WO2020235853A2

WO2020235853A2 - Method for generating english vocabulary list optimized for user level

Info

Publication number: WO2020235853A2
Application number: PCT/KR2020/006321
Authority: WO
Inventors: 김주혁; 이승명
Original assignee: Kim Joo Hyeok; Lee Seung Myung
Priority date: 2019-05-20
Filing date: 2020-05-14
Publication date: 2020-11-26
Also published as: KR20200133635A; KR102260377B1; WO2020235853A3

Abstract

A method for generating an English vocabulary list optimized for a user level according to an embodiment of the present invention comprises the steps of: crawling English content from a web page server; extracting an English text from the English content; collecting English syntax items by analyzing the English text; classifying the English syntax items by level; providing an English syntax item corresponding to a user's level in a test form; receiving an answer for the test from the user; and adjusting the level of the English syntax item on the basis of the user's answer.

Description

How to create an English vocabulary optimized for user ratings

The present invention relates to a method for generating an English vocabulary book optimized for user ratings.

In general, learners who study language use a vocabulary book that memorizes words while carrying them easily and utilizes them for their achievement. When you write it on the spot and take it out and memorize it, you will create your own English vocabulary book that you can memorize.

On the other hand, with the development of digital and communication technologies, such offline vocabulary functions are provided as software and online services as a function of online education contents, but in the past, the use of offline vocabulary is merely provided as it is. It is inconvenient and does not utilize its advantages as software or online services.

The above-described content is only intended to aid in understanding the background art of the technical idea of the present invention, and therefore it cannot be understood as the content corresponding to the prior art known to those skilled in the art.

An object of the present invention is to provide a method of generating an English vocabulary optimized for a user level that supports user-customized English learning by automatically collecting and analyzing English text from a web page server.

In order to achieve the above object, a method for generating an English wordbook optimized for a user rating according to an embodiment of the present invention includes the steps of: crawling English content from a web page server; Extracting English text from the English content; Analyzing the English text and collecting English phrases; Classifying the English phrase by grade; Providing an English phrase corresponding to the user's level in a question format; Receiving an answer to the question from the user; And adjusting the level of the English phrase based on the user's response.

The crawling the English content from the web page server may include crawling the English content from the portal site vocabulary server.

The crawling the English content from the web page server may include crawling the English content from the overseas news web page server.

The extracting of the English text from the English content may include receiving a structure of the web page; And extracting the English text based on the structure of the input web page.

The step of analyzing the English text and collecting the English phrase may include collecting the English phrase by analyzing the text in the English text through a natural language analysis deep learning model.

The analyzing the English text and collecting the English phrase may include analyzing the English text and collecting an English word or English sentence.

Classifying the English phrase by grade may include classifying the English phrase according to the subject, difficulty, importance, and frequency of use.

Providing an English phrase corresponding to the user's level in a question format may include providing an English phrase corresponding to the level input by the user in a question format.

Adjusting the grade of the English phrase based on the user's answer may include adjusting the grade of the English phrase based on the user's grade and the correct answer rate.

According to an embodiment of the present invention, a method for generating an English vocabulary book optimized for user ratings includes: crawling English content from a web page server; Extracting English text from the English content; Analyzing the English text and collecting English phrases; Classifying the English phrase by grade; Providing an English phrase corresponding to the user's level in a question format; Receiving an answer to the question from the user; And adjusting the user's rating based on the user's response.

The crawling of the English content from the web page server may include crawling the English content from a portal site vocabulary server or an overseas news web page server.

Adjusting the user's rating based on the user's answer may include adjusting the user's rating based on the level of the English phrase and the correct answer rate of the user's answer.

According to the method for generating an English vocabulary optimized for user level according to the embodiment of the present invention, by automatically collecting and analyzing English text from a web page server and providing English words and sentences tailored to the user level, users can learn English. I can help. In addition, by continuously updating the grade of English words and sentences and the user grade through interaction, an English vocabulary optimized for the user grade can be created and maintained, and the user can also maintain a grade suitable for his or her ability.

1 is a block diagram schematically showing an English vocabulary generation system according to an embodiment of the present invention.

2 is a flow chart showing a method of generating an English wordbook according to an embodiment of the present invention.

3 is a flowchart illustrating a method of generating an English wordbook according to another embodiment of the present invention.

In the description below, for purposes of explanation, many specific details are presented to aid in understanding the various embodiments. However, it is obvious that various embodiments may be practiced without these specific details or in one or more equivalent manners. In other instances, well-known structures and devices are shown in block diagrams in order to avoid unnecessarily obscuring the various embodiments.

In the drawings, the sizes or relative sizes of layers, films, panels, regions, etc. may be exaggerated for clarity. In addition, the same reference numbers indicate the same components.

Throughout the specification, when a part is said to be "connected" with another part, this includes not only "directly connected" but also "indirectly connected" with another element interposed therebetween. . However, if it is stated that a part is "directly connected" to another part, it will mean that there are no other elements between that part and the other part. "At least any one of X, Y, and Z", and "at least any one selected from the group consisting of X, Y, and Z" means X one, Y one, Z one, or two of X, Y, and Z, or Any combination beyond that (eg, XYZ, XYY, YZ, ZZ) will be understood. Herein, “and/or” includes all combinations of one or more of the constituents.

Here, terms such as first, second, etc. may be used to describe various elements, elements, regions, layers, and/or sections, but such elements, elements, regions, layers, and/or Or the sections are not limited to these terms. These terms are used to distinguish one element, element, region, layer, and/or section from another element, element, region, layer, and/or section. Accordingly, a first element, element, region, layer, and/or section in one embodiment may be referred to as a second element, element, region, layer, and/or section in another embodiment.

Spatially relative terms such as "below", "above", etc. may be used for explanatory purposes, thereby describing the relationship of one device or feature to another device(s) or feature(s) as shown in the drawings. do. This is only used to represent the relationship of one component to another component on the drawings, and does not mean an absolute position. For example, if the device shown in the figure is turned over, elements depicted as being “below” other elements or features are positioned in the “above” direction of other elements or features. Accordingly, in one embodiment, the term “below” may include both directions above and below. In addition, the device may be in any other orientation (eg, rotated 90 degrees or in another orientation), and such spatially relative terms as used herein are interpreted accordingly.

The terms used herein are for the purpose of describing specific embodiments and not for the purpose of limiting. Throughout the specification, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless otherwise specified. Unless otherwise defined, terms used herein have the same meaning as commonly understood by those of ordinary skill in the field to which the present invention belongs.

Referring to FIG. 1, the English wordbook generation system includes a service server 30, and the webpage server 10, the user terminal 50, and the service server 30 are connected through a network 20 to generate an English wordbook. Send and receive all necessary data and information. As an example, the English vocabulary generation system may further include a database 40 for storing user information, English words, sentences, and their grades. As an example, the English wordbook may include at least one of English words, idioms, sentences, and phrases.

The web page server 10 is a web page server that provides English content. As an embodiment, the webpage server 10 may include at least one of a portal site server and an overseas news webpage server.

The network 20 may use various types of communication networks, for example, wireless LAN (WLAN, Wireless LAN), Bluetooth, Wi-Fi, Wibro, Wimax, high-speed downlink packet access ( Wireless communication methods such as HSDPA, High Speed Downlink Packet Access, or Ethernet, xDSL (ADSL, VDSL), HFC (Hybrid Fiber Coax), FTTC (Fiber to The Curb), FTTH (Fiber To The Home), etc. Wired communication method can be used. Meanwhile, the network 20 is not limited to the above-described communication method, and may include any other widely known or future developed communication method in addition to the above-described communication method.

The service server 30 transmits and receives all data and information necessary for generating an English vocabulary through the web page server 10, the user terminal 50 and the network 20 to provide an English vocabulary generation and learning service.

The service server 30 extracts English text after crawling English content from the web page server 10. As an embodiment, the service server 30 may collect English content from the web page server 10 using an API call or RSS. In addition, the service server 30 receives and stores the structure of each web page in advance in order to extract the English text from the English content, and may extract the English text using this. As an example, the service server 30 may crawl English content from the web page server 10 using the late night time or the early morning time at regular time intervals such as daily or weekly.

The service server 30 analyzes the extracted English text to collect English phrases, and classifies the collected English phrases by grade. As an embodiment, the service server 30 may collect English phrases by analyzing text phrases through a natural language analysis deep learning model. In addition, the service server 30 may classify English phrases by class according to subject, difficulty, importance, and frequency of use.

The service server 30 provides each user with an English phrase corresponding to the user's level in a question format using the English phrases classified by grade. Each user answers a question about the English phrase provided through the user terminal 50, and the service server 30 receives the user's answer from the user terminal 50, and then the English phrase based on the user answer. Adjust the rating of. As an embodiment, the service server 30 provides IH-level users with English phrases classified in IH grade in a question format, and then analyzes the correct answer rate of answers received from IH-level users, and the correct answer rate is too high or You can adjust the grade of English phrases that are too low. For example, the grade of an English phrase with a correct answer rate higher than 80% may be adjusted to a lower IM grade, and the grade of an English sentence with a correct answer rate lower than 20% may be adjusted to a higher AL grade.

The service server 30 may include a server operated by an English learning service provider. As an example, it may include a server operated by an English learning service provider such as Pagoda, Hackers, Min Byeong-cheol, YBM, and Siwon School.

The database 40 stores all information necessary for generating an English vocabulary book including user information. As an example, the database 40 may store user information, English words, sentences, and their grades.

The user terminal 50 describes a mobile communication terminal that is connected to the network 20 to transmit and receive data as a representative example, but the terminal is not limited to a mobile communication terminal, and all information and communication devices, multimedia terminals, wired/wireless terminals , A fixed terminal and an Internet Protocol (IP) terminal. In addition, the terminal is VR (Virtual Reality) device, AR (Augmented Reality) device, mobile phone, PMP (Portable Multimedia Player), MID (Mobile Internet Device), Smartphone (Smart Phone), Desktop (Desktop), Tablet Computer (Tablet PC), a notebook (Note book), a net book (Net book), a server (Server), and may include a mobile terminal having various mobile communication specifications such as information communication devices.

Referring to FIG. 2, the service server crawls English content from the web page server (S10), and extracts English text from the English content (S20). As an example, the service server may crawl English content from a portal site vocabulary server or an overseas news web page server. As an embodiment, the service server may collect English content from a web page server using an API call or RSS. As an example, the service server may crawl English content from the web page server using the late night time or the early morning time at regular time intervals such as daily or weekly. In addition, in order to extract the English text from the English content, the service server receives and stores the structure of each web page in advance, and can extract the English text by using this.

The service server analyzes the English text to collect English phrases (S30), and classifies the English phrases by grade (S40). As an embodiment, the service server may collect English phrases by analyzing text phrases through a natural language analysis deep learning model. As an example, English words or English sentences may be collected by analyzing English text. In addition, the service server may classify English phrases by class according to subject, difficulty, importance, and frequency of use.

The service server provides an English phrase corresponding to the user's grade in a question format (S50), receives an answer to the question from the user (S60), and adjusts the grade of the English phrase based on the user's answer (S70). ). As an embodiment, when the user first uses the English vocabulary service after signing up for the English learning service, the service server may provide an English phrase corresponding to the level input by the user at the time of signing up in a question format. As an embodiment, the service server may adjust the English syntax level based on the user's level and the correct answer rate. For example, the service server provides IH level users with English phrases classified by IH level in a question format, and then analyzes the correct answer rate of answers received from IH level users, so that the correct answer rate is too high or too low. You can adjust the grade of the phrase. That is, the grade of an English phrase with a correct answer rate higher than 80% can be adjusted to a lower IM grade, and the grade of an English phrase with a correct answer rate lower than 20% can be adjusted to a higher AL grade. As an example, the English wordbook may include at least one of English words, idioms, sentences, and phrases.

3, the service server crawls English content from the web page server (S100), and extracts English text from the English content (S200). As an example, the service server may crawl English content from a portal site vocabulary server or an overseas news web page server. As an embodiment, the service server may collect English content from a web page server using an API call or RSS. As an example, the service server may crawl English content from the web page server using the late night time or the early morning time at regular time intervals such as daily or weekly. In addition, in order to extract the English text from the English content, the service server receives and stores the structure of each web page in advance, and can extract the English text by using this.

The service server analyzes the English text to collect English phrases (S300), and classifies the English phrases by grade (S400). As an embodiment, the service server may collect English phrases by analyzing text phrases through a natural language analysis deep learning model. As an example, English words or English sentences may be collected by analyzing English text. In addition, the service server may classify English phrases by class according to subject, difficulty, importance, and frequency of use.

The service server provides an English phrase corresponding to the user's grade in a question format (S500), receives an answer to the question from the user (S600), and adjusts the user's grade based on the user's answer (S700). . As an embodiment, the service server may adjust the user grade based on the user's grade and the correct answer rate of the user's answer. For example, the service server can provide IH level users with English phrases classified as IH level in question format, and then adjust the user's level if the correct answer rate of the answers received from IH level users is too high or too low. have. In other words, if the correct answer rate of the answers received from the IH user is higher than 80%, the user's grade is adjusted to a higher AL grade, and if the correct answer rate is lower than 20%, the user's grade is lowered to the IM grade. Can be adjusted.

According to the embodiments of the present invention as described above, the method for generating an English wordbook optimized for user rating according to an embodiment of the present invention automatically collects and analyzes English text from a webpage server, By providing sentences, it is possible to help users learn English. In addition, by continuously updating the grade of English words and sentences and the user grade through interaction, an English vocabulary optimized for the user grade can be created and maintained, and the user can also maintain a grade suitable for his or her ability. As an example, the English wordbook may include at least one of English words, idioms, sentences, and phrases.

As described above, in the present invention, specific matters such as specific components, etc., and limited embodiments and drawings have been described, but this is provided only to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments. , If a person of ordinary skill in the field to which the present invention belongs, various modifications and variations are possible from these descriptions.

Therefore, the spirit of the present invention is limited to the described embodiments and should not be defined, and all things that are equivalent or equivalent to the claims as well as the claims to be described later fall within the scope of the spirit of the present invention. .

Claims

Crawling English content from a web page server;

Extracting English text from the English content;

Analyzing the English text and collecting English phrases;

Classifying the English phrase by grade;

Providing an English phrase corresponding to the user's level in a question format;

Receiving an answer to the question from the user; And

A method of generating an English wordbook optimized for a user's rating, including the step of adjusting a rating of the English phrase based on the user's answer.
The method of claim 1,

The step of crawling English content from the web page server,

A method of generating an English vocabulary optimized for user ratings, including the step of crawling English content from a portal site vocabulary server.
The method of claim 1,

The step of crawling English content from the web page server,

A method of generating an English vocabulary optimized for user ratings, including the step of crawling English content from an overseas news web page server.
The method of claim 1,

Extracting the English text from the English content,

Receiving a structure of the web page; And

A method of generating an English wordbook optimized for user ratings, including the step of extracting English text based on the structure of the input web page.
The method of claim 1,

Analyzing the English text and collecting English phrases,

Generating an English wordbook optimized for user ratings, comprising the step of parsing the English text through a natural language analysis deep learning model and collecting English phrases.
The method of claim 1,

Analyzing the English text and collecting English phrases,

Analyzing the English text and collecting English words or English sentences.
The method of claim 1,

The step of classifying the English phrase by grade,

And classifying the English phrase according to the subject, difficulty, importance, and frequency of use.
The method of claim 1,

The step of providing an English phrase corresponding to the user's level in a question format,

A method for generating an English vocabulary optimized for user ratings, including providing an English phrase corresponding to the level input by the user in a question format.
The method of claim 1,

Adjusting the level of the English phrase based on the user's response,

And adjusting the grade of the English phrase based on the user's grade and the correct answer rate of the answer.
Crawling English content from a web page server;

Extracting English text from the English content;

Analyzing the English text and collecting English phrases;

Classifying the English phrase by grade;

Providing an English phrase corresponding to the user's level in a question format;

Receiving an answer to the question from the user; And

A method of generating an English vocabulary optimized for user ratings, including adjusting the user's rating based on the user's answer.
The method of claim 10,

The step of crawling English content from the web page server,

A method of generating an English vocabulary optimized for user ratings, including the step of crawling English content from a portal site vocabulary server or an overseas news web page server.
The method of claim 10,

Extracting the English text from the English content,

Receiving a structure of the web page; And

A method of generating an English wordbook optimized for user ratings, including the step of extracting English text based on the structure of the input web page.
The method of claim 10,

The step of classifying the English phrase by grade,

And classifying the English phrase according to the subject, difficulty, importance, and frequency of use.
The method of claim 10,

The step of providing the English phrase corresponding to the user's level in a question format,

A method for generating an English vocabulary optimized for user ratings, including providing an English phrase corresponding to the level input by the user in a question format.
The method of claim 10,

Adjusting the user's rating based on the user's answer,

And adjusting the user's grade based on the grade of the English phrase and the correct answer rate of the user's answer.