and C3: finding out similar topics.
The invention is further arranged that one test question can be matched with a plurality of labels.
The invention is further arranged that the candidate words include nouns, formulas, symbols, and graphics.
The invention has the beneficial effects that: 1. when the test questions are automatically classified, the high-accuracy tag feature word set is used for replacing the low-accuracy tag feature word set, so that the classification accuracy is improved continuously; 2. extracting test question feature words in test questions and label feature words in labels, and finding out corresponding labels by using a K nearest neighbor algorithm, so that the speed of classifying the test questions is greatly improved; 3. a perfect manual auditing mechanism is established, and the accuracy of classifying the question bank is ensured.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of the automatic classification module of the present invention;
FIG. 3 is a flow chart of the extraction of test question feature words of the present invention;
FIG. 4 is a flow chart of a manual audit module of the present invention;
FIG. 5 is a flow chart of the manual classification module of the present invention;
FIG. 6 is a flow chart of the automatic classification correction module of the present invention;
FIG. 7 is a flow chart of the homogeneous title search module of the present invention.
Detailed Description
The invention will be further described with reference to the following examples.
As can be seen from fig. 1 to 7; the automatic test question classification system comprises a test question database to be classified, an automatic classification module, a tag library, a test question database to be checked, a manual checking module, a manual classification module, an automatic classification correction module, a classified test question database and a similar question searching module;
the automatic classification module is used for matching the test questions in the test question database to be classified with the labels of the label library and transmitting the matched test questions to the test question database to be checked;
the manual checking module is used for checking the correctness of automatic classification of the test questions, transmitting the test questions to the classified test question database if the automatic classification is correct, and transmitting the test questions to the manual classification module if the automatic classification is incorrect;
the manual classification module is used for matching the test questions with the labels of the label library, if the labels matched with the test questions exist in the label library, directly matching the test questions with the labels, if the labels matched with the test questions do not exist in the label library, creating new labels matched with the test questions, adding the new labels into the label library, and transmitting the matched test questions to the classified test question database;
the automatic classification correction module is used for improving the correctness of the automatic classification module;
the similar question searching module is used for further classifying the questions of the classified question database.
When the test questions are automatically classified, the high-accuracy tag feature word set is used for replacing the low-accuracy tag feature word set, so that the classification accuracy is improved continuously; extracting test question feature words in test questions and label feature words in labels, and finding out corresponding labels by using a K nearest neighbor algorithm, so that the speed of classifying the test questions is greatly improved; a perfect manual auditing mechanism is established, and the accuracy of classifying the question bank is ensured.
The automatic test question classification method of the embodiment comprises the following steps:
step A: the automatic classification module matches the test questions in the test question database to be classified with the labels of the label library, and transmits the matched test questions to the test question database to be checked;
and (B) step (B): the manual examination module examines the automatic classification test questions of the test question database to be examined, if the automatic classification is correct, the test questions are transmitted to the classified test question database, and if the automatic classification is incorrect, the test questions are transmitted to the manual classification module;
step C: the manual classification module matches the test questions with the labels of the label library, if the labels matched with the test questions exist in the label library, the test questions are directly matched with the labels, if the labels matched with the test questions do not exist in the label library, new labels are created to be matched with the test questions, the new labels are added into the label library, and the matched test questions are transmitted to the classified test question database;
step D: the automatic classification correction module feeds the accuracy of the automatic classification and the accuracy of the manual classification back to the automatic classification module to improve the accuracy of the automatic classification module; .
Step E: the similar question searching module further classifies the questions of the classified question database.
The automatic test question classification method of the embodiment, the step A includes the following steps:
a1: extracting test question feature words from the test questions to be classified in the test question database to be classified;
a2: storing the test question feature words into a test question feature word set;
a3: extracting tag characteristic words from the existing tags in the tag library;
a4: storing the tag feature words into a tag feature word set;
a5: the test question feature word set and the label feature word set pass through a K nearest neighbor algorithm model;
a6: and matching the most similar labels with the test questions to be classified.
The automatic classification method for test questions in the embodiment, wherein the step A1 of extracting test question feature words comprises the following steps:
a1: preprocessing the test questions to be classified of the test question database to be classified;
a2: one test question is put forward from the pretreated test questions;
a3: word segmentation is carried out on the test questions;
a4: obtaining a plurality of candidate words after word segmentation;
a5: calculating the weight of each candidate word;
a6: and obtaining the test question feature words of the test questions to be classified.
A test question is usually proposed aiming at knowledge in a certain field, the knowledge in the field has corresponding attributes or characteristic words, and the characteristic words of certain knowledge can highlight what attributes the test question belongs to.
An unsupervised text keyword extraction, i.e. feature statistics keyword extraction, is presented herein, and the idea of a statistical feature-based keyword extraction algorithm is to extract keywords of a document using statistics of words in the document. The text is usually preprocessed to obtain a set of candidate words, and then keywords are obtained from the candidate set in a characteristic value quantization mode. The keyword extraction method based on the statistical features utilizes feature quantization based on word weights, wherein the feature quantization based on the word weights mainly comprises parts of speech, word frequency, word position and the like. For test question classification, feature quantization based on word weight mainly aims at parts of speech, nouns and formulas, and the invention increases the weight of nouns and reduces the weight of adjectives, verbs and the like.
The automatic test question classification method of the embodiment, the step D includes the following steps:
b1: a new tag library is obtained through a manual classification module, and a new tag characteristic word set is manually extracted from the new tag library;
b1: counting the number x of correct matching times of the test question feature word set and the automatically extracted tag feature word set;
b2: counting the number y of correct matching of the test question feature word set and the manually extracted tag feature word set;
b3: if y is larger than x, replacing the automatically extracted tag feature word set with the manually extracted tag feature word set when the automatic classification module automatically classifies the tag feature word set next time; if y is smaller than x, the label feature word set extracted automatically last time is continuously used when the automatic classification module automatically classifies next time.
The automatic test question classification method of the embodiment, the step E includes the following steps:
c1: extracting a test question feature word set of each test question;
c2: sorting out the similarity of the test question feature word set;
and C3: finding out similar topics.
According to the test question automatic classification method, one test question can be matched with a plurality of labels.
According to the test question automatic classification method, the candidate words comprise nouns, formulas, symbols and figures.
When the test questions are automatically classified, the high-accuracy tag feature word set is used for replacing the low-accuracy tag feature word set, so that the classification accuracy is improved continuously; extracting test question feature words in test questions and label feature words in labels, and finding out corresponding labels by using a K nearest neighbor algorithm, so that the speed of classifying the test questions is greatly improved; a perfect manual auditing mechanism is established, and the accuracy of classifying the question bank is ensured.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. An automatic test question classification system is characterized in that: the system comprises a test question database to be classified, an automatic classification module, a tag library, a test question database to be checked, a manual checking module, a manual classification module, an automatic classification correction module, a classified test question database and a similar question searching module;
the automatic classification module adopts a K nearest neighbor algorithm model to automatically classify, and is used for matching the test questions in the test question database to be classified with the labels of the label library and transmitting the matched test questions to the test question database to be checked;
the manual checking module is used for checking the correctness of automatic classification of the test questions, transmitting the test questions to the classified test question database if the automatic classification is correct, and transmitting the test questions to the manual classification module if the automatic classification is incorrect;
the manual classification module is used for matching the test questions with the labels of the label library, if the labels matched with the test questions exist in the label library, directly matching the test questions with the labels, if the labels matched with the test questions do not exist in the label library, creating new labels matched with the test questions, adding the new labels into the label library, and transmitting the matched test questions to the classified test question database;
the automatic classification correction module is used for improving the correctness of the automatic classification module;
the similar question searching module is used for further classifying the questions of the classified question database.
2. A classification method using the test question automatic classification system according to claim 1, characterized in that: the method comprises the following steps:
step A: the automatic classification module matches the test questions in the test question database to be classified with the labels of the label library, and transmits the matched test questions to the test question database to be checked;
and (B) step (B): the manual examination module examines the automatic classification test questions of the test question database to be examined, if the automatic classification is correct, the test questions are transmitted to the classified test question database, and if the automatic classification is incorrect, the test questions are transmitted to the manual classification module;
step C: the manual classification module matches the test questions with the labels of the label library, if the labels matched with the test questions exist in the label library, the test questions are directly matched with the labels, if the labels matched with the test questions do not exist in the label library, new labels are created to be matched with the test questions, the new labels are added into the label library, and the matched test questions are transmitted to the classified test question database;
step D: the automatic classification correction module feeds the accuracy of the automatic classification and the accuracy of the manual classification back to the automatic classification module to improve the accuracy of the automatic classification module;
step E: the similar question searching module further classifies the questions of the classified question database.
3. A classification method according to claim 2, characterized in that: the step A comprises the following steps:
a1: extracting test question feature words from the test questions to be classified in the test question database to be classified;
a2: storing the test question feature words into a test question feature word set;
a3: extracting tag characteristic words from the existing tags in the tag library;
a4: storing the tag feature words into a tag feature word set;
a5: the test question feature word set and the label feature word set pass through a K nearest neighbor algorithm model;
a6: and matching the most similar labels with the test questions to be classified.
4. A classification method according to claim 3, characterized in that: the step A1 of extracting the test question feature words comprises the following steps:
a1: preprocessing the test questions to be classified of the test question database to be classified;
a2: one test question is put forward from the pretreated test questions;
a3: word segmentation is carried out on the test questions;
a4: obtaining a plurality of candidate words after word segmentation;
a5: calculating the weight of each candidate word;
a6: and obtaining the test question feature words of the test questions to be classified.
5. A classification method according to claim 3, characterized in that: the step D comprises the following steps:
b1: a new tag library is obtained through a manual classification module, and a new tag characteristic word set is manually extracted from the new tag library;
b1: counting the number x of correct matching times of the test question feature word set and the automatically extracted tag feature word set;
b2: counting the number y of correct matching of the test question feature word set and the manually extracted tag feature word set;
b3: if y is larger than x, replacing the automatically extracted tag feature word set with the manually extracted tag feature word set when the automatic classification module automatically classifies the tag feature word set next time; if y is smaller than x, the label feature word set extracted automatically last time is continuously used when the automatic classification module automatically classifies next time.
6. A classification method according to claim 2, characterized in that: the step E comprises the following steps:
c1: extracting a test question feature word set of each test question;
c2: sorting out the similarity of the test question feature word set;
and C3: finding out similar topics.
7. A classification method according to claim 2, characterized in that: one test question may be matched with a plurality of tags.
8. A method of sorting as in claim 4, wherein: the candidate words include nouns, formulas, symbols, and graphics.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811230070.0A CN109446522B (en) | 2018-10-22 | 2018-10-22 | Automatic test question classification system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811230070.0A CN109446522B (en) | 2018-10-22 | 2018-10-22 | Automatic test question classification system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109446522A CN109446522A (en) | 2019-03-08 |
| CN109446522B true CN109446522B (en) | 2023-09-19 |
Family
ID=65546879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811230070.0A Active CN109446522B (en) | 2018-10-22 | 2018-10-22 | Automatic test question classification system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109446522B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111125443A (en) * | 2019-11-14 | 2020-05-08 | 临沂市拓普网络股份有限公司 | On-line updating method of test question bank based on automatic duplicate removal |
| CN111125081B (en) * | 2019-12-25 | 2024-03-26 | 北京爱论答科技有限公司 | Question bank establishment method and system |
| CN113450066A (en) * | 2021-05-28 | 2021-09-28 | 作业帮教育科技（北京）有限公司 | Method, device and storage medium for automatically passing examination and verification of test questions |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101819601A (en) * | 2010-05-11 | 2010-09-01 | 同方知网(北京)技术有限公司 | Method for automatically classifying academic documents |
| CN102033873A (en) * | 2009-09-29 | 2011-04-27 | 中国移动通信集团福建有限公司 | Method and equipment for establishing text organizational framework |
| WO2015177861A1 (en) * | 2014-05-20 | 2015-11-26 | 株式会社日立製作所 | Device and method for generating training data |
| CN107491536A (en) * | 2017-08-22 | 2017-12-19 | 广东小天才科技有限公司 | A kind of examination question method of calibration, examination question calibration equipment and electronic equipment |
| CN107943865A (en) * | 2017-11-10 | 2018-04-20 | 阿基米德（上海）传媒有限公司 | It is a kind of to be suitable for more scenes, the audio classification labels method and system of polymorphic type |
| CN108256090A (en) * | 2018-01-25 | 2018-07-06 | 成都贝发信息技术有限公司 | APP divides class method for distinguishing automatically based on keyword |
| CN108376151A (en) * | 2018-01-31 | 2018-08-07 | 深圳市阿西莫夫科技有限公司 | Question classification method, device, computer equipment and storage medium |
| CN108520041A (en) * | 2018-04-03 | 2018-09-11 | 有米科技股份有限公司 | Trade classification method, system, computer equipment and the storage medium of text |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110255788A1 (en) * | 2010-01-15 | 2011-10-20 | Copanion, Inc. | Systems and methods for automatically extracting data from electronic documents using external data |
-
2018
- 2018-10-22 CN CN201811230070.0A patent/CN109446522B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102033873A (en) * | 2009-09-29 | 2011-04-27 | 中国移动通信集团福建有限公司 | Method and equipment for establishing text organizational framework |
| CN101819601A (en) * | 2010-05-11 | 2010-09-01 | 同方知网(北京)技术有限公司 | Method for automatically classifying academic documents |
| WO2015177861A1 (en) * | 2014-05-20 | 2015-11-26 | 株式会社日立製作所 | Device and method for generating training data |
| CN107491536A (en) * | 2017-08-22 | 2017-12-19 | 广东小天才科技有限公司 | A kind of examination question method of calibration, examination question calibration equipment and electronic equipment |
| CN107943865A (en) * | 2017-11-10 | 2018-04-20 | 阿基米德（上海）传媒有限公司 | It is a kind of to be suitable for more scenes, the audio classification labels method and system of polymorphic type |
| CN108256090A (en) * | 2018-01-25 | 2018-07-06 | 成都贝发信息技术有限公司 | APP divides class method for distinguishing automatically based on keyword |
| CN108376151A (en) * | 2018-01-31 | 2018-08-07 | 深圳市阿西莫夫科技有限公司 | Question classification method, device, computer equipment and storage medium |
| CN108520041A (en) * | 2018-04-03 | 2018-09-11 | 有米科技股份有限公司 | Trade classification method, system, computer equipment and the storage medium of text |
Non-Patent Citations (1)
| Title |
|---|
| 电信大数据文本挖掘算法及应用;汪东升;计算机科学;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109446522A (en) | 2019-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11586637B2 (en) | Search result processing method and apparatus, and storage medium | |
| CN110442760B (en) | Synonym mining method and device for question-answer retrieval system | |
| CN107766371B (en) | Text information classification method and device | |
| US11113323B2 (en) | Answer selection using a compare-aggregate model with language model and condensed similarity information from latent clustering | |
| CN112819023B (en) | Sample set acquisition method, device, computer equipment and storage medium | |
| CN104881458B (en) | A kind of mask method and device of Web page subject | |
| CN110363194A (en) | Intelligently reading method, apparatus, equipment and storage medium based on NLP | |
| CN103299324A (en) | Learning tags for video annotation using latent subtags | |
| CN109446522B (en) | Automatic test question classification system and method | |
| CN111191051B (en) | Method and system for constructing emergency knowledge map based on Chinese word segmentation technology | |
| CN112069312B (en) | Text classification method based on entity recognition and electronic device | |
| CN113051914A (en) | Enterprise hidden label extraction method and device based on multi-feature dynamic portrait | |
| CN108038099B (en) | Low-frequency keyword identification method based on word clustering | |
| CN113434688B (en) | Data processing method and device for public opinion classification model training | |
| CN111782793A (en) | Intelligent customer service processing method, system and equipment | |
| CN111930792A (en) | Data resource labeling method and device, storage medium and electronic equipment | |
| CN111078835A (en) | Resume evaluation method and device, computer equipment and storage medium | |
| CN111930933A (en) | Detection case processing method and device based on artificial intelligence | |
| CN108595411B (en) | Method for acquiring multiple text abstracts in same subject text set | |
| CN113268615A (en) | Resource label generation method and device, electronic equipment and storage medium | |
| CN110705272A (en) | Named entity identification method for automobile engine fault diagnosis | |
| CN111754208A (en) | Automatic screening method for recruitment resumes | |
| CN111199151A (en) | Data processing method and data processing device | |
| CN116341521B (en) | AIGC article identification system based on text features | |
| CN113240562A (en) | Method and system for recommending and matching obstetrical and academic research projects based on nlp |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230818 Address after: 100000 806, floor 8, No. 6, Haidian middle street, Haidian District, Beijing Applicant after: Beijing layout Future Education Technology Co.,Ltd. Address before: 523000 Room 308, building 3, No. 6, Gongye South Road, Songshanhu high tech Industrial Development Zone, Dongguan City, Guangdong Province Applicant before: DONGGUAN QIBAOSHU EDUCATION TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 100000 806, floor 8, No. 6, Haidian middle street, Haidian District, Beijing Patentee after: Beijing Layout Future Technology Development Co.,Ltd. Address before: 100000 806, floor 8, No. 6, Haidian middle street, Haidian District, Beijing Patentee before: Beijing layout Future Education Technology Co.,Ltd. |
|
| CP01 | Change in the name or title of a patent holder |