KR102542268B1 - Table of Contents Memorization Learning System Using Keyword Image Interpreting - Google Patents

Abstract

A table of contents memorization learning system using keyword image substitution according to the present invention includes a word library for storing word groups, which are sets of a plurality of words having at least one of time-series relationships, bias relationships, and hierarchical relationships; an image library for storing a corresponding image corresponding to the stored word; a table of contents input module that receives a table of contents of a text including the main subject of the book; A major subject filtering unit that compares and processes the input number of major subjects with the number of words belonging to each word group to filter word groups having the same number of words as the number of major subjects; and selects one of the filtered word groups as a corresponding group a table of contents corresponding module including a corresponding group setting unit configured to be set as ; and a table of contents composition module that completes the table of contents of a book by editing and arranging words connected to the main topic, corresponding images corresponding to the corresponding words, and the table of contents of the text.

Description

Table of Contents Memorization Learning System Using Keyword Image Interpreting}

The present invention relates to a rote learning system for a table of contents using keyword image substitution. In more detail, the table of contents using keyword image substitution maximizes the memorization efficiency of the table of contents by matching mutually related words such as time-series relationship, bias relationship, and hierarchical relationship to each of the main and subtopics constituting the table of contents of the book. It is about a rote learning system.

One of the common sayings in book memorization learning is that ‘even if you just memorize the table of contents, you will understand and learn half of the contents of the book.’ This is an example that shows at a glance how important it is to remember the table of contents in learning.

This table of contents is a list in which the item titles and corresponding page numbers are listed in order in a book or document, etc., so that the contents can be briefly known or easily found. It is used similarly to the sequence of items written in a book or article. Usually, the table of contents is put first after the book cover, followed by the introduction of the author or the main body, and if there are many documents or book contents, a sub-table of contents is added separately in the middle.

It can be said that memorizing the table of contents is important in memorizing the contents of the book. If the number of main and subtopics included in the table of contents is small and simple, memorizing the table of contents can be done without difficulty, but if the contents of the table of contents are long and large, it is difficult to memorize the contents. It is natural that this exists.

In particular, when the content of the table of contents is complicated or the terms used are difficult, not only does it take more time to memorize, but it may not be easy to understand the table of contents itself.

In order to solve this problem, Korea Patent Publication No. 10-2013-0123206, 'Image Table of Contents Editing System for Memorizing Table of Contents', filed by the present applicant, breaks away from the known table of contents creation method, which was composed only of text characters. Therefore, a technology that promotes the convenience of memorization and understanding of the table of contents is disclosed by converting a word designated by a text character into an image and creating the image as a part of the table of contents together with the table of contents.

However, in such an image table of contents editing system, in the case of contents of a table of contents that cannot be imaged, the contents of the table of contents cannot be imaged, so only the contents of the table of contents that can be matched with images are imaged and used for memorization.

Therefore, in order to solve the problems described above, by matching words that are related to each other, such as time-series relationships, biased relationships, and hierarchical relationships, to each of the main topic and subtopic constituting the table of contents of the book, and reminiscent of the table of contents through the matched words There is a need to develop a system for memorizing a table of contents that maximizes the memorization efficiency of a table of contents by enabling memorization.

The present invention provides a table of contents memorization learning system using keyword image substitution, which connects a set of mutually related words to the contents of the table of contents and images the connected words so that the contents and images of the table of contents can be memorized. as its main purpose

Another object of the present invention is to connect and memorize contents of the table of contents and images, and increase memorization efficiency by repeatedly exposing images.

Another object of the present invention is to process keyword images having a hierarchical relationship by performing matching processing so that keywords also have a hierarchical relationship in linking major and subtopics and keywords of the table of contents.

A further object of the present invention is to maximize the efficiency of memorization through the emphasis treatment of large themes.

In order to achieve the above object, a table of contents rote learning system using keyword image substitution according to the present invention. a word library for storing a word group, which is a set of a plurality of words having at least one of a chronological relationship, a bias relationship, and an upper/lower relationship; an image library for storing a corresponding image corresponding to the stored word; a table of contents input module that receives a table of contents of a text including the main subject of the book; A major subject filtering unit that compares and processes the input number of major subjects with the number of words belonging to each word group to filter word groups having the same number of words as the number of major subjects; and selects one of the filtered word groups as a corresponding group a table of contents corresponding module including a corresponding group setting unit configured to be set as ; and a table of contents composition module that completes the table of contents of a book by editing and arranging words connected to the main topic, corresponding images corresponding to the corresponding words, and the table of contents of the text.

Furthermore, the system is characterized in that it includes a text editing module for additionally inserting the corresponding image on one side of at least one of pages of a book containing content related to each of the main topics.

In addition, the word library includes a function of receiving and storing a subgroup, which is a set of a plurality of words having a vertical relationship with words belonging to a specific word group, and the text table of contents includes subtopics, which are subcategories of the main topic. wherein the table of contents corresponding module includes a candidate group calculation unit for filtering subgroups having a vertical relationship with words belonging to the corresponding group among all word groups stored in the word library and designating them as candidate groups; and any one of the designated candidate groups. And a matching group setting unit for setting a matching group for the corresponding word, wherein the word linking unit connects any one of the words belonging to the matching group for each of the subtopics, and the table of contents configuration module It is characterized by editing and arranging words connected to the main topic and the sub-topic, a corresponding image corresponding to the corresponding word, and the table of contents of the text.

In addition, the system is characterized by including a text editing module for displaying a corresponding image of a word connected to the sub-topic on one side of at least one page among pages containing content related to the main topic.

The table of contents rote learning system using keyword image substitution according to the present invention,

1) A word group, which is a set of words having at least one of time-series relationship, bias relationship, and hierarchical relationship, is matched with the main topic constituting the table of contents, and words included in the word group are matched one-to-one with the main topic, The efficiency of memorizing the table of contents was improved by organizing the table of contents by arranging the matched words and corresponding images of the words together with the main topic.

2) Main subject and corresponding image. That is, the keyword image is substituted and memorized, but the corresponding image is repeatedly exposed in the text to increase the memorization efficiency.

3) In the sub-topic, which is a sub-component of the major topic, a sub-group having a semantic hierarchical relationship with the matched word group is matched once more and placed in the table of contents, memorizing the hierarchical relationship of the table of contents by connecting it to the hierarchical relationship of the keyword image. At the same time as allowing

4) There is an effect of maximizing the efficiency of memorization by emphasizing the major theme through the placement of the emphasizer, which is a marker for emphasizing meaning.

1 is a conceptual diagram showing a schematic configuration of the system of the present invention.
2 is a block diagram showing the overall configuration of the system of the present invention.
3 is a conceptual diagram showing an example of the table of contents of the present invention.
4 is a conceptual diagram showing an example of a book page;
5 is a conceptual diagram showing a table of contents of a book in which an emphasizer is arranged together.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The accompanying drawings are not drawn to scale, and like reference numbers in each drawing indicate like elements.

1 is a conceptual diagram showing a schematic configuration of the system of the present invention.

Referring to FIG. 1, the system for memorizing and learning a table of contents using keyword image substitution according to the present invention can be applied to a booklet made of paper as well as an e-book using electronic media, and the above-described booklet and e-book itself Of course, it encompasses providing the basis for making them.

In other words, the rote learning system for the table of contents of the present invention can be implemented as a book production system used by publishers and the like that produce books. The table of contents memorization learning system of the present invention includes hardware and software that performs detailed functions to be described later. The hardware may include a system server, a DB server, a web/app application server 4, and a table of contents server 3, Each server includes CPU, memory, and storage space. In addition, software will be embodied below as a program or performing a series of processes based on the hardware. That is, the system of the present invention means that such hardware and software are organically combined.

In the present invention, the system means the main server 1 that implements the system, and here, it should be clarified in advance that the system has the same meaning as the main server 1.

Therefore, the system, that is, the main server 1 receives the text table of contents including the main subject of the book from the author terminal 2 such as a computer or tablet PC equipped by the author, and interworks with dictionary servers in Korean/English and other languages. By becoming a dictionary, it grasps the meaning of the main subject entered through the dictionary and the word group, which is a set of words to be described later, and receives and stores the word group from one of these dictionary servers, system administrators, or writers. , and has a role of receiving and storing a corresponding image corresponding to a word included in a word group.

In addition, the system matches the input word group with the main subject of the book, connects the word of the matched word group and each main subject, and arranges the image corresponding to the word together with the linked main subject in the table of contents of the book. performs the function of completing

This system refers to hardware having a CPU and storage means for processing information. In other words, the system includes a central processing unit (CPU), storage means such as memory and hard disk, and wireless communication equipment such as wired and Bluetooth. A program that can be executed in the central processing unit, that is, software, is installed on the hardware base provided and this software can be executed. .

At this time, the system temporarily and / or permanently stores the signals (or data) processed therein (RAM: Random Access Memory, not shown) and ROM (ROM: Read-Only Memory, not shown), processor can include In addition, the system may be implemented in the form of a system on chip (SoC) including at least one of a graphics processing unit, RAM, and ROM.

The processor may include one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus) for transmitting and receiving signals to and from other components.

The memory may store programs (one or more instructions) for executing and controlling modules or parts to be described later. Programs stored in the memory may be divided into a plurality of modules according to functions.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module includes RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, Alternatively, it may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

That is, the components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler). Functional aspects may be implemented in an algorithm running on one or more processors.

The configuration of such a 'module' or 'unit' or 'interface' refers to a configuration of hardware such as software or FPGA or ASIC that is installed and stored in a storage means of a system and executed via a CPU and memory. At this time, the configuration of 'module' or 'unit' or 'interface' is not limited to hardware, and may be configured to be in an addressable storage medium or configured to reproduce one or more processors. As an example, 'module' or 'part' or 'interface' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables.

Functions provided by these 'modules' or 'units' or 'interfaces' may be combined into a smaller number of components and 'units' or 'modules', or may be combined into additional components and 'units' or 'modules'. can be further separated.

Hereinafter, detailed configurations and functions thereof will be described based on the macroscopic configuration of such a system.

2 is a block diagram showing the overall configuration of the system of the present invention.

Referring to FIG. 2, the table of contents memorization learning system using keyword image substitution of the present invention includes a word library 100, an image library 200, a table of contents input module 300, a table of contents corresponding module 400, and a table of contents configuration module. It is characterized by including (500).

The word library 100 is a word set of a plurality of words from at least one of the above-described dictionary server or the author's terminal 2 possessed by the writer, the manager terminal possessed by the system administrator, or the editor terminal possessed by the editor of the book publishing company. Performs a function to receive and save a group.

Here, the writer terminal 2, the manager terminal, and the editor terminal can communicate with the system (main server 1) through communication and perform the input function of the word group, preferably PC, laptop, tablet PC , It may be at least one of smart phones.

In addition, at this time, a word group means a set of words including a plurality of words, and it is based on that a plurality of words constituting one word group are mutually related words. In this case, the mutual correlation between words is at least one of a time-series relationship, a bias relationship, and a hierarchical relationship. When representing semantic relationships between words, representative ones are time-series relationships, bias relationships, and hierarchical relationships.

First of all, time-series relationship means a relationship according to the lapse of time, and means a relationship having an arrangement, organization, and order according to the chronological order. In a similar sense, there may be a vertical relationship. An example of a time-series relationship could be 'Spring, Summer, Fall, Winter' or 'Breakfast, Lunch, Dinner'.

A bias relationship is an association relationship between a certain subject field and a certain concept, and generally means a relationship in which the concept is considered to belong to another subject field. For example, in the case of 'Forsythia, Chick, Butterfly', it can be said to be a word group having a biased relationship in that it belongs to the subject field of 'Spring'. Another example of a biased relationship is 'snow, ice, steam, In that 'rain' belongs to the subject field of 'water', it can be said to be a group of words bound by a biased relationship. Here, the bias relationship may be an association relationship of a general concept, but as some subjective interpretation may be involved, in the case of a word belonging to a word group having a bias relationship, the subjectivity of an editor, writer, or system administrator may be reflected and input processed.

The hierarchical relationship is one of the hierarchical structures of vocabulary, meaning a vertical relationship in which one lexical element semantically subsumes or is subsumed by another lexical element. This includes some biased relationships and similar content. More specifically, the hierarchical relationship can be divided into a place subsumption relationship, a part-whole relationship, and a class subsumption relationship.

Here, the relationship of subsuming place means a relationship in which one of the two elements that make up the hierarchy is included as a place in the other, and can be expressed as “Go is inside me.” For example, the relationship between 'room' and 'house', which can be expressed as "the room is in the house." Therefore, 'room' can be a sub-composition of the concept of 'house', and at this time, 'room, kitchen, bathroom, living room', etc., which can be sub-compositions of the concept of 'house', can be one word group. there is. Here, the word group of 'room, kitchen, bathroom, living room' is basically stored including information about the sub-concept of the word 'house'.

A part-whole relationship is a relationship in which one word functions as a part of another word. You can use the sentence frame “Going is part of me” to identify relationships. For example, the whole part relationship between the body and the arm can be identified as “the arm is a part of the body.” Therefore, there can be 'arm' as a sub-composition of the concept of 'body', and at this time, 'arm, leg, head, torso, waist', etc. It can be. These word groups of 'arms, legs, head, torso, and waist' are stored including information that is a sub-concept of the word 'body'.

A class subsumption relationship is a relationship in which one of the two elements constituting the hierarchy collectively refers to the class of the other. It appears in the sentence ‘It is the age to go’. For example, in ‘Azalea is a flower’, ‘Azalea’ is a type of flower. Therefore, there can be 'Azalea' as a sub-composition of the concept of 'flower', and at this time, 'Azalea, Forsythia, Rose', etc., which can be sub-compositions of the concept of 'flower', can be one word group. In addition, these word groups of 'Azalea, Forsythia, and Rose' are stored including information about the sub-concept of the word 'flower'.

Therefore, a word group having a hierarchical relationship can be referred to as a word group in which a plurality of words having a hierarchical relationship with a specific word are grouped into a word group. to include information about

Here, since the number of words constituting the word group is not limited, the number of words may be different for each individual word group. In addition, it is possible for a specific word group to have multiple relationships. For example, it is possible for the words 'A, B, C, D' belonging to a specific word group to have a time-series relationship, as well as a bias relationship and a top-down relationship, which means that the semantic relationship between words is more complex and diverse than expected. It is due to what can be assumed.

Therefore, the word library 100 classifies each word group and stores the words belonging to the word group, and it is more desirable to record and store the meaning of the word together through a dictionary. To this end, as described above, the dictionary server and the system may be interlocked.

The image library 200 receives and stores a corresponding image corresponding to a word included in a word group stored in the word library 100, that is, an image corresponding to a word stored in the word library 100. At this time, the corresponding image may be input through an editor terminal or an administrator terminal.

For example, taking an example in which word groups including 'Azalea, Forsythia, and Rose' are stored in the word library 100, the image library 200 displays images included in the word group, that is, 'Azalea',' Corresponding images corresponding to 'forsythia' and 'rose' are stored. This may be entered by a system administrator, a publisher administrator, or a writer.

In addition, preferably, the corresponding image indicates an image that can represent the corresponding word, for example, an icon image for the corresponding word, or a jpg or jpeg that is representatively displayed through a website search. gif. png extension, and may or may not include a background. Most preferably, an image representing a corresponding word may be referred to as a corresponding image. For example, in the case of 'rose', an image representing a picture of a rose or a photograph of a rose may be a corresponding image.

Therefore, corresponding images corresponding to words belonging to all word groups stored in the word library 100 are stored in the image library 200, and at this time, the image library 200 adds word groups to the word library 100 in real time. Corresponding images can be received and stored together.

3 is a conceptual diagram showing an example of the table of contents of the present invention.

Referring to FIG. 3 , the table of contents input module 300 performs a function of receiving an input of a table of contents of text basically created by a writer. That is, the table of contents is generally formed on the initial pages of booklets, e-books, etc. This table of contents is mainly composed of word combinations and text in the form of sentences. The input module 300 performs a function of inputting a text table of contents through a writer's terminal (mainly a PC or an application) possessed by a writer or scanning a document having a text table of contents, reading characters by an OCR program, and then inputting the table of contents.

In addition, the table of contents of the text entered at this time may be stored in the table of contents server 3. Here, the table of contents server 3 refers to a server mainly provided by a publishing house, and can be linked with a computer program or application of a smartphone (ie, implemented as a table of contents editing interface), and literary works (such as novels, etc.) It is possible to provide a function of inputting and saving a written copy of a text) as well as a function of inputting a table of contents from an author.

When a writer intends to produce a book (including paper books, e-books, etc.) of his or her own content, a table of contents related to the content, that is, a text that clearly or concisely summarizes the subject or core content of the main body of the content, is created. So, by organizing this into a table of contents, I would like to inform the reader about the overall story of the main text.

Such a table of contents is mainly composed of a combination of words or a sentence form, that is, text. When a writer directly creates and provides the table of contents in this way, the table of contents input module 300 receives the table of contents from the writer, thereby generating the table of contents. It is possible to configure the table of contents on the initial page of the book.

Here, the table of contents may include a main topic of the book and a sub-topic that is a sub-concept of the main topic. As shown in FIG. 3, paragraph 1, paragraph 2, paragraph 3, and paragraph 4 are referred to as major themes, and the major topics may receive sub-topics, which are sub-concepts, which may be indicated as 1), 2), and 3).

Therefore, most preferably, in a state in which the table of contents basically includes at least one major topic, each major topic may or may not include a minor topic.

In addition, the main theme is paragraph 1. Sentence structure / 2 paragraphs. Tense / 3 paragraphs. Auxiliary verb / 4 paragraphs. Indicates a classification of large subjects, such as the passive voice. The sub-topic can be composed of 1) tense / 2) present perfect included in the tense of the second paragraph, for example.

Since it is desirable that the table of contents of the text including the main topic and subtopic be input by the writer, it can be input according to the writing direction and writing content that the writer thinks.

In summary, the table of contents input module 300 inputs the text table of contents, including major and subtopics, written by the most basic writer to the system of the present invention to create a table of contents to which keyword images sought in the present invention are substituted. is to do

Assuming that the table of contents corresponding module 400 receives input of a text table of contents including a major subject, the table of contents corresponding module 400 connects the major subject with words of a specific word group by substituting words belonging to a specific word group to the major subject included in the text table of contents. perform processing functions. To this end, the table of contents corresponding module 400 includes a subject filtering unit 310, a corresponding group setting unit 320, and a word linking unit 330.

The major subject filtering unit 310 compares and processes the number of words belonging to each word group stored in the word library 100 and the number of major subjects input in the table of contents of the book, and performs word groups in which the number of major subjects matches the number of words. It performs the function of filtering only .

For example, if the number of major topics is 4 of paragraph 1, paragraph 2, paragraph 3, and paragraph 4, only word groups including 4 words are filtered. For example, in the example of the above word group, the word group 'Spring, Summer, Fall, Winter', 'Snow, Ice, Steam, Rain', 'Room, Kitchen, Bathroom, Living Room', which is a word group containing four words, and Only word groups containing four words are filtered out.

Therefore, it can be said that the function of filtering only the word groups in which the number of major subjects and the number of words belonging to the word group match, allowing one-to-one connection between the major subject and the word group is performed.

The corresponding group setting unit 320 performs a function of setting, as a corresponding group, one of the word groups filtered by the major subject filtering unit 310, that is, the word groups having the same number of words as the number of major topics.

At this time, since there is no restriction on the matching group setting method, random setting may be performed, or one of the word groups filtered by a system administrator, writer, publisher manager, etc. may be selected and the selected word group may be set as the matching group. .

The word linking unit 330 performs a function of connecting one of the words belonging to the corresponding group for each major topic and performing a one-to-one correspondence between the major topic and the words belonging to the corresponding group, that is, one-to-one connection processing. The words belonging to the group and the main subject are sequentially connected. As shown in the example of FIG. 3, paragraph 1 is connected to spring, paragraph 2 is summer, paragraph 3 is autumn, and paragraph 4 is connected to winter. is to sequentially process one-to-one connection. Alternatively, the main topic and words belonging to the correspondence group may be randomly connected. Therefore, if one-to-one correspondence (one-to-one connection) is possible, the connection method is not limited.

In addition, the word linking unit 330 may also automatically perform connection processing on the system, or select any one of the words belonging to the corresponding group selected by the system administrator or the like and any one of the main topics and process the connection of the selected main topic and words. Of course it is possible.

The table of contents composition module 500 performs a function of completing a visual table of contents of a book by editing and arranging a text table of contents including a word connected to a main subject, a corresponding image corresponding to the word, and the connected main subject. An example of the table of contents completed here is shown in FIG. 3 .

Here, the words and corresponding images connected to each major subject of the text table of contents are edited and arranged together. Most preferably, the words connected to the corresponding major subject are listed next to the corresponding major subject, and the corresponding image corresponding to the word is placed near the corresponding major subject so as to be included in the book. It can be said that it performs the function of completing the inserted table of contents page.

To this end, a template of the table of contents page may be created first, or it is possible to complete the table of contents by arranging major topics, words, and corresponding images in a template pre-input by a system administrator or publisher manager.

Here, the template refers to the layout for the table of contents of the book, and the main subject. It can be said that approximate positions for arranging words connected to the main subject and corresponding images corresponding to the words are designated.

Therefore, at each position of the template, the main topic included in the text table of contents, the words connected to the main topic, and the corresponding images corresponding to the words are arranged/input processed to complete the visual table of contents.

In other words, by inserting an image on one side of each block constituting the text table of contents, or by placing the text table of contents on the left page and the image on the right page, etc., the table of contents designed with the image as a whole is provided, and the freshness of the table of contents is maintained. Not only can refinement be pursued, but since the image embodies a specific word connected to the main topic of the text table of contents, readers can look at these images and substitute more connected words, that is, keyword images, so that they can remember the table of contents well. It provides characteristics that make it easier to understand and remember the overall plot or key flow.

4 is a conceptual diagram showing an example of a book page.

Referring to FIG. 4 , the system of the present invention may further include a text editing module 600 to insert a corresponding image into a page of a book.

The text editing module 600 performs a function of additionally inserting a corresponding image on one side of at least one page on a page containing content related to each major subject in the text of the book, which is also An example can be found in 4.

Most preferably, a corresponding image is placed together with the main topic on a page where the main body of the main topic begins, and the word connected to the main topic and the corresponding image are emphasized and words connected through the corresponding image, that is, keywords and keywords. It is possible to increase the learning efficiency of table of contents memorization through images.

Therefore, instead of inserting a corresponding image only in the table of contents, a corresponding image is inserted along with the main topic in the body of the table of contents by repeatedly exposing the image to the reader by connecting the corresponding image, that is, the keyword, which is a connected word, and the keyword image, which is an image of the keyword, to the main topic. It maximizes the efficiency of rote learning.

Furthermore, in the case of the word groups stored in the above-described word library 100, it is possible to have an upper-lower relationship between words. That is, for a word belonging to a specific word group, there may be another word group having a vertical relationship with the corresponding word.

For example, if there is a word group 'spring, summer, fall, winter', the word group 'forsythia, chick, butterfly', which is associated with 'spring' belonging to the word group, has a vertical relationship with 'spring'. is to have That is, there is a hierarchical relationship even within a word group. To classify this, the word library 100 includes a function of receiving and storing a subgroup, which is a set of a plurality of words having a hierarchical relationship with words belonging to a specific word group. can do.

In other words, a set of a plurality of words having an upper-lower relationship with words belonging to a specific word group is stored as a subgroup of words belonging to the corresponding word group. To explain this as an example, the word group 'forsythia, chick, butterfly', which is a set of words that have a vertical relationship with 'spring' belonging to the word group 'spring, summer, fall, winter', is 'spring, summer, fall, winter'. Among the word groups, it becomes a subgroup of the word 'spring' belonging to the group.

Therefore, through this function, an upper-lower relationship between word groups, that is, a hierarchy is created, and an upper group, which is a word group at a higher level of a specific word group, and a lower level group at a lower level of a specific word group can be set. . At this time, the subgroup and the upper group may be input by a system manager or a publisher manager. In setting the hierarchy, it is also possible to set the hierarchy by using word-net on the system, or it is possible to set the hierarchy by the system manager or the publisher manager.

Here, most preferably, the word group connected to the major subject is the highest group, and although mentioned in the above description, the group connected to the minor subject, which is a sub-category of the major subject, may be a subgroup of the corresponding word group.

To this end, the table of contents corresponding module 400 may include a candidate group calculation unit 410 and a matching group setting unit 420 .

The candidate group calculation unit 410 has a hierarchical relationship with words belonging to the corresponding group linked to the above-mentioned main subject among all word groups stored in the word library 100, preferably a lower layer of words belonging to the corresponding group. Only the group is filtered and the filtered subgroups are designated as candidate groups.

Here, the candidate group may be at least one, and for example, when the corresponding group is 'spring, summer, fall, winter', a subgroup that is a lower layer of words 'spring', 'summer', 'fall', and 'winter'. to filter only Examples of filtered subgroups would be 'forsythia, chick, butterfly' subgroup having a hierarchical relationship with 'spring', and 'snow, snowman, ice, Christmas tree, sleigh' subgroup having a hierarchical relationship with 'winter'. can Therefore, only the filtered subgroup is set as a candidate group.

At this time, at least one candidate group can be calculated for each word belonging to the corresponding group, and the number of candidate groups to be calculated is not limited.

The matching group setting unit 420 performs a function of setting any one of the designated candidate groups as a matching group for the corresponding word. For example, in the case of the word 'spring', 'forsythia, chick, Set the 'nabi' group as a matching group for the word.

Since there is no restriction on the matching group setting method here, if only one candidate group is designated for a specific word, the corresponding candidate group is directly designated as the matching group, and if several candidate groups are designated for a specific word, one of them is selected as the corresponding word Set as a matching group for .

This may be done by a system administrator, writer or publisher manager, or may be set randomly on the system. Therefore, there is no separate restriction on the matching group setting method.

In this case, the word linking unit 330 performs a function of connecting a word of the corresponding major topic with any one of the words belonging to the matched matching group for each sub-topic that is a sub-category of the specific major topic.

In other words, in the example of paragraph 1 connected to the main theme of ‘Spring’, let’s say that paragraph 1 has sub-topics of 1), 2), and 3). At this time, for each sub-topic, one of the words belonging to the matching group, 'forsythia, chick, and butterfly', is connected and processed.

For example, 1) is a chick, 2) a butterfly, and 3) a chick. In this case, matching processing must be performed for all subtopics. That is, the number of words belonging to the matching group must be equal to or greater than the number of subtopics, and if the number of words belonging to the matching group is smaller than the number of subtopics, the matching group may be changed.

Therefore, one word is processed for each sub-topic, and the word group of words connected to the sub-topic is a sub-group of words connected to the main subject.

In this case, the table of contents configuration module 500 edits and arranges each major topic, words connected to the subtopic, and corresponding images and texts corresponding to the words to form a visual table of contents page, thereby forming a visual table of contents page, thereby forming a list of connected words (keywords) and corresponding words. It is possible to grasp the hierarchical relationship at a glance with only the corresponding image (keyword image), thereby increasing memorization efficiency.

In addition, in this case, the above-described text editing module 600 described in conjunction with FIG. 4 goes further than adding the corresponding image to one side of at least one of the pages of the book containing the contents related to each of the major topics. , Corresponding images of words connected to the sub-topic may be displayed on one side of at least one page among pages containing content related to the main topic.

That is, on one side of at least one of the pages of the major topic, each of which contains contents related to the major topic, not only the corresponding image of the word connected to the major topic, but also the corresponding image of the word connected to the minor topic is displayed together, so that not only the corresponding image of the major topic Corresponding images of subtopics are also repeatedly exposed, so that the reader can increase the efficiency of memorizing and learning the table of contents through keywords and keyword images connected through corresponding images.

Furthermore, the above-described table of contents configuration module 500 of the present invention provides a function of editing and arranging a text table of contents including major and sub-topics, words connected to each of the main and sub-topics, and corresponding images corresponding to the words. Specifically, it consists of a page setting unit 510, an arrangement setting unit 520, and a table of contents editing unit 530.

The page setting unit 510 performs a function of setting the number of pages occupied by the table of contents in a book, and preferably determines the table of contents page in consideration of all pages of the book.

The arrangement setting unit 520 performs a function of determining the size and position of a corresponding image to be manually or automatically arranged through an author's operation or a standard template preset on the system.

In the manual operation method, a manipulation window is provided in the writer's terminal 2 possessed by the artist, and a corresponding image is inserted into the table of contents like the well-known 'preview' function in the manipulation window, and then the position and size of the corresponding image are displayed with the mouse. It can be operated by a drag and drop method or, in the case of a touch screen in a mobile communication terminal (smart phone), it can be operated by hand.

That is, the manual means in the arrangement setting unit 520 allows the size and size of the corresponding image to be adjusted within the table of contents of the book through the operation window.

In addition, the automatic setting means in the arrangement setting unit 520 automatically determines the size and position of the corresponding image through a standard template prepared in advance. Specifically, the number of pages of the table of contents and the number of corresponding images It provides a function of searching for matching standard templates, extracting search results, and using the corresponding image size and location provided in any one template among these search results.

Here, the reference template is the same as a layout in which the configuration of pages is set in advance, and since the database is processed with the number of pages and images together, the number of images/contents pages determined by the above function and the comparison search method to select the most suitable reference template.

The table of contents editing unit 530 performs a function of editing the corresponding image together with the text table of contents according to the size/position of the corresponding image determined by the arrangement setting unit 520 to finally generate the table of contents.

That is, corresponding images are inserted on one side of each main topic and subtopic constituting the text table of contents, or the text table of contents is located on the left page and the corresponding image is located on the right page, etc. to provide a table of contents designed by the corresponding image as a whole. Not only can the freshness and sophistication of the table of contents be pursued, but the corresponding image is a representation of words connected to each of the major and minor topics belonging to the table of contents, so that the reader can remember the table of contents better by looking at these corresponding images It provides characteristics that make it easier to understand and remember the overall plot or core flow of the booklet.

5 is a conceptual diagram showing the table of contents of a book in which an emphasizer is co-located.

Referring to FIG. 5, the above-described table of contents configuration module 500 further includes an emphasizer generating unit 540 to generate an emphasizer 10 (emphasizer), which is a mark emphasizing a major topic, Can be placed nearby.

Here, the emphasizer (10) is a compound of the meaning of 'emphasize' and '~er' meaning 'one who does', meaning 'one who emphasizes'. That is, here, the emphasizer 10 means a mark for emphasizing the major subject, and there is no limitation on its shape. Therefore, the emphasizer 10 may be a mark of a specific shape, such as an underline drawn on the main subject or a star or a heart attached to the front of the main subject.

Therefore, such an emphasizer 10 is also displayed in the table of contents of the book, so that the emphasizer 10 is placed near the main topic to conspicuously emphasize the main topic, so that the memorization effect for the main topic can be further enhanced.

However, more preferably here, rather than the emphasizer 10 simply underlining the major theme, a mark of a specific shape becomes the emphasizer 10 to conspicuously emphasize the major theme. At this time, the shape of the emphasizer 10 is not limited, but in the present invention, a star-shaped mark is used as the emphasizer 10.

Therefore, through the insertion and arrangement of the emphasizer 10, the efficiency of memorization of the main subject, that is, the table of contents, can be increased once more by emphasizing the main subject.

Further here, the table of contents configuration module 500 may add a variable control configuration for the size of the batch-processed emphasizer 10 for a specific subject. To this end, the table of contents configuration module 500 determines the degree of similarity ( 550) and an emphasizer size controller 560 may be further included.

The similarity determination unit 550 performs a function of determining the similarity between the words and the text of the major topic that has been connected. In other words, as the main theme, ‘paragraph 1. If there is a 'sentence structure' and the word connected to the main topic is 'spring', the similarity between the 'sentence structure' and the word 'spring' is compared and processed.

At this time, since no restrictions are placed on the similarity comparison processing method between words, object chain laddering, sentence structure similarity comparison, latent semantic structure-based analysis, related word vector-based analysis, similarity using predicate bigram, which are conventional methods used for word similarity Comparison processing may be performed through various conventional similarity comparison methods that have not been mentioned or analyzed, or similarity comparison analysis using word-net, which will be described later, can be performed.

Through this similarity analysis, a digitized similarity value is identified. As the identified similarity value, that is, a digitized similarity value, is identified, the emphasizer size control unit 560 determines the level of the emphasizer 10 according to the level of similarity. It performs the function of differentially variable controlling the size. Here, the size of the emphasizer 10 means the length or width of the emphasizer, and most preferably, in the image file constituting the emphasizer 10, a rectangular box used for adjusting the image size is selected. Then, the size of the emphasizer 10 can be adjusted by adjusting the diagonal length. That is, the diagonal length of the rectangle connecting the four vertexes of the image or object of the emphasizer 10 is adjusted.

That is, if the similarity between the text of a specific major topic and the words connected is high, the size of the emphasizer 10 is increased, and if the similarity between the text of a specific major topic and the words connected is low, the size of the emphasizer 10 is made small. . Or, conversely, if the similarity between the text of a specific major topic and the connected words is high, the size of the emphasizer 10 is reduced to a small size, and if the similarity between the text of a specific major topic and the words connected is low, the size of the emphasizer 10 is reduced. It is also possible to make it larger.

Therefore, in order to increase the memorization efficiency of the main subject, that is, the table of contents, through the connected words, the size of the emphasizer 10 disposed near the main subject is variably controlled according to the similarity between the text of the main subject and the words, and based on this This is to maximize the memorization efficiency for the main topic, that is, the table of contents.

More preferably, it is possible to determine the similarity between the text of the main subject and words based on WordNet. For this purpose, the similarity determining unit 550 includes an LCS determining part 551 and a similarity determining part 552 It can be.

The LCS identification part 551 performs a function of identifying the lowest common subsumer (LCS), which is a concept encompassing the text of the main subject and the word, which have been connected and processed on word-net.

Here, WordNet means a semantic vocabulary list of English. WordNet categorizes English words into a group of synonyms called 'synset', provides brief and general definitions, and records various semantic relationships between these vocabularies. Its purpose is twofold. One is to create a combination of a dictionary (a glossary) and a thesaurus (thesaurus and antonyms) that can be used more intuitively and support automated text analysis and artificial intelligence applications.

The database and program tools are distributed under a BSD-type license, and can be downloaded and used freely. The database can also be searched online.

WordNet was created and maintained by the Center for Cognitive Sciences at Princeton University, directed by George A. Miller, professor of psychology. Development began in 1985. Over the years, the project has raised $3 million in funding, mostly from government agencies interested in machine translation. In recent years, Dr. Christiane Fellbaum has been looking at the development of WordNet.

Therefore, in the case of this WordNet, since it uses the conventionally used concept, a detailed description will not be made.

In the present invention, in order to perform comparison processing between the text of the main topic and the words connected on WordNet, all words connected to the text of the main topic are translated into English, and the degree of similarity between the words translated into English is determined. do.

Here, in the English translation, when the text of the corresponding word or the corresponding major topic is translated by a translation tool such as Papago or Google Translate, a representative English translation is used.

WordNet has a hierarchical form in which a higher concept has a comprehensive meaning and a lower concept has a specific word. A concept is a node containing a word in WordNet.

Lowest common subsumer (LCS) refers to a semantic concept that can simultaneously cover the text of a major topic and words connected to the corresponding major topic on WordNet. In other words, if A is the text of the major topic and B is the word connected to the major topic, the closest word or concept that can simultaneously encompass the meanings of A and B becomes the LCS, or LCS.

In other words, concepts that are connected with upper and lower concepts like a net, that is, concepts that simultaneously encompass the meaning of the text of the main subject (A) and the meaning of the word (B) connected to the main subject in the relationship between notes, A and B The concept closest to is the Least Common Consumer, or LCS.

The similarity determination part 552 identifies the text of the major subject, the connected words, and the least common subject on WordNet, that is, WordNet having a hierarchical form forming a mesh, and the text of the major subject connected based on their relationship. and a function of determining the degree of similarity between words.

Preferably, the degree of similarity may be calculated through Equation 1 below.

Equation 1,

Here, S is the similarity, L is the distance between the text and word of the major topic connected on WordNet, and D is the maximum depth of the least common subject.

Here, the distance refers to the text (A) of the major topic on WordNet, such as a mesh. And it means the distance between when the connected words (B) are connected to each other, and the maximum depth of the LCS means the maximum value of the depth in the concept of the LCS. The value here is a value given from WordNet and has no unit.

Here, when explaining the maximum depth of the LCS, when a semantic pair of the text (A) of the major topic and the LCS is formed, on WordNet calculated based on the corresponding semantic pair When a semantic pair of the depth value, the connected word (B), and the LCS is formed, the depth value on WordNet calculated based on the corresponding semantic pair is calculated, and a larger value is extracted from the value. can be said to have been

All of these values are values of meaning pairs of words that can be found in the concept of WordNet, so they are based on values that can be found in the WordNet database.

As an example, the distance calculated for the semantic pair (text of the main topic (A), word processed by concatenation (B)) is 5, the depth is 4, and the meaning of the text (text of the main topic (A), LCS) Assuming that the distance calculated for the pair is 10, the depth is 3, and the calculated distance is 8 and the depth is 5 for the semantic pair (connected word (B), LCS),

similarity

is calculated as

Therefore, in this WordNet-based distance and depth-based similarity calculation method, not only the semantic distance between two concepts, but also the conceptual depth to the concept encompassing the two concepts are reflected, and the similarity is calculated by reflecting the relationship between concepts. This has the effect of increasing the objectivity and accuracy of similarity judgment.

In addition, when calculating similarity, the size of the emphasizer 10 is variably controlled according to the above-mentioned conceptual similarity, so that the similarity is calculated according to the relationship between concepts, and the emphasis marker, The size of the emphasizer 10 is differentially and variably controlled.

Through this, it is also possible to enable the reader to more emphasize and confirm the main topic connected with concepts with high semantic similarity, and to memorize the main topic to which keyword images (corresponding images) with high semantic similarity are connected more by emphasizing. .

Or, by allowing concepts with low semantic similarity to be more emphasized and identified, the keyword image (corresponding image) that does not match well can be memorized by emphasizing the connected subject, maximizing memorization learning efficiency. let it be

Furthermore, for each word group entered into the word library 100, the corresponding word group is processed in connection with the main topic of the book input by the author, and is placed in the table of contents and body of the book and reflected in the content of the book. .

At this time, there may be a writer's preference for a word group. The writer's preference may be high for a specific word group, but the writer's preference may be relatively low for a specific word group.

In this case, in the variable control of the size of the emphasizer 10, if a word group with a higher author's preference is matched by reflecting the author's preference, that is, a word with a higher author's preference is matched, the main subject associated with the corresponding word The similarity calculated between texts is corrected a little higher to increase the size of the emphasizer 10, or conversely, if a word with a low preference of the writer is matched, it is between the word and the text of the major topic connected It is also possible to reduce the size of the emphasizer 10 by correcting the calculated similarity slightly lower.

To this end, the similarity determination unit 550 may include a weight input part 553 and a correction value determination part 554 .

The weight input part 553 performs a function of receiving a preference weight for each word group input into the word library 100 from the author's terminal 2 possessed by the author of the book. Here, the preference weight can be input in a range of real numbers having a value greater than 0 and greater than 1, and is entered for each word group. That is, for all word groups input to the word library 100, a word group having a high preference has a value closer to 1, and a word group having a lower preference has a value closer to 0.

Therefore, the preference weight is input and processed from the writer terminal 2 such as a PC, smartphone, or tablet PC possessed by the writer, and for this purpose, all word groups in the word library 100 are listed in the writer terminal 2 and receives the preference weight for each word group from the writer terminal 2 as a real value within the above range.

Subsequently, the correction value identification part 554 performs a function of calculating a correction value obtained by correcting the similarity based on the input preference weight. Here, the correction value calculation method is not limited, but preferably, the correction value is calculated through Equation 2 below.

Equation 2,

는 특정 대주제의 텍스트와 연결 처리된 단어가 포함된 단어그룹에 대해 입력된 선호도가중치로서

, D는 최소공통포섭자(LCS)의 최대 깊이)(Where R is a correction value, S is similarity,

is the preference weight entered for the word group including the word associated with the text of a specific subject.

, D is the maximum depth of the LCS)

Here, the above-mentioned preference weight is explained. Assuming that the text (A) of the major topic and the word (B) that have been connected, the word group that has been connected, that is, input from the writer terminal (2) for the word group including B This means that the preference weight In addition, as in the above description, the maximum depth of LCS is the depth on WordNet calculated based on the semantic pair of the text (A) of the major topic and the LCS (LCS). When a semantic pair of value, connected word (B), and LCS is formed, a depth value on WordNet calculated based on the corresponding semantic pair is calculated and a large value is extracted from the value can be said to be

For example, the similarity was calculated as 0.3 in the above description for the text (A) and the connected word (B) of the above-mentioned major topic, and the maximum depth of LCS for D was calculated as 5 , assuming that the input preference weight is 0.5,

is corrected with

That is, since the correction value tends to increase as the preference weight increases, in this case, it is preferable that variable control occur so that the size of the emphasizer 10 increases as the correction value increases.

In addition, the preference weight here is not basically reflected in a simple linear way, but through correction processing through hypersine, so that too large a value is not calculated. By reflecting and performing mutual comparison, there is an effect of preventing rapid changes in values according to preference weights, while simultaneously reflecting the Least Common Accommodator and preference weights so that the similarity can be corrected based on this.

As described so far, the configuration and operation of the rote learning system using keyword image substitution according to the present invention have been expressed in the above description and drawings, but this is only an example and the spirit of the present invention is limited to the above description and drawings. It is not, and of course, various changes and changes are possible within a range that does not deviate from the technical spirit of the present invention.

1: main server 2: writer terminal
3: Table of Contents Server 4: Application Server
10: Emphasizer 100: Word Library
200: image library 300: table of contents input module
310: subject filtering unit 320: corresponding group setting unit
330: word connection unit 400: table of contents corresponding module
410: candidate group calculation unit 420: matching group setting unit
500: table of contents composition module 510: page setting unit
520: arrangement setting unit 530: table of contents editing unit
540: Emphasizer generation unit 550: Similarity determination unit
551: LCS identification part 552: Similarity identification part
553: weight input part 554: correction value identification part
560: Emphasizer size control unit 600: Body editing module

Claims (9)

A table of contents memorization learning system using keyword image substitution,
a word library for storing a word group, which is a set of a plurality of words having at least one of a chronological relationship, a bias relationship, and an upper/lower relationship;
an image library for storing a corresponding image corresponding to the stored word;
a table of contents input module that receives a table of contents of a text including the main subject of the book;
A major subject filtering unit that compares and processes the input number of major subjects with the number of words belonging to each word group to filter word groups having the same number of words as the number of major subjects; and selects one of the filtered word groups as a corresponding group a table of contents corresponding module including a corresponding group setting unit configured to be set as ;
Completing the table of contents of a book by editing and arranging a word connected to the main subject, a corresponding image corresponding to the corresponding word, and the table of contents of the text. ) is generated and arranged in the vicinity of the major subject, a similarity determining unit that determines the similarity between the text and words of the connected major subject, and an empa generated for each major subject according to the identified similarity. A table of contents configuration module including an emphasizer size control unit that variably controls the size of the sizer; includes,
The similarity determination unit,
An LCS identification part that identifies the lowest common subsumer (LCS), which is a concept encompassing the text of the major topic and the word, which are connected on word-net; and
Including a similarity determination part for determining the similarity between the text and words of the connected major subject based on the least common conqueror,
The degree of similarity is
Characterized in that it is calculated through the following Equation 1, the table of contents memorization learning system.
Equation 1,

(Where, S is similarity, L is the distance between the text and words of the major topic connected on WordNet, and D is the maximum depth of LCS) According to claim 1,
The system,
A text editing module for additionally inserting the corresponding image on one side of at least one of the pages of a book containing contents related to each of the major topics; Table of Contents memorization learning system. According to claim 1,
The word library,
Includes a function of receiving and storing a subgroup, which is a set of a plurality of words having an upper and lower relationship with words belonging to the specific word group,
The table of contents of the text,
It includes a sub-topic that is a sub-category of the main topic,
The table of contents corresponding module,
a candidate group calculation unit for filtering subgroups having an upper-lower relationship with words belonging to the corresponding group among all word groups stored in the word library and designating them as candidate groups;
A matching group setting unit for setting one of the designated candidate groups as a matching group for the corresponding word;
The word connection part,
For each of the subtopics, one of the words belonging to the matching group is connected and processed,
The table of contents configuration module,
Characterized in that editing and arranging words connected to each of the main topic and the subtopic, a corresponding image corresponding to the corresponding word, and the table of contents of the text, a table of contents memorization learning system. According to claim 3,
The system,
A text editing module for displaying a corresponding image of a word connected to the sub-topic on one side of at least one of the pages containing content related to the main topic. According to claim 1,
The table of contents configuration module,
a page setting unit for determining the number of pages of the table of contents;
An arrangement setting unit for setting the size and arrangement position of the corresponding image;
and a table of contents editing unit for editing and constructing the corresponding image together with the table of contents of the text according to the criterion determined by the arrangement setting unit. According to claim 1,
The similarity determination unit,
A weight input part for receiving a preference weight for each word group input into the word library from an author terminal possessed by the author of the book;
A correction value identification part for calculating a correction value obtained by correcting the similarity based on the input preference weight,
The correction value is
Characterized in that it is calculated through the following Equation 2, the table of contents memorization learning system.
Equation 2,

(Where R is a correction value, S is similarity,

is the preference weight entered for the word group including the word associated with the text of a specific subject.

, D is the maximum depth of the LCS) delete delete delete

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