JP5317093B2 - Learning content generation device, learning content generation method, program for causing a computer to function as the learning content generation device, and computer-readable recording medium storing the program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To easily generate questions for language study. <P>SOLUTION: A learning content generator 200 comprises an input part 210 for accepting operation, an output part 220 for outputting content, a control part 230, a memory part 240, a program module part 250 and a main memory part 270. The memory part 240 includes a bilingual example and bilingual template database, English dictionary data, Japanese dictionary data, English thesaurus data, Japanese thesaurus data 245, and templates thereof. The program module part 250 includes a bilingual example and bilingual template selecting part, a rearranging question generating part, a fill-in-the-blank question generating part, an error pointing out question generating part, an English grammar processing part, a Japanese grammar processing part, a random number generating part, a random number normalization part, a conjugation searching part, a synonym searching part, and a dictionary consulting part. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to information processing, and more particularly to a technique for generating a problem sentence for learning based on a sentence prepared in advance.

  An information processing apparatus such as an electronic dictionary or a mobile phone has a new function added to the original function such as a dictionary function or a telephone function. For example, the electronic dictionary has a pronunciation function. In addition, the mobile phone has a dictionary function, a document editing function, and the like. The information processing apparatus also functions as a learning support apparatus in addition to such usage as the original information processing function. For example, there is a specification mode in which a problem created in advance is stored in a mobile phone, and a user of the mobile phone answers a question to be asked.

  For example, Japanese Patent Application Laid-Open No. 2003-195733 (Patent Document 1) discloses an education system and an education method that can achieve language learning as well as achieve a learning goal of the subject.

Japanese Patent Application Laid-Open No. 2007-072594 (Patent Document 2) discloses a translation apparatus and a translation method for translating a correct translation into an original word / phrase.
JP 2003-195733 A JP 2007-072594 A

  However, in the configuration in which learning content such as a question sentence is stored in advance in the information processing apparatus, the question to be asked is limited to a problem created in advance, and there is a limit to variations in the question pattern.

  The present invention has been made to solve the above-described problems, and an object thereof is to provide a learning content generation apparatus that can easily generate a problem.

Another object is to provide a learning content generation method that can easily generate a problem.
Another object is to provide a program for causing a computer to function as a learning content generation apparatus that can easily generate a problem.

  Still another object is to provide a recording medium storing a program for causing a computer to function as a learning content generation apparatus that can easily generate a problem.

  A learning content generation device according to an aspect of the present invention includes a display unit that displays a screen, a storage unit that stores an example sentence composed of a plurality of words in a first language, and a control unit that controls the learning content generation device. Is provided. The control means generates a question sentence different from the example sentence based on any one of the plurality of words, and causes the display means to display the question sentence.

  Preferably, the control unit generates a question sentence different from the word arrangement in the example sentence by changing the arrangement of the plurality of words.

  Preferably, the storage means stores a dictionary of words in the first language. The control means randomly selects a word from the dictionary, and generates a question sentence in which the arrangement of words in the word group including the selected word is changed to a plurality of words.

Preferably, the dictionary includes a synonym dictionary.
Preferably, the dictionary includes a variation obtained by changing a word of the first language according to a grammatical rule of the language. The control means generates a question sentence by including any variation of the plurality of words in the modified array of the plurality of words.

  Preferably, the storage means stores a dictionary of words in the first language. The control means selects a first word from a plurality of words, selects a plurality of candidate words from a dictionary, and a series of remaining words excluding the first word from a plurality of words constituting an example sentence A question sentence is generated based on the word, the display for indicating the deletion of the first word, and the plurality of candidate words.

  Preferably, the dictionary includes a synonym dictionary. The control means selects a plurality of synonyms of the first word as the plurality of candidate words.

  Preferably, the dictionary includes a variation obtained by changing a word of the first language according to a grammatical rule of the language. The control means selects any variation of the plurality of words as the plurality of candidate words.

  Preferably, the storage means stores a dictionary of words in the first language. The control means selects a first word from the plurality of words, selects a second word different from the first word from the dictionary, and removes the first word from the plurality of words, A question sentence is generated by randomly arranging two words.

  Preferably, the dictionary includes a variation obtained by changing a word of the first language according to a grammatical rule of the language. The control means selects a variation of the first word as the second word from the dictionary.

  Preferably, the dictionary includes a synonym dictionary. The control means selects a synonym of the first word as the second word.

  Preferably, the storage unit stores a translated sentence of the example sentence in the first language in the second language. The control means causes the display means to display the problem sentence in the first language and the translated sentence in the second language.

  When the other situation of this invention is followed, the production | generation method of the learning content by a computer is provided. The computer includes a processor, a memory, and a display device. The method includes a step in which a processor loads an example sentence composed of a plurality of words in a first language from a memory, and the processor is different from the example sentence based on any one of the plurality of words. Generating a sentence; and causing the processor to display the question sentence on a display.

  If the other situation of this invention is followed, the program for functioning a computer as a learning content production | generation apparatus will be provided. The computer includes a processor, a memory, and a display device. The program loads the processor with an example sentence composed of a plurality of words in the first language from the memory, and generates a question sentence different from the example sentence based on any one of the plurality of words. A step and a step of displaying a question sentence on a display are executed.

  According to still another aspect of the present invention, a computer-readable recording medium storing the above program is provided.

  According to the present invention, problems can be easily generated.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description, the same parts are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.

[Hardware configuration]
With reference to FIG. 1, a specific configuration of the learning content generation apparatus according to the embodiment of the present invention will be described. FIG. 1 is a block diagram showing a hardware configuration of a computer system 100 that realizes a learning content generation apparatus according to the present embodiment. In addition to the computer system 100, the learning content generation apparatus can be realized by a mobile phone, a dictionary, a PDA (Personal Digital Assistant), or the like.

  The computer system 100 includes, as main components, a CPU (Central Processing Unit) 110 that executes a program, a mouse 120 and a keyboard 130 that receive input of instructions to the computer system 100, data generated by execution of the program by the CPU 110, Alternatively, a RAM (Random Access Memory) 140 that stores data input via the mouse 120 or the keyboard 130 in a volatile manner, a hard disk 150 that stores data in a nonvolatile manner, an optical disk drive device 160, and a monitor 110 communicate with each other. I / F (Interface) 190. Each component is connected to each other by a data bus. A CD-ROM (Compact Disc-Read Only Memory) 162 and other optical disks are mounted on the optical disk drive 160.

  Processing in the computer system 100 is realized by each hardware and software executed by the CPU 110. Such software may be stored in the hard disk 150 in advance. The software may be stored in a CD-ROM 162 or other storage medium and distributed as a program product. Alternatively, the software may be provided as a program product that can be downloaded by an information provider connected to the so-called Internet. Such software is read from the storage medium by the optical disk drive 160 or other reading device, or downloaded via the communication I / F 190 and then temporarily stored in the hard disk 150. The software is read from the hard disk 150 by the CPU 110 and stored in the RAM 140 in the form of an executable program. CPU 110 executes the program.

  Each component constituting the computer system 100 shown in FIG. 1 is a general component. Therefore, it can be said that the essential part of the present invention is software stored in the RAM 140, the hard disk 150, the CD-ROM 162, or other storage medium, or software that can be downloaded via a network. Since the operation of each hardware of computer system 100 is well known, detailed description will not be repeated.

  The recording medium is not limited to a CD-ROM, FD (Flexible Disk), and hard disk, but is a magnetic tape, a cassette tape, an optical disk (MO (Magnetic Optical Disc) / MD (Mini Disc) / DVD (Digital Versatile Disc)). IC (Integrated Circuit) card (including memory card), optical card, mask ROM, EPROM (Electronically Erasable Programmable Read-Only Memory), EEPROM (Electronically Erasable Programmable Read-Only Memory), fixing of flash ROM and other semiconductor memories Alternatively, it may be a medium carrying a program.

  The program here includes not only a program directly executable by the CPU but also a program in a source program format, a compressed program, an encrypted program, and the like.

[Function configuration]
With reference to FIG. 2, the configuration of learning content generation apparatus 200 according to the embodiment of the present invention will be described. FIG. 2 is a block diagram showing a configuration of functions realized by learning content generation apparatus 200. The learning content generation apparatus 200 includes an input unit 210, an output unit 220, a control unit 230, a storage unit 240, a program module unit 250, and a main memory unit 270.

  The input unit 210 receives an operation input for the learning content generation apparatus 200. The input unit 210 sends a signal corresponding to the operation to the control unit 230. The input unit 210 is realized as a keyboard, buttons, or other input switches, or as a touch panel. In another aspect, the input unit 210 is configured using a piezoelectric sensor.

  The output unit 220 outputs information to the outside of the learning content generation device 200 based on control by the control unit 230. In an aspect, the output unit 220 is realized as a display device. In another aspect, output unit 220 can be realized as a speaker that outputs sound.

  The control unit 230 controls the operation of the learning content generation device 200. In one aspect, the control unit 230 is realized by a processor, an FPGA (Field Programmable Gate Array), or the like. Based on the signal sent from the input unit 210, the control unit 230 converts the data stored in the storage unit 240, the program module stored in the program module unit 250, or the data held in the main memory unit 270. Based on this, processing for realizing the function of the learning content generation apparatus 200 is executed. For example, the control unit 230 outputs an English sorting problem based on an operation on the input unit 210. In another aspect, the control unit 230 causes the output unit 220 to display English and Japanese sentences.

  The storage unit 240 includes a bilingual example sentence database 241, an English dictionary data 242, a Japanese dictionary data 243, an English thesaurus data 244, and a Japanese thesaurus data 245. The storage unit 240 further includes a rearrangement problem generation template 246, a hole filling problem generation template 247, and an error indication problem generation template 248. The storage unit 240 is realized by a flash memory, a hard disk, or other nonvolatile memory. In another aspect, the storage unit 240 may be realized by a removable recording medium.

  The program module unit 250 includes a bilingual example sentence selection unit 251, a rearrangement problem generation unit 252, a hole filling problem generation unit 253, an error indication problem generation unit 254, an English grammar processing unit 255, and a Japanese grammar processing unit 256. , Random number generation unit 257, random number normalization unit 258, variation search unit 259, synonym search unit 260, and dictionary lookup unit 261. These are realized as a program realized by a processor in a certain aspect. The program module unit 250 is realized on a flash memory, a hard disk, a ROM, or other nonvolatile recording medium. The program module unit 250 and the storage unit 240 are implemented in physically the same or different storage devices.

  The bilingual example sentence selecting unit 251 is configured to select a bilingual example sentence from the storage unit 240 in accordance with an instruction from the control unit 230. For example, the parallel translation example sentence selection unit 251 randomly selects a parallel translation example sentence. Alternatively, in another aspect, the bilingual example sentence selecting unit 251 may be configured to select an example sentence having a low correct answer rate of the question.

  The rearrangement problem generation unit 252 uses the rearrangement problem generation template 246 and the example sentences and translations selected from the bilingual example sentence database 241 in accordance with instructions from the control unit 230 to determine the word order of a plurality of words constituting a regular sentence. Generate a sorted problem.

  The filling-in-place problem generation unit 253 uses the filling-in-place problem generation template 247 and the example sentences and translations selected from the bilingual example sentence database 241 in accordance with the instruction from the control unit 230 as a filling point problem. Generate.

  The error indication problem generation unit 254 uses the error indication problem generation template 248 and the example sentence and the translation selected from the bilingual example sentence database 241 in accordance with the instruction of the control unit 230 to form any word that forms a regular sentence. Generate a sentence with a change as an error indication problem.

  The English grammar processing unit 255 refers to the English dictionary data 242 and identifies the original word from the word change forms (past form, past participle form, present participle form, plural form, comparative grade, superlative level, etc.).

  The Japanese grammar processing unit 256 refers to the Japanese dictionary data 243 and constructs a variation form (a verb, an adjective, an adjective verb utilization form, etc.) from the original form of a Japanese word. In another aspect, the Japanese grammar processing unit 256 identifies the original form from the changed form.

  The random number generator 257 generates a numerical value within a continuous interval between 0 and 1 with an equal probability within this interval.

  The random number normalization unit 258 converts the random number generated by the random number generation unit 257 into a numerical value (for example, an integer) suitable for the purpose. In the present embodiment, for example, it is assumed that the number of data records constituting the bilingual example sentence database 241 included in the bilingual example sentence database in the range to be used is 500 sets. And it is assumed that 1 to 500 are assigned to each set by serial numbers. In this case, the output from the random number normalization unit 258 is a continuous numerical value between the random numbers (0 to 1) generated by the random number generation unit 257 and any integer between 1 and 500 (with an equal probability). Will be converted to a

  In another aspect, random number generation unit 257 and random number normalization unit 258 may function as a unit, for example, as a random number generation unit. In the above example, the random number generation unit according to another aspect, for example, suddenly generates any integer between 1 and 500 with equal probability.

  The variation search unit 259 refers to the English dictionary data 242 and acquires information (original form, variation, etc.) of the word designated by the control unit 230. For example, in the case of a verb, search for “original form”, “third-person singular singular form”, “past form”, “past participle form”, and “ing form” are searched for variations including the original form.

  The synonym search unit 260 refers to the English thesaurus data 244 and searches for synonyms of the word specified by the control unit 230. In another aspect, the synonym search unit 260 refers to the Japanese thesaurus data 245 and searches for synonyms of the word specified by the control unit 230.

  The dictionary lookup unit 261 refers to the English dictionary data 242 or the Japanese dictionary data 243 designated for the search based on the output from the variation search unit 259 or the synonym search unit 260.

  Referring again to FIG. 2, the main memory unit 270 includes a sentence counter 271, a word counter 272, a bilingual example sentence buffer 273, an original sentence word buffer 274, a temporary word buffer 275, and a word order change result buffer 276. , A filling option buffer 277, an error question buffer 278, a question sentence buffer 279, and a replaced word count counter 280. In one aspect, the main memory unit 270 is realized as a RAM or other volatile memory.

[data structure]
With reference to FIGS. 3 to 7, the data structure of learning content generation apparatus 200 according to the present embodiment will be described. FIG. 3 is a diagram conceptually showing one mode of data storage in the bilingual example sentence database 241.
The bilingual example sentence database 241 includes a plurality of example sentences. Each example sentence includes a pair of parallel example sentences (in two languages such as English and Japanese). The language of the example sentence is not limited to Japanese and English, and may be Chinese, Korean, French, German or other languages.

  More specifically, the bilingual example sentence database 241 includes English example sentence data 310, Japanese example sentence data 320, alignment data 330, and a temporary flag 350.

  The English example sentence data 310 includes a plurality of English example sentences. Each example sentence includes ID (Identification), a word constituting the example sentence, a part of speech of the word, and a variation of the word.

  The bilingual example sentence database 241 includes a plurality of Japanese example sentence data 320. Each Japanese example sentence data includes an ID, a word constituting the example sentence, a part of speech of the word, and a variation of the word.

  The alignment data 330 defines the correspondence between the English example sentence and the Japanese example sentence. In the alignment data 330, an ID is assigned to each group in which a correspondence relationship between English and Japanese can be defined based on a word constituting an English example sentence and a word constituting a Japanese example sentence. In addition, the numbers of the words constituting the group are associated as English word numbers and Japanese word numbers, respectively.

  The temporary flag 350 holds data indicating whether or not the example sentence template has been used. The temporary flag 350 can be rewritten based on the control of the control unit 230 and can be initialized based on a reset command.

  FIG. 4 is a diagram conceptually showing the configuration of English dictionary data 242. The English word dictionary data 242 includes an ID, a headline, a part of speech, a variation, and data indicating meaning. The data indicating the meaning includes a semantic category, a semantic code, and a translated word. When the English word has a plurality of meanings, a plurality of items of data indicating the meanings are defined according to each meaning.

  In the example shown in FIG. 4, the full spelling of the change form (in the case of a verb “third-person singular present form”, “past form”, “past participle form”, “ing form”) is described in the dictionary data. In another aspect, when a word is configured to change according to a rule, only a certain change code (a code indicating the rule) may be described and a change form may be obtained based on the change rule. . According to such a configuration, the amount of memory used can be suppressed.

Further, in the present embodiment, an example of a variation of a verb is shown, but in the case of other parts of speech, a variation associated with the part of speech is included. For example, if the word is a noun, the variations include “original form” and “plural form”. If the word is an adjective or adverb, as a variation, "comparatives""superlative" is used.

  FIG. 5 is a diagram conceptually showing the configuration of Japanese dictionary data 243. The Japanese dictionary data 243 includes an ID, a headline, a part of speech, a variation, and data indicating meaning. The data indicating the meaning includes a semantic category, a semantic code, and a translated word. In the case where the Japanese word has a plurality of meanings, they are respectively defined according to the meaning of the data indicating the meaning.

  The ID identifies each Japanese word. As the headline, for example, the first character of the word is used.

  Each dictionary data shown in FIGS. 4 and 5 is input in advance by, for example, the manufacturer of learning content generation apparatus 200.

  FIG. 6 is a diagram showing the concept of thesaurus data in the English thesaurus data 244. In the example shown in FIG. 6, the thesaurus of the word thing (thing) is developed. Specifically, another word “physical thing” is defined in the word “thing”. In this word physical thing, another word foods is defined. The word foods has another word associated with seasoning and the word vegetable. The word seasoning is associated with other words such as the word salt, the word sugar, and the word spice. Furthermore, the word pepper and the word chili and other words are associated with the word spice as other words.

  FIG. 7 is a diagram conceptually showing an aspect of data in English thesaurus data 244. The English thesaurus data 244 includes, for each word, an ID, a semantic category, a representative word, a distance from the top node, an upper node, and a lower node. The ID specifies the word. The semantic category defines the classification of an English thesaurus that includes the word. The representative word represents a representative (for example, word seasoning) of the English thesaurus data. The distance from the top node represents the distance between the word seasoning and the word thing defined under the word. In the example shown in FIG. 6, the word seasoning goes from the word thing to the word physical thing and the word foods. Meanwhile, three nodes are included. Therefore, the distance from the top node is defined as “3”.

  The lower node indicates a node of a word defined below the word seasoning. In the example illustrated in FIG. 6, the word seasoning is associated with the word salt, sugar, spice, and the like as other words, so the ID of each word is associated as a lower node.

[Control structure]
(Word order rearrangement problem generation)
With reference to FIGS. 8 to 11, one aspect of the control structure of learning content generation apparatus 200 according to the present embodiment will be described. FIG. 8 to FIG. 11 are flowcharts showing a part of processing executed by the learning content generation apparatus 200 to generate the word order rearrangement problem.

  In step S810, control unit 230 initializes sentence number counter 271 by setting “0”.

  In step S820, control unit 230 clears the “used” flag (temporary flag 350) for all pairs of example sentences in bilingual example sentence database 241.

  In step S830, control unit 230 selects a pair of example sentences and templates from bilingual example sentence database 241 based on the output result of random number generation unit 257. The control unit 230 sets the selected set number as a variable k, for example.

  In step S840, control unit 230 determines whether or not the “use” flag is set in the set of example sentences of set k. If control unit 230 determines that the flag is set (YES in step S840), control returns to step S830. If not (NO in step S840), control unit 230 switches control to step S900. In this case, the control unit 230 reselects another example sentence.

  In step S <b> 900, control unit 230 performs word order replacement from example sentences of set k, adds an extra word, and stores the word in word order replacement result buffer 276. Details of the processing in step S900 will be described in detail with reference to FIG.

  In step S850, control unit 230 generates a question sentence using rearrangement question generation template 246 from the word order replacement result.

  In step S860, control unit 230 sets a “used” flag in temporary flag 350 of the set of example sentences of set k.

  In step S870, control unit 230 advances (increments) the numerical value of sentence number counter 271 by one.

  In step S880, control unit 230 determines whether or not the numerical value of sentence counter 271 has reached n (n is the number of prescribed problem sentences). When control unit 230 determines that the numerical value of sentence counter 271 has reached n (YES in step S880), control unit 230 switches control to step S890. If not (NO in step S880), control unit 230 returns control to step S830.

  In step S890, control unit 230 causes output unit 220 to output the generated problem sentence. For example, the question sentence is displayed on the monitor 180. In another aspect, the control unit 230 may output the question sentence as a voice.

  Details of step S900 will be described with reference to FIGS. 9 and 10. FIG. 9 and FIG. 10 are flowcharts showing processing by the control unit 230 such as changing the word order and adding extra words.

  Referring to FIG. 9, in step S902, control unit 230 counts the number of words in the target sentence, and sets the number nw in word number counter 272.

  In step S904, control unit 230 sets (initializes) 0 to replaced word number counter 280. The control unit 230 sets the numerical value stored in the replaced word number counter 280 as a variable nw1.

  In step S906, control unit 230 secures word order exchange result buffer 276 having word nw + 1 areas in main memory unit 270.

  In step S908, control unit 230 secures original word buffer 274 having word nw + 1 region in main memory unit 270, and initializes the region.

  In step S910, control unit 230 cuts the target sentence into words, and stores each word in the word number 1 to nw region of original word buffer 274, respectively.

  In step S912, control unit 230 selects one word from source word buffer 274 based on the outputs of random number generation unit 257 and random number normalization unit 258. The control unit 230 uses the variable j as the number of the word (1 ≦ j ≦ nw).

  In step S 914, control unit 230 determines whether or not the “checked” flag is set in the word j area of original word buffer 274. If controller 230 determines that the flag is set (YES in step S914), control returns to step S912. If not (NO in step S914), control unit 230 switches control to step S916.

  In step S 916, control unit 230 sets a “checked” flag in the word j area of original word buffer 274.

  In step S918, control unit 230 determines whether or not word j has an original form or a modified form different from itself. When control unit 230 determines that word j has an original form or a modified form (YES in step S918), control unit 230 switches control to step S920. If not (NO in step S918), control unit 230 returns control to step S912.

  In step S 920, control unit 230 acquires a prototype or variation of word j having a shape different from itself from English dictionary data 242, and stores the acquired prototype or variation in temporary word buffer 275. To do.

  In step S922, control unit 230 stores the word stored in temporary word buffer 275 in area nw + 1 of original word buffer 274.

  Referring to FIG. 10, in step S924, control unit 230 initializes a “used” flag for the area of word number nw + 1 in original word buffer 274.

  In step S926, control unit 230 selects one word from source word buffer 274 based on the output results of random number generation unit 257 and random number normalization unit 285. The control unit 230 specifies the number of the word using the variable i (1 ≦ i ≦ nw + 1).

  In step S928, control unit 230 determines whether or not the “used” flag is set in the word i region of original word buffer 274. The control unit 230 determines whether or not the “used” flag is set. If control unit 230 determines that the “used” flag is set in the area (YES in step S928), control unit 230 returns control to step 926. If not (NO in step S928), control unit 230 switches control to step S930.

  In step S930, control unit 230 determines that variable i is 1, the initial letter of word i is capitalized, word i is not listed in the word dictionary, and initial letter of word i. It is determined whether or not a word converted from a lowercase letter is listed in the word dictionary. When control unit 230 determines that such a word is included in English dictionary data 242 (YES in step S930), control unit 230 switches control to step S932. If not (NO in step S930), control unit 230 switches control to step S934.

  In step S932, control unit 230 sets the word obtained by converting the initial letter of word i to lower case in nw + 1 of word order change result buffer 276.

  In step S934, control unit 230 sets word i to nw1th in word order exchange result buffer 276.

  In step S936, control 230 sets a “used” flag in the word i region of source word buffer 274.

  In step S938, control unit 230 advances the apparatus (nw1) of replaced word number counter 280 by one.

  In step S940, control unit 230 determines whether or not the value of replaced word number counter 280 has reached value nw + 1. When control unit 230 determines that the value of replaced word number counter 280 has reached nw + 1 (YES in step S940), control returns to main processing (FIG. 8). If not (NO in step S940), control unit 230 returns control to step S926.

  As described above, learning content generation apparatus 200 according to the embodiment of the present invention randomly selects a set of example sentences and translations from a plurality of sets of example sentences and translations stored in advance. The learning content generation apparatus 200 generates a word order rearrangement problem by randomly rearranging the words constituting the example sentence. More specifically, the control unit 230 generates a question sentence in which the arrangement of words in a word group including the selected word is changed to a plurality of words. Thereby, the user of the learning content generation apparatus 200 can easily enjoy the word order rearrangement problem.

  In the present embodiment, the learning content generation apparatus 200 uses a Japanese sentence (translation) from the used bilingual example sentence database 241 and outputs it as it is as a hint. This is due to the following reason. In other words, in addition to the method of adding (exchanging) the one extra word when the extra one word with a changed form is added to English (the correct answer intended by the learning content generating apparatus 200), There is a possibility that a correct sentence can be made as an English sentence by rearranging except one word. Therefore, in order to limit the correct answer by the user to only the correct answer intended by the learning content generation apparatus 200 according to the present embodiment, an English sentence corresponding to the original sentence is assembled together with a translation corresponding to the original sentence. A simple problem.

  The reason for this configuration is that the learning content generation apparatus 200 according to the present embodiment does not have a configuration that automatically determines whether or not a sequence of English word groups is correctly formed as an English sentence. is there. Therefore, if the learning content generation device according to another aspect has a configuration that automatically determines whether or not a sequence of certain English words is correctly formed as an English sentence, the above-described step S918 is performed. In the processing for obtaining the variation in step S920, the configuration may be incorporated.

  More specifically, the configuration includes, for example, 1) adding a variation as one extra word, 2) removing another one word, and 3) English other than the English sentence of the original bilingual example sentence data It is determined whether or not there is a case where it can hold as a sentence. 4) If there is, the change form is not adopted. By adopting such a configuration, the learning content generation device according to another aspect can generate a problem in which there is no correct answer other than the English sentence in the original bilingual example sentence database. Thereby, it can be established as a problem without adding a Japanese sentence.

  Alternatively, in yet another aspect, learning content generation apparatus 200 according to the present embodiment generates a rearrangement problem, and then determines whether or not there are a plurality of correct answers for the problem, and the determination You may comprise so that a result may be input into the learning content generation apparatus 200. FIG. Even in this case, it can be established as a problem without adding Japanese sentences.

<First Modification>
With reference to FIG. 11, another aspect of generating the word order rearrangement problem by the learning content generation apparatus 200 will be described. FIG. 11 is a flowchart showing a procedure of processing executed when learning content generation apparatus 200 generates a word order rearrangement problem using thesaurus data.

  In step S 1118, control unit 230 determines that the word exists within semantic distance 2 from the position of the semantic information of word j in English thesaurus data 244, and the word is stored in source word buffer 274. It is determined whether or not both match. When control unit 230 determines that such a situation is satisfied (YES in step S1118), control unit 230 switches control to step S1120. If not (NO in step S1118), control unit 230 returns control to step S912.

  In step S 1120, control unit 230 stores, in temporary word buffer 275, one word existing within semantic distance 2 from the position of the semantic information of word j in English thesaurus data 244. Here, the word existing within the semantic distance 2 from the position of the semantic information of the word j is one that does not match any of the words stored in the original word buffer 274.

  The value of the semantic distance is not limited to “2”. It may be larger or smaller. Further, although the concept of “semantic distance” is used as the definition of the synonym, other concepts may be used.

<Second Modification>
In yet another aspect, a variation of the original word may be used as the word included in the sorting problem. The English dictionary data 242 which is one of the dictionary data according to this aspect includes a variation obtained by changing according to English grammatical rules like a normal electronic dictionary. The control unit 230 may generate the question sentence by including any variation of the plurality of words in the array after the change of the plurality of words. With such a configuration, variations of the sorting problem can be increased.

(Generate hole filling problem)
With reference to FIG. 12 to FIG. 14, a control structure when the learning content generation apparatus 200 generates a hole filling problem will be described. 12 to 14 are flowcharts showing a part of a series of operations executed by the control unit 230. The same steps as those described above are denoted by the same step numbers. Therefore, those descriptions are not repeated.

  Referring to FIG. 12, in step S840, control unit 230 determines whether or not the “used” flag is set for the set of example sentences and templates of set k. If controller 230 determines that the “used” flag is set (YES in step S840), control returns to step S830. If not (NO in step S840), control unit 230 switches control to step S1300.

  In step S1300, control unit 230 sets a blank part from the example sentence of set k, adds an option, and stores the result in original word buffer 274 and filling option buffer 277, respectively. The process of step S1300 will be described in detail with reference to FIG.

  In step S 1250, control unit 230 generates a question sentence using the question template (fill-filling question generation template 247) from the result of storing in filling-in option buffer 277.

  Referring to FIG. 13, in step S <b> 1306, control unit 230 secures filling option buffer 277 having three option areas in main memory unit 270.

  In step S1308, control unit 230 secures source word buffer 274 having nw word regions in main memory unit 270, and initializes the memory region.

  In step S1312, control unit 230 selects one word from source word buffer 274 based on the output results of random number generation unit 257 and random number normalization unit 258. Control unit 230 represents the number of the selected word using variable i (1 ≦ i ≦ nw).

  In step S1314, control unit 230 determines whether or not there is a “used” flag in the word i region of original word buffer 274. When control unit 230 determines that there is a “used” flag in the area (YES in step S1314), control unit 230 returns control to step S1312. If not (NO in step S1314), control unit 230 switches control to step S1318.

  In step S1318, control unit 230 determines whether or not word i has a plurality of original forms or modified forms different in shape from itself. When control unit 230 determines that the word satisfies such a condition (YES in step S1318), control unit 230 switches control to step S1320. If not (NO in step S1318), control unit 230 switches control to step S914.

  In step S1320, control unit 230 selects one of the three option regions of hole-filling problem buffer 277 based on the output results of random number generation unit 257 and random number normalization unit 258, and The word i is stored in the selected option area.

  In step S1322, control unit 230 obtains two original forms or modified forms different from itself for word i, and adds them to the remaining two of the three option areas of hole filling problem buffer 277. Stores the acquired word.

  In step S1324, control unit 230 deletes word i in source word buffer 274. The control unit 230 returns the control to the main process (FIG. 12).

  As described above, according to learning content generation apparatus 200 according to the present embodiment, a hole filling problem can be generated using a template. Therefore, the user of the learning content generation apparatus 200 can easily generate a hole filling problem.

<Third Modification>
With reference to FIG. 14, another aspect of the hole filling problem generation will be described. FIG. 14 is a flowchart showing a part of another process executed by control unit 230. The same steps as those described above are denoted by the same step numbers. Therefore, description of such processing will not be repeated.

  In step S1410, based on English thesaurus data 244, control unit 230 has a plurality of words within the semantic distance 2 from the position of the semantic information of word i, and each word is stored in source word buffer 274. It is determined whether any of the stored words does not match. If control unit 230 determines that such a condition is satisfied (YES in step S1410), control unit 230 switches control to step S1320. If not (NO in step S1410), control unit 230 switches control to step S914.

  In step S1412, the control unit 230 causes the English thesaurus data 244 to exist within the semantic distance 2 from the position of the semantic information of the word i and does not match any of the words in the source word buffer 274. Are stored in the remaining two of the three choice areas of the hole filling problem buffer 277, respectively.

  FIG. 15 is a diagram conceptually showing the structure of the thesaurus for the word agreement. Referring to FIG. 15, the word agreement is associated with the collocation “interaction among people”. This collocation is also associated with other words, such as the word session and the word talk.

  With reference to FIG. 16 and FIG. 17, generation of an error indication problem by the learning content generation apparatus 200 will be described. FIG. 16 and FIG. 17 are flowcharts showing a part of the operations executed by control unit 230, respectively. The same steps as those described above are denoted by the same step numbers. Therefore, description of such processing will not be repeated.

  In step S1600, control unit 230 generates a phrase in which one word is mistaken from the example sentence of set k, and stores the generation result in error problem buffer 278. Details of the processing will be described with reference to FIG.

  In step S 1510, control unit 230 generates a problem sentence using error indication problem generation template 248 based on the result of storage in error problem buffer 278.

  Referring to FIG. 17, in step S <b> 1604, control unit 230 reserves error problem buffer 278 having an area of nw words in main memory unit 270.

  In step S1318, control unit 230 determines whether or not word i has an original form or a modified form different from itself. If control unit 230 determines that such a condition is satisfied (YES in step S1318), control unit 230 switches control to step S1620. If not (NO in step S1318), control unit 230 switches control to step S1630.

  In step S1620, control unit 230 obtains an original form or a modified form having a shape different from that of word i itself from English dictionary data 242 for word i, and stores the obtained original form or modified form in temporary word buffer 275. .

  In step S1622, control unit 230 determines whether or not the value of variable i is 1. When control unit 230 determines that variable i is 1 (YES in step S1622), control unit 230 switches control to step S1624. If not (NO in step S1622), control unit 230 switches control to step S1626.

  In step S1624, control unit 230 converts the first character of the word in temporary word buffer 275 to upper case.

  In step S <b> 1626, control unit 230 copies words other than word i among the words in the original word buffer 274 to the error problem buffer 278.

  In step S 1628, control unit 230 stores the word in temporary word buffer 275 in the word i area of error problem buffer 278.

  In step S 1630, control unit 230 sets a “used” flag in the word j area of original word buffer 274.

  When the process of step S1628 ends, control unit 230 returns control to the main process (FIG. 16).

  Here, with reference to FIG. 18, the structure of another dictionary provided in learning content generation apparatus 200 according to the present embodiment will be described. FIG. 18 conceptually shows the dictionary structure of the word year. The same applies to other words.

  The dictionary of the word year includes an ID, a headline, a part of speech, a variation, and data indicating meaning. The data indicating meaning includes a semantic category, a semantic code, and a translated word. When the word has a plurality of meanings, a plurality of words may exist according to the meanings of the data indicating the meanings.

  The ID identifies the word year. The headline indicates the word year itself. The part of speech indicates the part of speech of the word. A variation indicates a variation that may exist for the word. When the word is a noun, a plural form (years) is defined as a variation.

  Regarding the meaning, in the case of the word year, two meanings are defined in the present embodiment. One meaning has a meaning category of “TIME”, and “year” is assigned as a translation. As another meaning, the translated word “year” is defined for the semantic category “TIME-UNIT”.

  With reference to FIG. 19, another aspect of the error indication problem generation by the learning content generation apparatus 200 will be described. FIG. 19 is a flowchart showing a part of the operation executed by control unit 230. The same steps as those described above are denoted by the same step numbers. Therefore, description of these steps will not be repeated.

  In step S1314, control unit 230 determines whether or not the “used” flag is set in the area of word i in original text buffer 274. If control unit 230 determines that the “used” flag is set in the area (YES in step S1314), control returns to step S1312. If not (NO in step S1314), control unit 230 switches control to step S1720.

  In step S1720, control unit 230 causes word in English thesaurus data 244 to have a word within semantic distance 2 from the position of the “original” semantic information of word i, and the word is stored in original word buffer 274. It is determined whether or not it matches any of the words. If control unit 230 determines that such a condition is satisfied (YES in step S1720), control unit 230 switches control to step S1730. If not (NO in step S1720), control unit 230 switches control to step S1630.

  In step S1730, control unit 230 causes word in English thesaurus data 244 to exist within a semantic distance of 2 from the position of the “original” semantic information of word i and does not match any of the words in the original word buffer. Is changed to the same change form as the word i, and the changed word is stored in the temporary word buffer 275.

  With reference to FIG. 20, another aspect of the English thesaurus data 244 will be described. FIG. 20 is a diagram showing the structure of the thesaurus of the word extend. The word extend is associated with the collocation “change in size” as a related phrase. This collocation is further associated with another word expand, word shrink, and the like.

[Operation]
With reference to FIGS. 21 to 41, the operation of learning content generation apparatus 200 according to the present embodiment based on the above-described structure and flowchart will be described.

  When a problem generation command is received based on an operation on the input unit 210, the sentence number counter 271, the word number counter 272, and the replaced word number counter 280 are initialized (FIGS. 21 to 23). In the main memory unit 270, a word order exchange result buffer 276 is secured (FIG. 24). The original English text randomly selected from the bilingual example sentence database 241 is stored in the original word buffer area 274 (FIG. 25).

  Here, with reference to FIG. 26, the structure of the dictionary for the word extend will be described. The word extend includes, as a dictionary structure, an ID, a headline, a part of speech, a variation, and data indicating meaning. An ID (00632308) is assigned to the word extend. For the headline, the word itself is used. Part of speech is defined as a “verb”. Variations include three units, past tense, past participle form, and ing (current molecular form).

  As meaning data, two meanings (translation: “continue / continue” and “extend / extend”) are used.

  Referring to FIG. 26, for the word extended with the word number “6”, the dictionary ID (= 00632308) including the word is referred to, and as shown in FIG. 27, the word extending is selected as the ing form from the dictionary. , Stored in temporary word buffer 275.

  The word (extending) stored in the temporary word buffer 275 is used to generate a word replacement problem described later.

  Referring to FIG. 28, in the original word buffer area 274, a flag indicating that the word extended (word number 6) used for selecting a word replacement candidate is set (refer to FIG. 28). Flag = 1).

  Then, as shown in FIG. 29, the word extending stored in the temporary word buffer 275 is stored in the empty area (word number 11) in the original word buffer area 274.

  Using the data stored in this way in the original word buffer 274, a word order change problem is generated.

  First, referring to FIG. 30, the first word is stored in the word order 1 area in word order change result buffer 276. In the example shown in FIG. 30, the word automatically is stored in the word order 1 area. When the replacement is performed, the value of the replaced word number counter 280 is incremented by 1 (nw1 = 1) (FIG. 31).

  When such replacement processing is performed by the number of words and / or symbols stored in the original word buffer 274 (11 times in the example shown in FIG. 29), the word order is changed as shown in FIG. The completed question sentence is completed in the word order change result buffer 276.

[template]
The rearrangement question template 246 will be described with reference to FIG. FIG. 33 is a diagram conceptually showing the configuration of rearrangement problem template 246. The sorting question template 246 includes a question sentence (fixed form) indicating that the question is a question of the sorting question, a template that prescribes the words constituting the regular sentence at random, and a display of the translation of the regular sentence. And a template that prescribes. In another aspect, a configuration that does not display a translation may be used for the sorting problem template 246.

[Display mode]
With reference to FIG. 34, the display mode of the screen of learning content generation apparatus 200 according to the present embodiment will be described. FIG. 34 is a diagram illustrating a display form of a question sentence on the monitor 180.

  For example, when the user of the learning content generation apparatus 200 instructs the start of generation of a question sentence (for example, when the enter key is pressed), the learning content generation apparatus 200 executes processing based on the above-described flowchart to configure English sentences. Generate a problem with the word order reversed. As shown in FIG. 34, the learning content generation apparatus 200 displays the generated question sentence on the monitor 180.

  With reference to FIGS. 35 to 41, another aspect of the rearrangement problem will be described. Referring to FIG. 35, a case will be described in which the word automatically (word number 5) is a target for searching for synonyms in the original text stored in original text word buffer 274. In this case, the synonym search unit 260 uses a thesaurus structure as shown in FIG. 36 to obtain a word within the semantic distance 2 from the word automatically. For example, in the example shown in FIG. 36, the thesaurus of the word automatically defines the word “way of operation”, the words manually, smoothly, and the like. Here, if one link between words or between words and collocations is defined as a semantic distance 1, a collocation “way of operation” exists at the semantic distance 1 from the word automatically, and from the word automatically At the semantic distance 2, the word manually and the word smoothly exist. Therefore, when the semantic distance 2 is defined, the synonym search unit 260 randomly selects a word manually or a word smoothly from a thesaurus as shown in FIG. 36, and selects the selected word as a temporary word buffer 275. (FIG. 37).

  In this embodiment, the definition of the synonym is “within the semantic distance 2 on the thesaurus structure”, but the definition of the synonym is not limited to this. The control unit 230 stores the synonym manually stored in the temporary word buffer 275 in the empty area (word number 11) of the original word buffer 274 (FIG. 38). In addition, a flag “1” indicating that the word has been selected is set to the word automatically selected as a replacement target.

  Thereafter, control unit 230 randomly selects a word (for example, word agreement) in the first area (word order 1) and stores the word in word order exchange result buffer 276 (FIG. 39). Similarly, when the word rearrangement process stored in the original word buffer 274 is repeated, the problem in which the word order is changed is completed (FIG. 40).

  Therefore, the control unit 230 outputs the question sentence to the output unit 220 using the data stored in the word order exchange result buffer 276. When the learning content generation apparatus 200 is realized by the computer system shown in FIG. 1, the monitor 180 displays a question sentence, a word sequence in which the word order is changed, and a Japanese translation as a hint (see FIG. 1). FIG. 41).

[Generate hole filling problem]
With reference to FIG. 42 to FIG. 51, the operation of the learning content generation apparatus 200 when generating a hole filling problem will be described. In the following description, a case where there are three options will be described, but the number of options is not limited to this. There may be two options, or four or more options.

  After initializing the memory area of the main memory unit 270, the control unit 230 stores in the original word buffer 274 the original text that is the target for generating the hole-filling problem (FIG. 42). The control unit 230 secures at least the number of memory areas in the hole filling option buffer 277 as many as the number of options. In this example, the control unit 230 secures three memory areas. Based on the random number generation unit 257 and the random number normalization unit 258, the control unit 230 selects a word that is a target of the hole filling problem. In the original text shown in FIG. 42, word number 6 (word extended) is selected. The control unit 230 stores the selected word in any region of the filling option buffer 277 (FIG. 43). The controller 230 refers to the English dictionary data 242 and further selects the original or changed form of the selected word extended. If there are more selection candidates than the number of options, the control unit 230 may selectively determine the original shape or the variation based on the random number generation unit 257 and the random number normalization unit 258. The control unit 230 stores the selected original form or changed form in another area of the filling option buffer 277 (FIG. 44).

  Referring to FIG. 45, control unit 230 deletes word extended selected as the target of the hole filling problem in original word buffer 274 and confirms that the word is selected as a question sentence candidate in the area of word number 6. The flag “1” shown is set.

  Referring to FIG. 46, control unit 230 uses the hole filling problem generation template 247 to use the data stored in source word buffer 274 and the data stored in hole filling option buffer 277 to fill the hole filling problem. Is generated. The problem sentence generated by the control unit 230 is displayed on the monitor 180 as shown in FIG. 47 according to the hole filling problem generation template 247.

  As described above, the learning content generation apparatus 200 according to the embodiment of the present invention generates a hole filling problem, so that the user of the learning content generation apparatus 200 can easily tackle the hole filling problem.

<Fourth Modification>
A case where synonyms are used as the hole filling problem will be described with reference to FIGS. 48 to 51. The control unit 230 selects a word that is a candidate for the filling problem from the original text (FIG. 42) stored in the original word buffer 274, and writes the selected word in the area of the filling option buffer 277 (FIG. 48). . The control unit 230 selects synonyms session and talk of the selected word agreement using the thesaurus (FIG. 15). The control unit 230 stores the selected synonyms in the filling option buffer 277 (FIG. 49).

  Referring to FIG. 50, control unit 230 deletes word agreement stored in word number 2 in source word buffer 274 and flag “1” indicating that the word has been used to create a question. "Is set.

  Referring to FIG. 51, control unit 230 uses word filling problem generation template 247 (FIG. 46) to store the words stored in hole filling option buffer 277 and numbers randomly selected from the original text in source word buffer 274. The generated hole filling problem is displayed on the monitor 180 based on the punctured sentence (FIG. 50) in which the word is deleted and the word column of the number is blank.

<Fifth Modification>
In still another aspect, the learning content generation apparatus 200 may include other forms of deleted words as candidates for the answer to the hole-filling problem. As an example of a specific aspect, the plural form when the singular form is deleted, the past form when the original form of the verb is deleted, and the like may be used as the answer candidate. Even in this case, variations of the problem can be increased.

  In this aspect, the English dictionary data 242 includes a variation obtained by changing English words according to English grammar rules. The control unit 230 selects one of the plurality of words as the plurality of candidate words. The control unit 230 outputs words including the variation as a plurality of candidate words.

[Generating an error indication problem]
With reference to FIGS. 52 to 62, an aspect of generating an error indication problem by the learning content generation apparatus 200 will be described. Referring to FIG. 52, control unit 230 stores the original text randomly selected from bilingual example sentence database 241 in original text word buffer 274. The control unit 230 uses the random number generation unit 257 and the random number normalization unit 258 to select words that are candidates for the error indication problem. For example, the control unit 230 selects the word years stored in the word number 9. The control unit 230 refers to a word dictionary (FIG. 18) including the selected word years, and specifies the original year of the word when the word years is plural. Referring to FIG. 53, control unit 230 stores the specified word in temporary word buffer 275.

  Referring to FIG. 54, control unit 230 stores, in error problem buffer 278, words other than word number 9 (word selected as a question sentence candidate) among the words stored in original word buffer 274. To do.

  Referring to FIG. 55, control unit 230 further stores word year stored in temporary word buffer 275 in error problem buffer 278. As a result, a problem sentence in which the plural form is intentionally changed to the original form is generated as an error problem.

  Referring to FIG. 56, control unit 230 uses error indication problem generation template 248 to apply the data stored in error problem buffer 278 to the template to generate a problem sentence. Referring to FIG. 57, control unit 230 displays the generated problem sentence on monitor 180 (FIG. 57).

<Sixth Modification>
A case where a synonym is used as an error indication problem will be described with reference to FIGS.

  Referring to FIG. 58, control unit 230 stores the question sentence randomly selected from bilingual example sentence database 241 in original sentence word buffer 274. The control unit 230 selects a word that is a target of the error indication problem based on the outputs of the random number generation unit 257 and the random number normalization unit 258. For example, the control unit 230 selects word number 6 (word extended). The control unit 230 specifies the original form of the selected word using dictionary data, and selects a synonym from the original form using a thesaurus (FIG. 20). For example, when the semantic distance 2 is defined as a synonym selection criterion, the control unit 230 can select words such as word expand and word shrink as synonyms for the word extend. The control unit 230 selects any word using the random number generation unit 275 and the random number normalization unit 258. For example, the control unit 230 selects the word shrink.

  Referring to FIG. 59, since the selected word is a past tense, control unit 230 transforms the selected word into a past tense using dictionary data, and stores the past tense in temporary word buffer 275.

  Referring to FIG. 60, control unit 230 stores the word stored in source word buffer 274 in error problem buffer 278 with the area of word number 6 selected as the candidate for the problem blanked. In this case, the control unit 230 stores words other than the word extended in the error problem buffer 278.

  Referring to FIG. 61, control unit 230 stores word shrinked stored in temporary word buffer 275 in the area of word number 6.

  Referring to FIG. 62, control unit 230 causes monitor 180 to display the completed sentence stored in error problem buffer 278.

  As described above, the learning content generation apparatus 200 according to the embodiment of the present invention randomly selects a plurality of sets of prepared example sentences and translations, and uses the selected set of example sentences and translations. A problem is generated by replacing, transforming, and removing words constituting the example sentence. Therefore, since it is not necessary to hold a problem in advance, the learning content generation apparatus 200 can make a random question every time it is activated. Further, the learning content generation apparatus 200 can generate many variations of problems without being restricted by the number of problems. Thereby, the learning content generation apparatus 200 which can generate a problem easily can be provided.

  The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

  The present invention can be applied to an information processing apparatus such as a personal computer, an information processing terminal such as a PDA, an electronic dictionary, and the like.

It is a block diagram showing the hardware constitutions of the computer system 100 which implement | achieves the learning content generation apparatus which concerns on embodiment of this invention. It is a block diagram showing the structure of the function implement | achieved by the learning content generation apparatus 200 which concerns on embodiment of this invention. It is a figure which represents notionally the one aspect | mode of the storage of the data in the bilingual example sentence database 241 of the learning content generation apparatus 200 concerning this Embodiment. It is a figure which represents notionally the structure of the English dictionary data 242. It is a figure which represents notionally the structure of the Japanese dictionary data 243. It is a figure which shows the concept of thesaurus data in the English thesaurus data 244. FIG. FIG. 4 is a diagram conceptually illustrating one aspect of data in English thesaurus data 244. It is a flowchart (the 1) showing a part of process which the learning content generation apparatus 200 performs in order to generate a word order rearrangement problem. It is a flowchart (the 2) showing a part of process which the learning content generation apparatus 200 performs in order to produce | generate a word order rearrangement problem. It is a flowchart (the 3) showing a part of process which the learning content generation apparatus 200 performs in order to produce | generate a word order rearrangement problem. It is a flowchart (the 4) showing a part of process which the learning content generation apparatus 200 performs in order to produce | generate a word order rearrangement problem. It is a flowchart (the 1) showing a part of operation | movement performed in order that the control part 230 produces | generates a hole-filling problem. It is a flowchart (the 2) showing a part of operation | movement performed in order that the control part 230 may generate | occur | produce a hole-filling problem. It is a flowchart (the 3) showing a part of operation | movement performed in order that the control part 230 produces | generates a hole-filling problem. It is a figure which represents notionally the structure of the thesaurus about the word agreement. It is a flowchart (the 1) showing a part of operation | movement performed in order that the control part 230 may generate | occur | produce an error indication problem. It is a flowchart (the 2) showing a part of operation | movement performed in order that the control part 230 may generate | occur | produce an error indication problem. It is a figure which represents notionally the dictionary structure of the word year. 10 is a flowchart showing a part of another processing mode of error indication problem generation by control unit 230. It is a figure showing the structure of the thesaurus of the word extend. It is a figure showing the state by which the sentence number counter 271 was initialized. It is a figure showing the state where the word number counter 272 was initialized. It is a figure showing the state by which the replaced word number counter 280 was initialized. FIG. 10 is a diagram illustrating a state in which a word order replacement result buffer 276 is secured in the main memory unit 270. It is a figure showing the state where the English original sentence selected at random from the bilingual example sentence database 241 was stored in the original sentence word buffer area | region 274. It is a figure showing the structure of the dictionary about the word extend. FIG. 10 is a diagram illustrating a state of temporary word buffer 275. It is a figure showing the state of the flag in the original word buffer area | region 274. It is a figure showing the data structure of the original text word buffer area | region 274. It is a figure showing one mode of storage of data in word order change result buffer. It is a figure showing the state of the replaced word number counter 280. It is a figure showing the state where the question sentence in which the word order was replaced was completed in the word order replacement result buffer 276. FIG. 5 is a diagram conceptually showing the configuration of a sorting problem template 246. It is a figure showing the display mode of the question sentence in monitor 180. FIG. It is a figure which represents notionally the state of the data in the original word buffer 274. It is a figure which represents notionally the thesaurus structure of the word automatically. FIG. 11 is a diagram illustrating an aspect of data storage in temporary word buffer 275. It is a figure showing the one aspect | mode of data storage in the original word buffer. It is a figure showing one mode of storage of data in word order change result buffer. It is a figure showing one mode of storage of data in word order change result buffer. It is a figure which shows the display of the problem sentence in the monitor 180, and a Japanese translation. It is a figure showing the one aspect | mode of data storage in the original word buffer. FIG. 11 is a diagram (part 1) showing one mode of data storage in a hole-filling option buffer 277; FIG. 11 is a diagram (part 2) showing one mode of data storage in the hole-filling option buffer 277; It is a figure showing the one aspect | mode of data storage in the original word buffer. It is a figure showing the hole filling problem production | generation template 247. FIG. It is a figure showing the display of the hole filling problem in the monitor. It is a figure which represents notionally the one aspect | mode of storage of the data of the hole-filling choice buffer 277 in another situation. It is a figure showing the one aspect | mode of the data storage of the hole-filling choice buffer 277 in another situation. It is a figure which represents notionally the one aspect | mode of the storage of the data of the original word buffer 274 in another situation. It is a figure showing the display of the monitor 180 in another situation. It is a figure showing the one aspect | mode of the storage of the data in the original text word buffer 274 in the case of generation | occurrence | production of an error indication problem. FIG. 11 is a diagram illustrating an aspect of data storage in temporary word buffer 275. FIG. 10 is a diagram (part 1) showing one mode of data storage in error problem buffer 278; FIG. 11 is a diagram (part 2) showing one aspect of data storage in error problem buffer 278; It is a figure showing the error indication problem generation template 248. It is a figure showing the display in the monitor 180. FIG. It is a figure showing the one aspect | mode of the storage of the data in the original text word buffer 274 in the case of generation | occurrence | production of an error indication problem. FIG. 11 is a diagram illustrating an aspect of data storage in temporary word buffer 275. FIG. 10 is a diagram (part 1) showing one mode of data storage in error problem buffer 278; FIG. 11 is a diagram (part 2) showing one aspect of data storage in error problem buffer 278; It is a figure showing the display in the monitor 180. FIG.

Explanation of symbols

  100 Computer system, 162 CD-ROM.

Claims (12)

A learning content generation device,
Display means for displaying a screen;
A first language example sentence composed of a plurality of words in a first language and a translated sentence obtained by translating the example sentence in the first language into a second language are stored in association with each other. Storage means for storing the part of speech of the word and the variation of the language as dictionary data in association with each other;
Control means for controlling the learning content generation device,
The control means includes
Randomly selecting one word from the plurality of words constituting the example sentence of the first language;
The variation of the selected one word is obtained from the dictionary data, and it is determined whether or not there are a plurality of variations of the selected one word, and the variation of the selected one word When it is determined that there are not a plurality of words, one word is re-selected at random from the plurality of words, and the determination is performed on the re-selected one word.
When it is determined that there are a plurality of variations of the selected one word , a plurality of words having the same part of speech as the selected one word are acquired from the dictionary data , and the acquired plurality of words are A plurality of candidate words changed to the changed form are selected from the storage means,
A series of words consisting of the remaining words excluding the selected one word from a plurality of words constituting the example sentence, a display for indicating deletion of the selected one word , and the selected Generating a question sentence that displays one word and the plurality of candidate words as options;
A learning content generation apparatus that causes the display unit to display the question sentence. A learning content generation device,
Display means for displaying a screen;
A first language example sentence composed of a plurality of words in a first language and a translated sentence obtained by translating the example sentence in the first language into a second language are stored in association with each other. Storage means for storing the part of speech of the word and the variation of the language as dictionary data in association with each other;
Control means for controlling the learning content generation device,
The control means includes
Randomly selecting one word from the plurality of words constituting the example sentence of the first language;
The variation of the selected one word is obtained from the dictionary data, and it is determined whether there is a variation of the selected one word, and the variation of the selected one word When it is determined that there is no shape, one word is randomly reselected from the plurality of words, and the determination is performed on the one reselected word,
If it is determined that there is variation of 1 word said selected, said from the dictionary data to get the different different word from the word of the 1 in the same part of speech a word of the one, acquired the A candidate word obtained by changing another word into the variation is selected from the storage means;
A series of words consisting of the remaining words excluding the selected one word from a plurality of words constituting the example sentence, a display for indicating deletion of the selected one word , and the selected Generating a question sentence that displays one word and the plurality of candidate words as options;
A learning content generation apparatus that causes the display unit to display the question sentence. The dictionary includes a synonym dictionary;
It said control means selects the one word synonyms as said another word, the learning content generation apparatus of claim 2.   The learning content generation apparatus according to claim 1, wherein the control unit causes the display unit to display the question sentence in the first language and the translated sentence in the second language. .   The learning content generation apparatus according to claim 1, wherein the control unit causes the display unit to display the question sentence and the translated sentence of the example sentence. A learning content generating method by a computer, wherein the computer converts a processor, a first language example sentence composed of a plurality of words in a first language, and a first language example sentence into a second language. And a display device, a memory that stores the translated sentence translated into the dictionary data in association with the part of speech of the word in the first language and the variation of the language,
The processor randomly selects one word from the plurality of words constituting the example sentence of the first language, obtains a variation of the selected one word from the dictionary data, and selects the selection It is determined whether or not there is a plurality of variations of the selected one word, and when it is determined that there are not a plurality of variations of the selected one word, one word is randomly selected from the plurality of words Reselecting and making the determination for the reselected one word;
When it is determined that there are a plurality of variations of the selected one word , the processor acquires a plurality of words having the same part of speech as the selected one word from the dictionary data , and acquired Selecting, from the memory, a plurality of candidate words obtained by changing a plurality of words into the variation;
A series of words including a remaining word obtained by removing the selected one word from a plurality of words constituting the example sentence; and a display for indicating deletion of the selected one word ; Generating a question sentence that displays the selected one word and the plurality of candidate words as options;
And a step of causing the processor to display the question sentence on the display device. A learning content generating method by a computer, wherein the computer converts a processor, a first language example sentence composed of a plurality of words in a first language, and a first language example sentence into a second language. And a display device, a memory that stores the translated sentence translated into the dictionary data in association with the part of speech of the word in the first language and the variation of the language,
The processor randomly selects one word from the plurality of words constituting the example sentence of the first language, obtains a variation of the selected one word from the dictionary data, and selects the selection It is determined whether there is a variation of the selected one word, and if it is determined that there is no variation of the selected one word, one word is re-randomly selected from the plurality of words. Selecting and making the determination for the reselected one word;
Wherein the processor, when the variation of the first word said selected is determined that there is the from the dictionary data to get the different different word from the word of the 1 in the same part of speech a word of the 1 Selecting from the memory a candidate word obtained by changing the acquired another word into the change form;
A series of words including a remaining word obtained by removing the selected one word from a plurality of words constituting the example sentence; and a display for indicating deletion of the selected one word ; Generating a question sentence that displays the selected one word and the plurality of candidate words as options;
And a step of causing the processor to display the question sentence on the display device. The dictionary data includes synonym dictionary data ,
Step includes selecting the first word synonyms as said another word, a method of generating a learning content according to claim 7, wherein said selecting.   9. The display according to claim 6, wherein the displaying includes displaying the question sentence in the first language and the translated sentence in the second language on the display device. Learning content generation method.   The learning content generating method according to claim 6, wherein the displaying includes displaying the question sentence and the translated sentence of the example sentence on the display device.   The program which makes a computer perform the production | generation method of the learning content in any one of Claims 6-10.   A computer-readable recording medium storing the program according to claim 11.

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