CN110807317A - Application of learning software system based on vocabulary statistics - Google Patents
Application of learning software system based on vocabulary statistics Download PDFInfo
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- CN110807317A CN110807317A CN201911271734.2A CN201911271734A CN110807317A CN 110807317 A CN110807317 A CN 110807317A CN 201911271734 A CN201911271734 A CN 201911271734A CN 110807317 A CN110807317 A CN 110807317A
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Abstract
The application is used for word statistics of foreign language data documents, relates to application of a learning software system, and solves the problems that the existing learning software lacks a word statistics function, so that students can not clearly know self vocabulary, blindly select learning data and can not effectively check self vocabulary learning effect. The method comprises the following steps: s1: the system marks the vocabulary statistical information of the data document in the data document; s2: establishing a corresponding personal database and a personal vocabulary library aiming at a user, adding the data document into the personal database for learning by the user, and reflecting the learning result in the personal vocabulary library; s3: the personal database and the vocabulary statistics of the personal vocabulary library vary from person to person, and the vocabulary variation thereof has an influence on the vocabulary statistics of the data document. The method and the device associate vocabulary statistics with user learning, and enable numbers to serve learning.
Description
Technical Field
The application relates to a learning software system, in particular to a learning software system which is applied to vocabulary statistics of data documents, realizes a vocabulary statistics function which is difficult to realize by manpower and enables data to serve for learning.
Background
The robot cannot replace a human to learn, but can become a helper for learning. At present, many people learn foreign languages through machine equipment such as mobile phones and computers, but the machine equipment such as the mobile phones and the computers does not exert the maximum effect, and the fundamental reason is that software program innovation is insufficient, and the strong calculation function of the machine equipment cannot be embodied. The foreign language words comprise words and phrases with huge quantity, the Word memory is the key point of foreign language learning and is the basis of language learning, the efficiency of memorizing words and phrases is extremely low, more and more people tend to learn and memorize words through sentences, articles, novel speeches or other data documents, and due to the limitation of people, people are difficult to count how many words or phrases are in the data documents. It should be noted that, in the present application, the vocabulary or the vocabulary library refers to the total amount of non-repeated words and phrases, foreign language data documents, especially novel speeches and articles, contain a large amount of repeated words, and the real vocabulary of the data documents can be counted only by removing the repeated words, and numerous software including Word software has no practical statistical function which is helpful for learning. More, more specific and practical vocabulary statistical information is needed, and linkage is generated between vocabulary statistics and personal learning so as to achieve the purpose of serving the statistical data for learning.
Disclosure of Invention
Therefore, the application provides the application of the learning software system based on the vocabulary statistics, and solves the problems of three aspects of the user:
1) the problem that the user is not cognized enough for the self vocabulary learning condition and the vocabulary amount;
2) the problem that how to select the data documents in a targeted manner by a user can maximize the expansion of personal vocabulary;
3) the problem that the user cannot effectively check the self vocabulary learning effect.
In order to achieve the purpose, the application provides the following technical scheme: an application of a learning software system based on vocabulary statistics, comprising the steps of:
s1, the system marks the vocabulary statistical information of the data document in the data document;
s2, creating a corresponding personal database and a personal vocabulary library aiming at the user, wherein the user adds the data document into the personal database for learning, and the learning result is reflected in the personal vocabulary library;
s3, the personal database and the vocabulary statistics of the personal vocabulary database are different from person to person, and the change has influence on the vocabulary statistics of the data document.
Preferably, in step S1, the vocabulary statistics information is a group of statistics for helping the user to understand the vocabulary composition of the profile document, and includes: the number of non-repeated words of the data document, the number of non-repeated high-frequency words of the data document, the number of non-repeated rare words of the data document, the number of non-repeated classified words of the data document, the number of newly added non-repeated words of the personal database and the number of non-repeated rare words of the personal database.
Preferably, in step S2, the relationship between the personal database and the personal vocabulary database is: the number of the non-repeated words in the personal database is more than or equal to that of the non-repeated words in the personal database, and when the user finishes the learning of all the data documents in the personal database, the equal sign is established.
Preferably, the non-repeating vocabulary is: the system counts the words repeated many times in the document as only one.
Preferably, the high-frequency vocabulary, the uncommon vocabulary and the classified vocabulary are defined by a system preset word bank.
Preferably, in step S3, the vocabulary statistics of the personal database and the personal vocabulary database vary from person to person, and the variation has an influence on the vocabulary statistics of the data document: correspondingly, the statistics of the non-repeated words and the non-repeated strange words in the personal database are influenced, and the statistics are associated with the learning of the user and are dynamic:
A. the system counts the vocabulary number of the data document which is not in the personal database and is called as the number of newly added non-repeated vocabularies of the personal database, the larger the number of newly added non-repeated vocabularies of the personal database is, the more the data document can expand the vocabularies of the personal database, and the advantage of marking the number of the newly added non-repeated vocabularies of the personal database in the data document is as follows: the user selects the data document suitable for the user through the vocabulary statistical information in a targeted manner;
B. the personal vocabulary library of different users has difference, the system counts the vocabulary number of the data document which is not in the personal vocabulary library, which is called the number of the non-repeated strange vocabularies of the personal vocabulary library, the smaller the number of the non-repeated strange vocabularies of the personal vocabulary library represents that the strange vocabularies of the data document are less for the user, and the advantage of marking the number of the non-repeated strange vocabularies of the personal vocabulary library in the data document is as follows: the method is convenient for users to find data documents with few strange words for checking the word learning effect of the users: the user checks whether he has actually mastered the vocabulary in the personal vocabulary library by viewing the use of the same vocabulary in different documents.
Preferably, when the user checks the learning effect of the own vocabulary, the system lists the vocabulary which is not mastered by the user and deducts the vocabulary from the personal vocabulary library.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a schematic diagram of an application flow of a learning software system based on vocabulary statistics according to an embodiment of the present application;
FIG. 2 is a system diagram illustrating the tagging of a document according to an embodiment of the present application;
FIG. 3 is a schematic illustration of a personal learning interface according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating examination of vocabulary learning effect according to an embodiment of the present application.
Detailed Description
As shown in fig. 1, the present application provides an application of a learning software system based on vocabulary statistics, which constructs an efficient and personalized learning software system based on vocabulary statistics, and comprises the following steps:
s1, as shown in FIG. 2, the system labels the vocabulary statistics of the data document in the data document, the vocabulary statistics being a set of statistics that help the user to understand the vocabulary composition of the data document, including: the number of non-repeated words of the data document, the number of non-repeated high-frequency words of the data document, the number of non-repeated rare words of the data document, the number of non-repeated classified words of the data document, the number of newly added non-repeated words of a personal database, the number of non-repeated rare words of the personal database and the like are included, the non-repeated words refer to that the system only counts one repeated word in the data document, and the high-frequency words, the rare words and the classified words are defined by a preset word library of the system;
s2: as shown in fig. 3, the system creates a corresponding personal database and a personal vocabulary library for the user, allows the user to add the data documents into the personal database for learning, the learning result is reflected in the personal vocabulary library, and in the personal learning interface, the user can see which data documents are added into the personal database and how much personal vocabulary is, and the relationship between the personal database and the personal vocabulary library is: the number of the non-repeated words in the personal database is more than or equal to that of the non-repeated words in the personal database, and when the user finishes the learning of all the data documents in the personal database, the equal sign is established;
s3: in the vocabulary statistics, the number of newly added non-repeated vocabularies in the personal database and the number of non-repeated strange vocabularies in the personal database are dynamic and relevant to the learning of users, the statistics of the statistics are different from person to person, and when the vocabularies of the personal database and the personal vocabulary library change, the change affects the vocabulary statistics of the system on the data documents:
A. the system counts the vocabulary number of the data document which is not in the personal database and is called as the number of newly added non-repeated vocabularies of the personal database, the larger the number of newly added non-repeated vocabularies of the personal database is, the more the data document can expand the vocabularies of the personal database, and the advantage of marking the number of the newly added non-repeated vocabularies of the personal database in the data document is as follows: the user selects the data document suitable for the user through the vocabulary statistical information in a targeted manner;
B. the personal vocabulary library of different users has difference, the system counts the vocabulary number of the data document which is not in the personal vocabulary library, which is called the number of the non-repeated strange vocabularies of the personal vocabulary library, the smaller the number of the non-repeated strange vocabularies of the personal vocabulary library represents that the strange vocabularies of the data document are less for the user, and the advantage of marking the number of the non-repeated strange vocabularies of the personal vocabulary library in the data document is as follows: as shown in fig. 4: the method is convenient for users to find data documents with few strange words for checking the word learning effect of the users: the user checks whether he has actually mastered the words in the personal vocabulary library by viewing the use of the same words in different documents, the unsophisticated words being listed by the system and subtracted from the personal vocabulary library, and only relearning can add them to the personal vocabulary library.
The present application is not limited to the above-mentioned preferred embodiments, and any other products in various forms can be obtained by anyone in the light of the present application, but any changes in the shape or structure thereof, which have the same or similar technical solutions as the present application, fall within the protection scope of the present application.
Claims (8)
1. An application of a learning software system based on vocabulary statistics, characterized in that: the method comprises the following steps:
s1: the system marks the vocabulary statistical information of the data document in the data document;
s2: establishing a corresponding personal database and a personal vocabulary library aiming at a user, adding the data document into the personal database for learning by the user, and reflecting the learning result in the personal vocabulary library;
s3: the personal database and the vocabulary statistics of the personal vocabulary library vary from person to person, and variations thereof have an influence on the vocabulary statistics of the data document.
2. The use of a lexical statistics based learning software system according to claim 1, wherein: in step S1, the vocabulary statistics information is a group of statistics for helping the user to understand the vocabulary composition of the profile document, and includes: the number of non-repeated words of the data document, the number of non-repeated high-frequency words of the data document, the number of non-repeated rare words of the data document, the number of non-repeated classified words of the data document, the number of newly added non-repeated words of the personal database and the number of non-repeated rare words of the personal database.
3. The use of a lexical statistics based learning software system according to claim 1, wherein: in step S2, the relationship between the personal database and the personal vocabulary database is: the number of the non-repeated words in the personal database is more than or equal to that of the non-repeated words in the personal database, and when the user finishes the learning of all the data documents in the personal database, the equal sign is established.
4. The use of a lexical statistics based learning software system according to claim 2, wherein: the non-repeating vocabulary refers to: the system counts the words repeated many times in the document as only one.
5. The use of a lexical statistics based learning software system according to claim 2, wherein: the high-frequency vocabulary, the uncommon vocabulary and the classified vocabulary are defined by a system preset word bank.
6. The use of a lexical statistics based learning software system according to claim 1, wherein: in step S3, the vocabulary statistics of the data document change with the statistics of the personal database and the personal vocabulary database: the personal database of claim 2 wherein the statistics of the number of new non-repeating words and the number of non-repeating unknown words in the personal database are influenced by changes in the words in the personal database and the personal database, and the statistics are dynamically associated with the user learning:
A. the system counts the vocabulary number of the data document which is not in the personal database and is called as the number of newly added non-repeated vocabularies of the personal database, the larger the number of newly added non-repeated vocabularies of the personal database is, the more the data document can expand the vocabularies of the personal database, and the advantage of marking the number of the newly added non-repeated vocabularies of the personal database in the data document is as follows: the user selects the data document suitable for the user through the vocabulary statistical information in a targeted manner;
B. the personal vocabulary library of different users has difference, the system counts the vocabulary number of the data document which is not in the personal vocabulary library, which is called the number of the non-repeated strange vocabularies of the personal vocabulary library, the smaller the number of the non-repeated strange vocabularies of the personal vocabulary library represents that the strange vocabularies of the data document are less for the user, and the advantage of marking the number of the non-repeated strange vocabularies of the personal vocabulary library in the data document is as follows: the method is convenient for users to find data documents with few strange words for checking the word learning effect of the users: the user checks whether he has actually mastered the vocabulary in the personal vocabulary library by viewing the use of the same vocabulary in different documents.
7. The use of a lexical statistics based learning software system according to claim 6, wherein: when the user checks the learning effect of the self vocabulary, the system lists the vocabulary which is not mastered by the user and deducts the vocabulary from the personal vocabulary library.
8. Use of a lexical statistics based learning software system according to claims 1-7, characterized in that: the system associates vocabulary statistics with user learning outcomes:
1) the user knows the personal vocabulary learning condition and the personal vocabulary amount through the vocabulary statistical information;
2) the vocabulary statistical information is linked with the learning of the user, and the user selects the data document suitable for the user in a targeted manner through the vocabulary statistical information;
3) the user selects the data documents with few strange words through the word statistical information to be used for checking the mastering condition of the learned words, and the learning achievement is favorably consolidated.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2197712Y (en) * | 1994-04-06 | 1995-05-17 | 詹国茂 | Assembled word learner |
| CN1555020A (en) * | 2003-12-23 | 2004-12-15 | 无敌科技(西安)有限公司 | Divergent expanding type language study system and method |
| TW201007645A (en) * | 2008-08-14 | 2010-02-16 | Inventec Besta Co Ltd | Method and system for dynamically changing the downloaded learning phase material according to learning efficiency of user |
| WO2010150986A2 (en) * | 2009-06-24 | 2010-12-29 | Nam Chan-Hee | Apparatus and method for the lifelong study of words in a foreign language |
| JP2011128365A (en) * | 2009-12-17 | 2011-06-30 | Cocone Corp | Language learning device and language learning method |
| CN102194346A (en) * | 2011-06-21 | 2011-09-21 | 无敌科技(西安)有限公司 | Embedded system and method thereof for automatically analyzing vocabulary difficulty and annotating difficult words |
| CN106856066A (en) * | 2016-12-20 | 2017-06-16 | 杨燕 | Word splits memory learning system |
| CN108364512A (en) * | 2018-02-27 | 2018-08-03 | 清华大学 | A kind of English adaptive and learning system based on MOOC |
| CN108847076A (en) * | 2018-07-11 | 2018-11-20 | 北京美高森教育科技有限公司 | The assessment method of language learner |
| TW201903672A (en) * | 2017-06-01 | 2019-01-16 | 碩網資訊股份有限公司 | A deep learning based system and method for analyzing customer features thereof |
| CN109727495A (en) * | 2018-07-11 | 2019-05-07 | 北京美高森教育科技有限公司 | A kind of language teaching device |
| CN109817039A (en) * | 2019-03-18 | 2019-05-28 | 淮阴工学院 | Foreign language learning auxiliary device |
| TWM585415U (en) * | 2018-12-03 | 2019-10-21 | 陳景泰 | User-adapted language learning system |
-
2019
- 2019-12-13 CN CN201911271734.2A patent/CN110807317A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2197712Y (en) * | 1994-04-06 | 1995-05-17 | 詹国茂 | Assembled word learner |
| CN1555020A (en) * | 2003-12-23 | 2004-12-15 | 无敌科技(西安)有限公司 | Divergent expanding type language study system and method |
| TW201007645A (en) * | 2008-08-14 | 2010-02-16 | Inventec Besta Co Ltd | Method and system for dynamically changing the downloaded learning phase material according to learning efficiency of user |
| WO2010150986A2 (en) * | 2009-06-24 | 2010-12-29 | Nam Chan-Hee | Apparatus and method for the lifelong study of words in a foreign language |
| JP2011128365A (en) * | 2009-12-17 | 2011-06-30 | Cocone Corp | Language learning device and language learning method |
| CN102194346A (en) * | 2011-06-21 | 2011-09-21 | 无敌科技(西安)有限公司 | Embedded system and method thereof for automatically analyzing vocabulary difficulty and annotating difficult words |
| CN106856066A (en) * | 2016-12-20 | 2017-06-16 | 杨燕 | Word splits memory learning system |
| TW201903672A (en) * | 2017-06-01 | 2019-01-16 | 碩網資訊股份有限公司 | A deep learning based system and method for analyzing customer features thereof |
| CN108364512A (en) * | 2018-02-27 | 2018-08-03 | 清华大学 | A kind of English adaptive and learning system based on MOOC |
| CN108847076A (en) * | 2018-07-11 | 2018-11-20 | 北京美高森教育科技有限公司 | The assessment method of language learner |
| CN109727495A (en) * | 2018-07-11 | 2019-05-07 | 北京美高森教育科技有限公司 | A kind of language teaching device |
| TWM585415U (en) * | 2018-12-03 | 2019-10-21 | 陳景泰 | User-adapted language learning system |
| CN109817039A (en) * | 2019-03-18 | 2019-05-28 | 淮阴工学院 | Foreign language learning auxiliary device |
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