KR101504004B1 - Example Sentence Providing System, Terminal and Method based on Studied Words - Google Patents

Abstract

A system for providing example sentences based on learning words, a learner terminal and a method for providing the example sentences are disclosed. A learning word-based example sentence providing system according to the present invention comprises: a learner terminal connected through a network; An example sentence storage server that stores a plurality of example sentences corresponding to the word dictionary; A learning word storage server storing a learned word according to a learner corresponding to the learner terminal; An example sentence search server for searching for example sentences corresponding to a new word requested by the example sentence storage server when a sentence for a new word is requested from the learner terminal; Counting the number of words stored in the learning word storage server corresponding to each example sentence retrieved by the example retrieval server and sequentially arranging the respective illustrative sentences in order from the largest number of counted words, ; And an illustrative sentence server for transmitting illustrative sentences arranged by the search exemplar arrangement server to the learner terminal through a network.

Description

This paper proposes a system for providing example sentences based on learning vocabulary, a learner terminal, and a method for providing the example sentences.

The present invention relates to a learning word-based example sentence providing system, a learner terminal and a method of providing an example sentence. More specifically, the present invention relates to a learning word providing system, By providing the example sentences corresponding to the new words, it is possible to reduce the time and effort required for the user to search for words in the illustrative sentences, and to provide a learning word based A learner terminal, and a method of providing an example sentence.

Recently, due to the increase in the number of Internet users and the activation of information retrieval through the Internet, a web page written in a language other than a native language is executed more frequently than a web page written in a native language. Also, with the development of the Internet, the tools of language learning are gradually changing from offline to online.

As a result, users can use the online dictionary service to search for unknown words in web pages written in other languages or unknown words found during foreign language learning.

On the other hand, as described in Korean Patent Laid-Open Publication No. 10-2012-0070782 (2012. 07.02), the online dictionary service provides an example sentence about a word to be searched by a user to help the user understand it, .

However, in a case where a user includes a large number of words that have not yet been learned or understood by an example sentence presented by an online dictionary service, it takes another time and effort to search for new words included in the example sentence.

In addition, when an example sentence presented by an online dictionary service contains many words that the user has not yet learned or understood, there is a problem in that it is difficult to immediately understand the usage of the word because the example sentence is not understood.

Korean Patent Publication No. 10-2012-0070782A, 2012. 07. 02, pp. 8-9.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide a method and apparatus for providing a user with a dictionary, Providing a sample word providing system based on a learning word, a learner terminal, and a method of providing an example sentence, which can reduce the time and effort required for searching for words in the illustrative sentences, .

According to another aspect of the present invention, there is provided a system for providing a sample word based on a learning word, the system comprising: a learner terminal connected through a network; An example sentence storage server that stores a plurality of example sentences corresponding to the word dictionary; A learning word storage server for storing information on learned words according to a learner corresponding to the learner terminal; An example sentence search server for searching for example sentences corresponding to a new word requested by the example sentence storage server when a sentence for a new word is requested from the learner terminal; Counting the number of words stored in the learning word storage server corresponding to each example sentence retrieved by the example retrieval server and sequentially arranging the respective illustrative sentences in order from the largest number of counted words, ; And an illustrative sentence server for transmitting illustrative sentences arranged by the search exemplar arrangement server to the learner terminal through a network.

The learning word-based example sentence providing system may further include a learning information storage server that stores learning information including at least one of the number of times learned and the learned date corresponding to each word stored in the learning word storage server . In this case, if the number of words to be counted is the same for each example sentence, the search example server arranges illustrative sentences based on the learning information stored in the learning information storage server.

The above-described learning word-based example sentence providing system may further include a gauge learning degree calculation server that calculates a gauge learning degree for each example sentence retrieved by the example query server. In this case, the retrieval example server arranges illustrative sentences based on a pre-learning diagram calculated by the pre-learning degree calculation server.

The pre-learning degree calculator can calculate the pre-learning degree for each word based on the following equation.

Here, i represents a word index, α represents a value representing the importance of each word constituting the sentence, β represents a normalized value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word.

Wherein the preliminary learning degree calculation burr includes at least one of words stored in the learning word storage server in consideration of at least one of a learning time, a learning frequency, a test evaluation score, a derivation type of a learning word, Can be set to a high weight.

According to another aspect of the present invention, there is provided a learner terminal capable of installing and executing a program, the learner terminal including: a sample sentence storage unit for storing a plurality of example sentences corresponding to a word dictionary; A learning word storage unit for storing a word learned by a learner; An example sentence searching unit for searching for example sentences corresponding to the new words requested in the sentence storing unit when the learner requests a sentence for a new word; A search example arrangement unit for counting the number of words stored in the learning word storage unit corresponding to each example sentences retrieved by the example sentence retrieval unit and sequentially arranging the respective illustrative sentences in order from the largest number of counted words, ; And an example sentence display unit for displaying example sentences arranged by the search example sentence arrangement unit on a display.

The learner terminal may further include a learning information storage unit for storing learning information including at least one of a number of times learned corresponding to each word stored in the learning word storage unit and a learned date. In this case, if the number of words counted for each of the illustrative sentences is the same, the retrieval example arranging unit arranges illustrative sentences based on the learning information stored in the learning information storage unit.

The learner terminal may further include a learner degree calculator for calculating a learner degree for each example sentences retrieved by the illustrative sentence retrieval unit. In this case, the retrieval example arranging unit arranges illustrative sentences based on the initial learning degree calculated by the initial learning degree calculating unit.

The pre-learning degree calculator calculates a pre-learning degree for each word based on the following equation.

Here, i represents a word index, α represents a value representing the importance of each word constituting the sentence, β represents a normalized value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word.

The pre-learning degree calculator calculates a weighted value by assigning a high weight to a word having a high importance in consideration of at least one of the learning time, the number of learning times, the test evaluation score, the derivation type of the learning word, Can be set.

According to another aspect of the present invention, there is provided a method of providing an example sentence based on a learning word, the method comprising: storing a plurality of example sentences corresponding to a word dictionary; step; Storing information on a word learned by a learner; Retrieving example sentences corresponding to the new word from the stored examples stored in the example sentence storing step when the learner requests the example sentence for the new word; Counting the number of words stored by the learning word storage step corresponding to each example sentence retrieved by the example sentence retrieving step; Arranging each example sentence retrieved by the example sentence retrieving step sequentially from the order in which the number of words to be counted is the largest; And displaying the example sentences arranged by the search sample phrase step on the display.

The method of providing an example sentence based on the learning word may further include storing learning information including at least one of the number of times learned corresponding to each word stored by the learning word storing step and the learned date . In this case, if the number of words counted for each of the illustrative sentences is equal, the retrieval example arrangement step arranges illustrative sentences based on the learning information stored by the learning information storage step.

The method of providing the example sentence based on the learning word may further include calculating a pre-learned degree of each example sentences retrieved by the example sentence retrieval step. In this case, the retrieval example arranging step arranges illustrative sentences based on the initial learning degree calculated by the initial learning degree calculating step.

The pre-learning degree calculation step calculates a pre-learning degree for each of the words based on the following equation.

Here, i represents a word index, α represents a value representing the importance of each word constituting the sentence, β represents a normalized value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word.

Wherein the step of calculating the degree of learning includes assigning a high weight to a word having a high importance in consideration of at least one of a learning time, a learning number, a test evaluation score, a derivation type of a learning word, can be set.

According to the present invention, when a user requests an illustrative sentence of a new word while learning a foreign language, the user firstly presents the most understandable illustrative sentences according to the language ability and the level of the user, It is possible to learn an optimal example sentence with respect to a new word without inconvenience of searching, and thus the efficiency of language learning of the user can be greatly improved.

1 is a diagram schematically illustrating a system for providing a sample word based on a learning word according to an embodiment of the present invention.
FIG. 2 is a diagram for explaining an example sentence structure corresponding to a new word according to an embodiment of the present invention.
3 is a view schematically showing a learner terminal according to an embodiment of the present invention.
4 is a flowchart illustrating a method of providing a sample word based on a learning word according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, a detailed description of known techniques well known to those skilled in the art may be omitted.

In describing the constituent elements of the present invention, the same reference numerals may be given to constituent elements having the same name, and the same reference numerals may be given thereto even though they are different from each other. However, even in such a case, it does not mean that the corresponding component has different functions according to the embodiment, or does not mean that the different components have the same function. It should be judged based on the description of each component in the example.

In the following description of the embodiments of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In describing the components of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. When a component is described as being "connected", "coupled", or "connected" to another component, the component may be directly connected or connected to the other component, Quot; may be "connected," "coupled," or "connected. &Quot;

1 is a diagram schematically illustrating a system for providing a sample word based on a learning word according to an embodiment of the present invention.

Referring to FIG. 1, a learning word-based example sentence providing system includes a learner terminal 100 and an example sentence providing apparatus 200. Here, the learner terminal 100 and the illustrative sentence providing apparatus 200 may be connected to each other through a network. The illustrative sentence providing apparatus 200 includes an illustrative sentence storage server 210, a learned word storage server 220, an illustrative sentence search server 230, a search illustrative sentence server 240, an illustrative sentence server 250, (260) and a pre-learning degree calculation server (270).

The learner terminal 100 can access the illustrative sentence providing apparatus 200 via the network. At this time, the learner terminal 100 includes a mobile communication terminal such as a mobile phone or a smart phone, which is connectable to a network through wireless communication or mobile communication, as well as a computer connectable to the network through wired communication.

The example sentence storage server 210 stores a plurality of example sentences corresponding to the word dictionary. For example, the illustrative sentence storage server 210 may store a plurality of illustrative sentences corresponding to respective words in a foreign language dictionary such as English, Japanese, German, and the like.

The learning word storage server 220 stores information on learned words according to the learner corresponding to the learner terminal 100. [ That is, when the learner logs in and accesses through the learner terminal 100, the learning word storage server 220 stores the words that have searched for meaning, example sentences, etc. according to the connected learner.

When the illustrative sentence search server 230 receives an example sentence for a new word from the learner terminal 100, the illustrative sentence search server 230 searches the illustrative sentence storage server 210 for illustrative sentences corresponding to the requested new word. That is, when the learner requests the illustrative sentence of a new word to be learned through the learner terminal 100, the illustrative sentence search server 230 searches the illustrative sentence storage server 210 for illustrative sentences of the new word.

The search example server 240 counts the number of words stored in the learning word storage server 220 corresponding to each example sentence retrieved by the example search server 230, and calculates the number of words to be counted Arrange sequentially from the largest number of orders. For example, as shown in FIG. 2, if the learner has already learned the words A, B, C, D, and E and requested a new word to learn a word F that is not known, You can search for examples P1, P2, P3, P4, P5, etc. for new words. At this time, the search example server 240 counts the number of words already learned by the learner for each of the example sentences P 1, P 2, P 3, P 4, P 5 retrieved by the example search server 230. In this case, the example sentence P1 has two words learned by the learner, four illustrative sentences P2, three illustrative sentences P3, one illustrative sentence P4, and five illustrative sentences P5. The search example server 240 sequentially rearranges the order of the examples retrieved by the example search server 230 according to the number of learned words of each example sentences.

The illustrative sentence server 250 transmits illustrative sentences arranged by the search exemplar arrangement server 240 to the learner terminal 100 via the network. As a result, since the learner can check the illustrative sentences including many words he knows, it is possible to reduce the troubles of searching for unknown words for each illustrative sentence, .

The learning information storage server 260 includes at least one of the learned learning time, the number of learning times, the test evaluation score, and the derived learning date of the learning word corresponding to each word stored in the learning word storage server 220 And stores the learning information. In this case, the search example server 240 may arrange illustrative sentences based on the learning information stored in the learning information storage server 260 when the number of words counted for each example sentence is the same. In other words, the learner may forget the meaning of the word, the example sentence, and the like again after a predetermined period even if the learned word is already learned. In this case, the learning information storage server 260 stores the learned word in the learning word storage server 220 The number of times of learning corresponding to each word, the most recently learned date, and the like can be stored as learning information. At this time, the search example server 240 includes an example sentence including a word having a large number of learned words when the number of learned words is equal to each example sentence corresponding to a new word, Can be arranged in priority.

The pre-learning degree calculation server 270 calculates a pre-learning degree for each example sentences retrieved by the example search server 230. [ In this case, the search example server 240 arranges illustrative sentences based on the pre-learning diagram calculated by the pre-learning degree calculation server 270.

At this time, the base learning degree calculation server 270 can calculate the base degree of learning for each of the words based on the following equation.

Here, i represents a word index, α represents a value representing the importance of each word constituting the sentence, β represents a normalized value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word. In this case, the base learning degree calculation server 270 can assign a high weight to a word likely to be forgotten among the words stored in the learning word storage server 220 to set?. A word with high forgettingness can be set statistically in consideration of the number of characters constituting the word, the meaning of the word, and the number of times retrieved by the learners. Also, the initial learning degree calculation server 270 can set a to a low weight value of a word of a part-of-speech having a relatively small determination power in terms of sentence understanding among words stored in the learning word storage server 220. For example, α can be set by assigning weights in such a manner that the lowest weight is given to the conjunction and the second lowest weight is given to the preposition. In this way, P has a value between 0 and 1 according to the sentence, and the higher the value, the higher the probability that the sentence will be important and helpful to learners.

3 is a view schematically showing a learner terminal according to an embodiment of the present invention.

3, the learner terminal 100 includes an example sentence storage unit 110, a learned word storage unit 120, an example sentence searching unit 130, a search example arrangement unit 140, an illustrative sentence display unit 150, The storage unit 160, and the initial learning degree calculation unit 170.

The example sentence storage unit 110 stores a plurality of example sentences corresponding to the word dictionary. For example, the illustrative sentence storage unit 110 may store a plurality of illustrative sentences corresponding to respective words in a foreign language dictionary such as English, Japanese, German, and the like.

The learning word storage unit 120 stores a word learned by the learner. That is, the learning word storage unit 120 stores the learned words when the learner learns the meaning of words, examples, and the like through the learner terminal 100.

The example sentence searching unit 130 searches the example sentence storage unit 110 for sentences corresponding to the new words requested by the learner. That is, when the learner requests the illustrative sentence of the new word to be learned through the learner terminal 100, the illustrative sentence searching unit 130 searches the illustrative sentence storage unit 110 for illustrative sentences of the new word.

The retrieval example arrangement unit 140 counts the number of words stored in the learning word storage unit 220 corresponding to each example sentences retrieved by the example retrieval unit 130. Each example sentence counts the number of words to be counted Arrange sequentially from the largest number of orders.

The illustrative sentence display unit 150 sequentially displays illustrative sentences arranged by the example sentence arrangement unit 140 on the display. As a result, since the learner can check the illustrative sentences including many words he knows, it is possible to reduce the troubles of searching for unknown words for each illustrative sentence, .

The learning information storage unit 160 stores learning information including at least one of the number of learning times and the learning dates corresponding to the respective words stored in the learning word storage unit 120. [ In this case, if the number of words to be counted is the same for each example sentence, the example sentence arrangement unit 140 can arrange illustrative sentences based on the learning information stored in the learning information storage unit 160. [

The pre-learning degree calculator 170 calculates a pre-learning degree for each illustrative sentence retrieved by the illustrative sentence retrieval unit 130. [ In this case, the search example arrangement unit 140 arranges illustrative sentences on the basis of the degree-of-learning diagram calculated by the initial degree-of-learning degree calculation unit 170. [

At this time, the initial learning degree calculator 170 can calculate the initial learning degree for each of the words based on the following equation.

Here, i represents a word index, α represents a value representing the importance of each word constituting the sentence, β represents a normalized value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word. In this case, the initial learning degree calculator 170 can assign a high weight to a word likely to be forgotten among the words stored in the learning word storage unit 120 to set?. Also, the initial learning degree calculator 170 can set a to a low weight value of a word of a part-of-speech having a relatively small determination power in understanding the sentence among the words stored in the learning word storage unit 120. [

4 is a flowchart illustrating a method of providing a sample word based on a learning word according to an embodiment of the present invention. The method of providing the example sentence based on the learning word may be executed by the example sentence providing device 200 of FIG. 1 or may be executed by the learner terminal 100 of FIG. It is assumed here that it is executed by the learner terminal 100. [

1 to 4, the example sentence storage unit 110 stores a plurality of example sentences corresponding to the word dictionary (S110). For example, the illustrative sentence storage unit 110 may store a plurality of illustrative sentences corresponding to respective words in a foreign language dictionary such as English, Japanese, German, and the like.

The learning word storage unit 120 stores the learned words by the learner (S120). That is, the learning word storage unit 120 stores the learned words when the learner learns the meaning of words, examples, and the like through the learner terminal 100.

At this time, the learning information storage unit 160 may store the learning information including at least one of the number of times learned and the learning date corresponding to each word stored in the learning word storage unit 120 (S130).

The example sentence searching unit 130 searches for example sentences corresponding to the new words requested in the sentence storing unit 110 (S140). That is, when the learner requests the illustrative sentence of the new word to be learned through the learner terminal 100, the illustrative sentence searching unit 130 searches the illustrative sentence storage unit 110 for illustrative sentences of the new word.

The first learning degree calculator 170 may calculate a first degree of learning for each example sentence retrieved by the example retrieval unit 130 (S150). At this time, the initial learning degree calculator 170 can calculate the initial learning degree for each of the words based on the following equation.

Here, i represents a word index, α represents a value representing the importance of each word constituting the sentence, β represents a normalized value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word. In this case, the initial learning degree calculator 170 calculates the initial learning degree of the words having high importance in consideration of at least one of the learning time, the number of learning times, the test evaluation score, the derivation type of the learning word, Can be set to a high weight. Also, the initial learning degree calculation server 270 can set a to a low weight value of a word of a part-of-speech having a relatively small determination power in terms of sentence understanding among words stored in the learning word storage server 220.

The retrieval example arrangement unit 140 counts the number of words stored in the learning word storage unit 220 corresponding to each example sentences retrieved by the example retrieval unit 130 (S160) The words are arranged in order from the largest number of words (S170). At this time, the search example arrangement unit 140 can arrange illustrative sentences based on the learning information stored in the learning information storage unit 160 when the number of words counted for each example is the same. In addition, the search example arrangement unit 140 can arrange the illustrative sentences based on the initial learning degree calculated by the initial learning degree calculation unit 170. [

The illustrative sentence display unit 150 sequentially displays illustrative sentences arranged by the example sentence arrangement unit 140 on the display (S180). As a result, since the learner can check the illustrative sentences including many words he knows, it is possible to reduce the troubles of searching for unknown words for each illustrative sentence, .

The present invention is not necessarily limited to these embodiments, as all the constituent elements constituting the embodiment of the present invention are described as being combined or operated in one operation. That is, within the scope of the present invention, all of the components may be selectively coupled to one or more of them. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. In addition, such a computer program may be stored in a computer-readable medium such as a USB memory, a CD disk, a flash memory, etc., and read and executed by a computer, thereby implementing embodiments of the present invention. As the storage medium of the computer program, a magnetic recording medium, an optical recording medium, a carrier wave medium, or the like may be included.

Furthermore, all terms including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined in the Detailed Description. Commonly used terms, such as predefined terms, should be interpreted to be consistent with the contextual meanings of the related art, and are not to be construed as ideal or overly formal, unless expressly defined to the contrary.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. In addition, the embodiments disclosed in the present invention are not intended to limit the scope of the present invention but to limit the scope of the technical idea of the present invention. Accordingly, the scope of protection of the present invention should be construed according to the claims, and all technical ideas within the scope of equivalents should be interpreted as being included in the scope of the present invention.

Claims (15)

A learner terminal connected through a network;
An example sentence storage server that stores a plurality of example sentences corresponding to the word dictionary;
A learning word storage server for storing information on learned words according to a learner corresponding to the learner terminal;
An example sentence search server for searching for example sentences corresponding to a new word requested by the example sentence storage server when a sentence for a new word is requested from the learner terminal;
Counting the number of words stored in the learning word storage server corresponding to each example sentence retrieved by the example retrieval server and sequentially arranging the respective illustrative sentences in order from the largest number of counted words, And
An illustrative sentence server for transmitting the illustrative sentences arranged by the search exemplar arrangement server to the learner terminal through a network,
And a gauge learning degree calculation server for calculating a gauge learning degree for each example sentences retrieved by the example query server,
Wherein the search example server arranges illustrative sentences based on a pre-learning degree calculated by the pre-learning degree calculation server,
Wherein the base learning degree calculation burr calculates a base degree of learning for each word based on the following equation, and calculates a learning degree of each word stored in the learning word storage server based on learning time, learning frequency, test evaluation score, , And part-of-speech, and assigns a weight according to the importance information set for each word to set?.

Here, i represents a word index, a represents a degree of importance of each word constituting the sentence, β represents a normalization value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word.
The method according to claim 1,
A learning information storage server for storing learning information including at least one of a learned number of times corresponding to each word stored in the learning word storage server and a learned date,
Further comprising:
Wherein the search example server arranges the illustrative sentences based on the learning information stored in the learning information storage server when the number of words counted for each illustrative sentence is the same.
delete delete delete A learner terminal capable of installing and executing a program,
An example sentence storage unit for storing a plurality of example sentences corresponding to the word dictionary;
A learning word storage unit for storing information on words learned by the learner;
An example sentence searching unit for searching for example sentences corresponding to the new words requested in the sentence storing unit when the learner requests a sentence for a new word;
A search example arrangement unit for counting the number of words stored in the learning word storage unit corresponding to each example sentences retrieved by the example sentence retrieval unit and sequentially arranging the respective illustrative sentences in order from the largest number of counted words, ;
An example sentence display unit for displaying example sentences arranged by the search example sentence arrangement unit on a display,
And a gauge learning degree calculator for calculating a gauge learning degree for each example sentence retrieved by the example sentence retrieving unit,
The search example arrangement unit arranges illustrative sentences based on a preliminary learning degree calculated by the preliminary learning degree calculation unit,
Wherein the first learning degree calculation unit calculates a first degree of learning for each of the words on the basis of the following equation, and calculates a learning degree of the words stored in the learning word storage unit, such as learning time, learning frequency, test evaluation score, , And part-of-speech, and assigns weights according to importance information set for each word to set?.

Here, i represents a word index, a represents a degree of importance of each word constituting the sentence, β represents a normalization value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word.
The method according to claim 6,
A learning information storage unit for storing learning information including at least one of a number of times learned corresponding to each word stored in the learning word storage unit,
Further comprising:
Wherein the search example arrangement unit arranges illustrative sentences based on the learning information stored in the learning information storage unit when the number of words counted for each illustrative sentence is the same.
delete delete delete A method of providing a sample sentence based on a learning word executed by a learner terminal,
Storing a plurality of example sentences corresponding to the word dictionary;
Storing information on a word learned by a learner;
Retrieving example sentences corresponding to the new word from the stored examples stored in the example sentence storing step when the learner requests the example sentence for the new word;
Counting the number of words stored by the learning word storage step corresponding to each example sentence retrieved by the example sentence retrieving step;
Arranging each example sentence retrieved by the example sentence retrieving step sequentially from the order in which the number of words to be counted is the largest; And
Displaying the example sentences arranged by the search example sentence step on the display,
And calculating a pre-learning degree for each example sentence retrieved by the example sentence retrieving step,
The retrieval example arrangement step arranges the illustrative sentences based on the initial learning degree calculated by the initial learning degree calculation step,
Wherein the first learning degree calculating step calculates a first learning degree for each of the words based on the following equation, and the learning time of the words stored by the learning word storing step, the number of learning times, the test evaluation score, Derived form, and part-of-speech, and assigns weights according to the importance information set for each word to set?.

Here, i represents a word index, a represents a degree of importance of each word constituting the sentence, β represents a normalization value according to the length of the sentence, and N represents the total number of words constituting the sentence except for the requested new word.
12. The method of claim 11,
Storing learning information including at least one of a number of times learned corresponding to each word stored by the learning word storing step and a learning date,
Further comprising:
Wherein the retrieval example arrangement step arranges illustrative sentences based on the learning information stored by the learning information storage step when the number of words counted for each example sentence is the same.





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