JP2009070194A - Translator selection method, translation content providing server, and translator selection program - Google Patents

Abstract

The present invention provides a translator selection method, a translation content providing server, and a translator selection program for selecting a translator capable of accurately translating original content.
When a translation request for a content is received from a user terminal in a server constituting the translation system, the original content is downloaded (S2), and information on the identity of the original author included in the original content is provided. Extracted (S4). The extracted information relating to the identity of the original author is compared with the information relating to the identity of the translator, and aptitude values based on nationality, age and sex are calculated (S6 to S8). A translator whose sum of aptitude values based on nationality, age and gender is maximized is extracted (S11), and is determined as an official translation request destination (S14).
[Selection] Figure 7

Description

  The present invention relates to a translator selection method, a translation content providing server, and a translator selection program. More specifically, the present invention relates to a translator selection method, a translation content providing server, and a translator selection program for selecting a translator in consideration of information relating to the identity of the author and translator of the original content.

  In recent years, with the spread of the Internet, users can obtain information that meets their needs from all over the world by searching Web content that is information on the WWW. However, in the case of Web content written in a language other than the native language, the user is not necessarily familiar with the language, and it is often difficult to understand the content. In such a case, since the user requests the translator to translate, a translation mediation system for selecting an appropriate translator has been proposed.

For example, in the translation mediation system described in Patent Document 1, when a user sends a document to be translated to a server, the server first selects a translator corresponding to the specialized field of the document. Thereafter, the translation is performed mechanically based on the dictionary created by the translator, and based on the result, the user is allowed to select a translator who is officially requested to translate.
JP 2003-271595 A

  However, in the translation mediation system described in Patent Document 1, it is possible to select a translator who matches the specialized field, but since the identity of the author of the document to be translated is not considered at all, It was not always possible to select an appropriate translator. For example, when translating a blog article of a female teenager, there is no specialized field, so it was difficult to select a translator in the specialized field. Moreover, even if this blog is related to some specialized field, for example, when a man in his 60s is selected as a translator, a translation reflecting the subtle nuances of the expression of a woman in his teens cannot be expected.

  The present invention has been made to solve the above-described problems, and includes a translator selection method, a translation content providing server, and a translator selection program for selecting a translator capable of accurately translating original content. The purpose is to provide.

  To achieve the above object, a translator selection method according to the first aspect of the present invention provides a server that provides content translated into a language different from the original content to a client terminal connected via a network. Translator selection method processed by the method, the original author information acquisition step for acquiring information on the identity of the original author who is the author of the original content that is the content to be translated, and the identity of a plurality of translator candidates A candidate information acquisition step for acquiring information on the information, information on the identity of the original author acquired in the original author information acquisition step, and the identity of the translator candidate acquired in the candidate information acquisition step The degree of commonality between the plurality of translator candidates and the information related to the identity of the original author. Ri translators candidates with information about high the identity, and a translator selection step of selecting as a translator.

  In the translator selection method of the invention according to claim 2, in addition to the configuration of the invention according to claim 1, the information on the identity includes information on nationality. In the translator selection step, It is characterized by prioritizing translator candidates of the same nationality.

  In the translator selecting method of the invention according to claim 3, in addition to the configuration of the invention according to claim 1 or 2, the information relating to the identity includes information relating to age, and in the translator selecting step, the original author It is characterized by preferentially selecting a translator candidate whose age is closer to that of the author.

  In the translator selecting method of the invention according to claim 4, in addition to the configuration of the invention according to any one of claims 1 to 3, the information relating to the identity includes information relating to gender, and in the translator selecting step, , A translator candidate having the same gender as that of the original author is preferentially selected.

  Moreover, in the translator selection method of the invention according to claim 5, in addition to the configuration of the invention according to any one of claims 1 to 4, the information relating to the identity includes information relating to religion, and in the translator selecting step, The candidate translators who believe in the same religion as the original author are preferentially selected.

  According to a sixth aspect of the present invention, there is provided a translator selection method based on the content analysis step of analyzing the content of the original text content and the analysis result of the content analysis means in addition to the configuration of the invention of the first aspect. A priority information setting step for setting priority information, which is information to be preferentially considered when selecting the translator from information included in the information relating to the identity, and in the translator selection step, In consideration of the priority information set in the priority information setting step, a translator candidate having information on the identity having a higher degree of commonality with the information on the identity of the original author is selected as the translator. It is characterized by.

  In addition, in the translator selecting method of the invention according to claim 7, in addition to the configuration of the invention according to claim 6, the information on the identity includes information on at least one of nationality, age, sex, and religion, The priority information setting step includes a priority order setting step for assigning a priority to the priority information set in the priority information setting step, and the translator selection step includes the priority order set in the priority order setting step. Considering the priority information in descending order, the translator candidate having information on the identity having a higher degree of commonality with the information on the identity of the original author is selected as the translator.

  The translation selection server of the invention according to claim 8 is a translator selection server that provides a content translated into a language different from the original content to a client terminal connected via a network, Original author information acquisition means for acquiring information on the identity of the original author who is the author of the original content that is the content to be targeted, candidate information acquisition means for acquiring information on the identity of a plurality of translator candidates, The plurality of translator candidates by comparing the information on the identity of the original author acquired by the original author information acquisition means with the information on the identity of the translator candidates acquired by the candidate information acquisition means From the above, candidate translators having information on the identity having a higher degree of commonality with the information on the identity of the original author And a translator selection means for selecting as a translator.

  Furthermore, a translator selection program according to a ninth aspect of the invention causes a computer to execute various processing steps of the translator selection method according to any one of the first to seventh aspects.

  In the translator selection method of the invention according to claim 1, information on the identity of the original author and a plurality of translator candidates is acquired, and a translator candidate having a higher degree of commonality with the original author in the information on the identity is selected as a translator. It is determined. The information about the identity is information about the person's birth and circumstances, so the higher the degree of commonness, the more common knowledge, and the way of thinking and writing are similar. . Therefore, it is possible to select a translator who can translate the original text accurately.

  In the translator selection method of the invention according to claim 2, information on the nationality of the original author and a plurality of translator candidates is acquired, and translator candidates of the same nationality as the original author are preferentially selected. If the countries where they were born and raised are the same, they will have much more knowledge in common with the original author than in other countries, and the way of thinking and expression will be similar. Therefore, by preferentially selecting translators of the same nationality, it is possible to select translators capable of translating more accurately reflecting the original text in addition to the effects of the invention of claim 1.

  In the translator selection method of the invention according to claim 3, information on the age of the original author and a plurality of translator candidates is acquired, and a translator candidate having an age closer to that of the original author is preferentially selected. When the ages are close, it seems that there is much knowledge common to the original authors, and the way of thinking and expression are similar to those of people of completely different generations. For example, the spoken language of Japanese high school students has many expressions specific to this age, but it is the same in other languages. Therefore, selecting translators capable of translation that directly reflects the expression and nuance of the original text in addition to the effects of the invention according to claim 1 or 2 by preferentially selecting translators of closer ages. Can do.

  In the translator selection method of the invention according to claim 4, information on the gender of the original author and a plurality of translator candidates is acquired, and translator candidates of the same gender as the original author are preferentially selected. If they are of the same sex, they will have much more knowledge in common with the original author than the opposite sex, and the way of thinking and expression will be similar. For example, women-specific phrases are difficult to translate because they are unfamiliar to men. Therefore, by preferentially selecting homosexual translators, in addition to the effects of the invention according to any one of claims 1 to 3, a translator capable of more natural and accurate translation can be selected.

  In the translator selection method of the invention according to claim 5, information about the religion of the original author and a plurality of translator candidates is acquired, and a translator candidate who believes in the same religion as the original author is preferentially selected. If the religion is the same, there is a lot of knowledge in common with the original author, and the way of thinking and expression are similar to those of people who believe in other religions. Therefore, by preferentially selecting a translator who believes in the same religion, in addition to the effect of the invention according to any one of claims 1 to 4, a translation capable of more accurately capturing the philosophy of the original author Person can be selected.

  In the translator selection method of the invention according to claim 6, the translator is selected in consideration of the priority information set according to the contents of the original text content. The important information (nationality, age, gender, religion, etc.) of the identity information differs depending on the content of the original content. Therefore, by changing the information to be preferentially considered when selecting a translator according to the contents, in addition to the effect of the invention of claim 1, it is possible to select a translator capable of more accurate translation. .

  In the translator selection method of the invention according to claim 7, priority is given to nationality, age, sex, and religion according to the content of the original text content. Therefore, in addition to the effect of the invention described in claim 6, it is possible to select an appropriate translator in consideration of various information according to the content of the original text content.

  In the translator selection server of the invention according to claim 8, information on the identity of the original author and a plurality of translator candidates is acquired, and a translator candidate having a higher degree of commonality with the original author in the information on the identity is selected as a translator. It is determined. The information about the identity is information about the person's birth and circumstances, so the higher the degree of commonness, the more common knowledge, and the way of thinking and writing are similar. . Therefore, it is possible to select a translator who can translate the original text accurately.

  The translator selection program of the invention according to claim 9 causes a computer to execute various processing steps of the translated content providing method according to any one of claims 1 to 7. Therefore, the effect of the invention according to any one of claims 1 to 7 can be achieved.

  DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, embodiments of the invention will be described with reference to the drawings. The drawings to be referred to are used for explaining the technical features that can be adopted by the present invention, and the configuration of the apparatus described, the flowcharts of various processes, etc., unless otherwise specified. It is not intended to be limited to that, but merely an illustrative example.

<First Embodiment>
First, the translation system 1 according to the first embodiment of the present invention will be described with reference to FIGS. First, a schematic configuration of the translation system 1 will be described with reference to FIG. FIG. 1 is a system configuration diagram showing a schematic configuration of the translation system 1.

  As shown in FIG. 1, the translation system 1 includes a server 10, a user terminal 20 connected to the server 10 via the Internet 3, and a translator terminal 30. The server 10, the user terminal 20, and the translator terminal 30 are so-called personal computers and are general-purpose devices. In FIG. 1, only one user terminal 20 and one translator terminal 30 are shown, but there are actually a plurality of them. The “user terminal 20” refers to a general member terminal of the translation system 1 that can request the server 10 to provide a translation. On the other hand, the “translator terminal 30” refers to a translator's terminal that provides a translated sentence to the server 10 in response to a request from the server 10. In the translation system 1, the user of the user terminal 20 pays a predetermined usage fee to the management company of the translation system 1, thereby providing a translation of Web content (hereinafter simply referred to as content) published on the Internet 3. Can be requested from the server 10. When receiving a translation provision request from the user terminal 20, the server 10 selects a translator as appropriate and requests the translator terminal 30 of the translator to translate. A method for selecting a translator at this time will be described in detail later. Also, in the following description, the original text content described in the first language published on the Internet is called “original text content”, and the original text content is translated into a second language different from the first language. This is called “translated content”.

  Next, the configuration of the server 10 will be described with reference to FIGS. FIG. 2 is a block diagram showing an electrical configuration of the server 10. FIG. 3 is an explanatory diagram of the storage area of the RAM 13. FIG. 4 is an explanatory diagram of the suitability value list stored in the suitability value storage area 134. FIG. 5 is an explanatory diagram of translator information stored in the translator database. FIG. 6 is an explanatory diagram of a translation request list stored in the translation request storage area 143.

  As shown in FIG. 2, the server 10 includes a CPU 11, a ROM 12, a RAM 13, a hard disk device (HDD) 14, a display 15, a keyboard 16, a mouse 17, and a communication I / F 18 that are connected to each other via a bus. . The ROM 12 stores various programs for operating the server 10 such as BIOS, and the CPU 11 controls the operation of the server 10 according to the programs stored in the ROM 12. The communication I / F 18 is for connecting to the Internet 3 and transmitting / receiving data to / from external devices such as the user terminal 20 and the translator terminal 30.

  The RAM 13 is a storage element for temporarily storing various data. Here, a storage area provided in the RAM 13 will be described with reference to FIG. As shown in FIG. 3, the RAM 13 has a plurality of storage areas including an original content storage area 131, an original author information storage area 132, a translator information storage area 133, an aptitude value storage area 134, and a candidate information storage area 135. I have. In the original text content storage area 131, an HTML document downloaded from a URL (Uniform Resource Locator) described in the request for providing translated content is stored as the original text content. In the original author information storage area 132, the identity of the author of the original content (hereinafter referred to as the original author) included in the original content, that is, information on the birth and circumstances of the person (hereinafter referred to as identity information) is extracted. Memorized. The translator information storage area 133 stores information about translators read from a translator database (DB) described later. In the aptitude value storage area 134, an aptitude value list indicating aptitude values calculated based on the identity information for each translator in a translator selection process described later is stored. Specifically, as shown in FIG. 4, aptitude values calculated based on nationality, age, and gender, which are constituent elements of identity information, are stored in the aptitude value list in association with the translator ID. Is done. In the candidate information storage area 135, information on a translator extracted as a translation request candidate in the course of a translator selection process described later is stored.

  As shown in FIG. 2, the HDD 14 serving as a storage device includes a plurality of storage areas including a program storage area 141, a translator DB storage area 142, and a translation request storage area 143. The program storage area 141 stores various programs for causing the server 10 to execute various processes including a translator selection process (see FIG. 7) described later. The translator DB storage area 142 stores a translator DB that stores information on translators of all translator terminals 30 connected to the server 10 (hereinafter referred to as translator information). The translation request storage area 143 stores a translation request list for specifying the original text content and the translation destination for the translation request made in response to the translation content provision request from the user terminal 20. Although not shown, in addition to these storage areas, the HDD 14 also has a storage area for storing a user database for storing user information such as identification codes, names, and contacts for all users of the user terminal 20. Is provided.

  Here, with reference to FIG. 5, the translator information stored in the translator DB of the translator DB storage area 142 (see FIG. 2) will be described. The translator information is created for each of the translators registered as translators who are users defined for each translator terminal 30 and who receive the request from the server 10 and translate the original text content. Information. As shown in FIG. 5, the translator information includes a translator ID, name, nationality, age, gender, and religion. In FIG. 5, only seven translator information is shown, but in reality, translator information corresponding to the number of translator terminals 30 exists in the translator DB. The “translator ID” is an identification code unique to each translator of the translator terminal 30. In “name”, “nationality”, and “age”, the name, nationality, and age of each translator are stored. In “sex”, the sex of the translator is stored as “M” for males and as “F” for females. In “Religion”, the religion that the translator believes is stored. Although not shown in FIG. 5, in the translator information registered in the translator DB, in addition to the information described above, the translator's address, telephone number, and mail address are also stored. . Furthermore, information on a translatable language (for example, “Chinese → Japanese”) indicating which language the translator can translate from which language into is also stored. In the following description, for the sake of convenience, it is assumed that all the translators listed in FIG. 5 are registered as translators capable of translating from English to Japanese.

  Next, the translation request list stored in the translation request storage area 143 will be described with reference to FIG. The translation request list is a list for specifying and managing the original text content requested for translation and the translation request destination. Although details will be described later, when the server 10 receives a new translation content provision request from any of the user terminals 20, a new record is created and added to the translation request list. The translation request list in FIG. 6 indicates that translation is currently requested for three original text contents. As shown in FIG. 6, the information stored in the translation request list includes an original text ID, a URL, a requester ID, a translation language, and a requested translator ID. The “original text ID” is an identification code unique to each original text content that is assigned by the server 10 to identify each original text content when a new record is created. “URL” is information for specifying the location of the original content on the Internet 3. The “requester ID” is an identification code of the user terminal 20 that is the transmission source of the translation content provision request. The “translation language” is the language of the original text content and the language of the desired translation content. The “request destination translator ID” is a translator ID of a translator determined as a translation request destination of the original text content by a translator selection process described later.

  Hereinafter, the translator selection process performed by the server 10 will be described with reference to FIGS. 7 and 8. This process is a process of determining a translation request destination in consideration of the identity information of the original author and the translator when the server 10 receives a translation content provision request from any of the user terminals 20. FIG. 7 is a flowchart of translator selection processing. FIG. 8 is a schematic diagram showing an example of identity information described in the vCard format. 7 is executed by the CPU 11 in accordance with the program stored in the program storage area 141 (see FIG. 2) of the HDD 14.

  When the translator selection process shown in FIG. 7 is started, the CPU 11 first creates a new record based on the translation content provision request received from the user terminal 20 in order to manage the contents of the translation request and the request destination. Then, it is stored in the translation request list (see FIG. 6) in the translation request storage area 143 of the HDD 14 (S1). In the present embodiment, the translation content provision request from the user terminal 20 includes the URL of the original text content to be translated. Therefore, the CPU 11 generates a new original text ID corresponding to the provision request, and adds it to the translation request list together with the received URL, the transmission source user terminal 20 identification code (requester ID), and the translation language. Subsequently, the CPU 11 downloads an HTML document, which is the original text content, from the URL stored in the translation request list via the Internet 3, and stores it in the original text content storage area 131 of the RAM 13 (S2).

  Next, the CPU 11 determines whether or not the HTML document stored in the original content storage area 131 includes the identity information of the original author (S3). Here, whether or not the identity information is included in the HTML document is determined by, for example, vCard and hCard, which are standard formats for electronic business cards, or FOAF (Friend of a Friend), which is a format for describing information about people. This can be done based on whether the described information is included. vCard and hCard can describe information such as the name, affiliation, address, telephone number, e-mail address, and birthday of the original author. As shown in FIG. 8, the information described in the vCard format starts with “BEGIN: VCARD” and ends with “END: VCARD” to indicate that the information is described in the vCard format. In the example of information described in the vCard format shown in FIG. 8, the contents include the name (N) of the original author, the first and last name (FN), the address (ADR), the telephone number (TEL), and the e-mail. An address (EMAIL) and a birthday (BDAY) are described. Since hCard is defined on the basis of vCard, identity information can be described in a similar format. In addition, FOAF can describe a wide variety of information such as the original author's name, affiliation, address, telephone number, e-mail address, birthday, gender, and topics of interest, similar to vCard and hCard.

  If the CPU 11 determines that the HTML document, which is the original text content, contains the original author's identity information (S3: YES), the CPU 11 extracts this from the HTML document, and uses the original author information in the RAM 13 as the original author information. The data is stored in the storage area 132 (S4). For example, information described in the vCard format shown in FIG. 8 is acquired in the original author information storage area 132 as original author information. Thereafter, the CPU 11 reads the translator information stored first from the translator DB stored in the translator DB storage area 142 of the HDD 14 as the first processing target, and stores it in the translator information storage area 133 of the RAM 13. (S5). In the example of the translator DB in FIG. 5, translator information of “Ichiro Ito” with the translator ID “001” is acquired in the translator information storage area 133. The translator information read out here is selected only from translator information including the same translatable language as the translation language stored in the translation request list (see FIG. 6). That is, for example, when a Japanese translation content request is made for an English original content, only a translator who stores “English → Japanese” as a translatable language is selected. In the following description, the same applies to the case of “reading the translator information from the translator DB”.

  Subsequently, the CPU calculates an aptitude value based on the nationality of the translator to be processed based on the original author information and the translator information, and is stored in the aptitude value storage area 134 of the RAM 13 together with the translator ID. It is stored in the suitability value list (S6). Specifically, for example, in S4, when vCard format identity information is acquired as the original author information, the country included in the address (ADR) described therein is regarded as a nationality, and this and the translator information Compare nationality included in When both are the same, the suitability value based on the nationality of the translator is set to “100”, and when both are different, “0” is stored in the suitability value list. This is because people in the same country have more knowledge in common with the original authors than people in other countries, and the way of thinking and expression are similar, and it is thought that they can create a translation that accurately reflects the original text. When the translator's nationality is the same as that of the original author, the suitability value based on the nationality is increased. In the address (ADR) described in the vCard format, the end part delimited by the last semicolon (;) represents the country, so the country name can be easily determined from this part. For example, when the vCard format identification information shown in FIG. 8 is acquired as the original author information, the country included in the address (ADR) is “Japan”. Further, the nationality of “Ichiro Ito” of the translator ID “001” that is the current processing target is “Japan”. Therefore, as shown in FIG. 4, the translator ID “001” and the aptitude value “100” based on nationality are stored in the aptitude value list. For example, when FOAF is acquired as the original author information and the address is included in the FOAF, the suitability value may be calculated in the same manner with regard to this as nationality. If the original author information obtained in S4 does not include any information about the country that can be regarded as “nationality” such as the address of the original author, the suitability value based on nationality should be “0” Good.

  When the calculation of the aptitude value based on the nationality is completed (S6), the CPU 11 calculates the aptitude value based on the age of the translator to be processed based on the original author information and the translator information. It is stored in the storage area 134 in association with the translator ID (S7). Specifically, for example, in S4, when vCard format identification information is acquired as the original author information, first, from the date included in the birthday (BDAY) described therein, the original author's information is obtained. Calculate the current age. Then, the value obtained by the suitability value based on age = 30 / (the absolute value of the difference between the age of the original author and the translator + 1) is stored in the suitability value list. That is, the closer the ages of the original author and translator are, the greater the suitability value of the translator with “30” as the maximum value. Humans of similar ages have more common knowledge than people of completely different generations and are thought to have similar ways of thinking and expression. For example, if an expression specific to a youth in a language of a country is translated into a language of another country, it can be said that the original nuance is reflected as it is in that language. Thus, as the translator's age is closer to that of the original author, the aptitude value is increased. For example, when the vCard format identification information shown in FIG. 8 is acquired as the original author information, the birthday (BDAY) is “1990-01-01”, that is, “January 1, 1990”. As of January 1, the original author is 17 years old. On the other hand, since the age of “Ichiro Ito” of the translator ID “001” to be processed is “31”, the suitability value based on the age is 30 / {(31-17) +1} = 2.00. As shown in FIG. 4, “2.00” is stored in the suitability value list in association with the translator ID “0001”. The same applies when information described by hCard or FOAF is acquired as the original author information. When the original author information acquired in S4 does not include any information about the age of the original author, such as the original author's birthday, the suitability value based on age may be set to “0”.

  When the calculation of the aptitude value based on the age is completed (S7), the CPU 11 calculates the aptitude value based on the sex of the translator to be processed based on the original author information and the translator information. It is stored in the storage area 134 in association with the translator ID (S8). Specifically, for example, in S4, identity information described in FOAF is acquired as original author information, and if it includes gender, it is compared with the gender included in the translator information. When both are the same, the suitability value based on the gender of the translator is set to “50”, and when both are different, “0” is stored in the suitability value list. If they are of the same sex, they will be more similar to the original author than the opposite sex, and will be able to create more natural and accurate translations. For example, women-specific phrases are difficult to translate because they are unfamiliar to men. Therefore, in this way, when the translator's gender is the same as that of the original author, the aptitude value is increased. Since vCard and hCard do not have an element that defines gender, when the original author information is information described in vCard or hCard format, or when information described in FOAF does not include gender. The suitability value based on gender is “0”.

  When the aptitude values based on nationality, age, and gender are calculated as described above (S6 to S8), the CPU 11 determines whether or not the processing has been completed for all the translator information stored in the translator DB. (S9). In the example of the translator DB shown in FIG. 5, the processing of the first translator information with the translator ID “001” has only been completed. That is, since the processing of all the translator information is not completed (S9: NO), the CPU 11 reads the second translator information as the next processing target and stores it in the translator information storage area 133 of the RAM 13. (S10), the process returns to S6. In the example of FIG. 5, the translator information of “Jiro Sato” with the translator ID “0002” is read in S10. Thereafter, the CPU 11 repeats the same processing as described above until all suitability values are calculated for all the translator information stored in the translator DB (S6 to S10).

  When the processing is completed for all the translator information (S9: YES), the CPU 11 refers to the suitability value list stored in the suitability value storage area 134, and the sum of suitability values for nationality, age, and gender is calculated. The maximum translator ID is extracted. Then, the translator corresponding to this translator ID is a candidate for officially requesting translation, and the translator information (candidate information) of this candidate is read from the translator DB, and the candidate information storage in the RAM 13 is stored. It is stored in the area 135 (S11). For example, if identity information written in FOAF is acquired as original author information, the address is “Japan”, the birthday is “January 1, 1990”, and the gender is “female”, the translator shown in FIG. When the processing for all the information is completed, the suitability value list is as shown in FIG. In this case, since the sum of the suitability values of the translator ID “005” is the largest at “160.00”, the translator information of “Hanako Suzuki” of the translator ID “005” shown in FIG. Is stored in the candidate information storage area 135. As described above, when only one candidate is extracted (S12: YES), the CPU 11 determines this candidate as the official requesting translator, and the translator ID is translated into the HDD 14. The requested translation ID is stored in the translation request list (see FIG. 6) stored in the request storage area 143 (S14). Then, the translator selection process in FIG. 7 ends.

  When there are a plurality of translator IDs with the maximum sum of aptitude values and a plurality of candidates are extracted (S12: NO), the CPU 11 randomly selects one from the plurality of candidates (S13). ). Any known method may be adopted as a method for selecting one person at random. For example, a random number generation program can be stored in the program storage area 141 of the HDD 14 and used. The random number generation program delivers a predetermined number as a random number when a predetermined value is given as an argument. Therefore, for example, the number of candidates may be given to the random number generation program as an argument, and the candidate corresponding to the obtained random number may be selected. In this way, when one candidate is selected at random (S13), the candidate is determined as the official requesting translator, and the translator ID is the translation request list as the requesting translator ID. (See FIG. 6) (S14). Then, the translator selection process in FIG. 7 ends.

  On the other hand, if the source text content downloaded in response to the translation content provision request from the user terminal 20 does not include identity information (S3: NO), the translator cannot be selected based on the identity information, so that The process shown in FIG. In this case, for example, according to the field to which the original text content to be translated belongs, a process for selecting a translator who specializes in the field may be performed. Alternatively, one translator may be selected at random from among translators whose translator information is registered in the translator DB by using a random number generation program as described above.

  After the translator as the request destination of the original text content is determined in this way, the CPU 11 of the server 10 sends the determined translator to the determined translator in accordance with the translation request processing program stored in the program storage area 141 of the HDD 14. Execute the translation request process. Specifically, for example, an email address corresponding to the requested translator ID stored in the translation request list of FIG. 6 is read from the translator DB, and a translation request describing the URL of the original content is transmitted to the email address. do it. Then, when the translated content is transmitted from the requested translator to the server 10, the CPU 11 of the server 10 sets a URL for publishing the translator content, and the requester ID stored in the translation request list in FIG. The set URL is notified to the user terminal 20 corresponding to. As a result, the user terminal 20 can browse the translated content requested to be provided.

  As described above, in the server 10 constituting the translation system 1 of the present embodiment, the identity information of the original author and the translator is acquired, and for each translator, the nationality, age, And suitability values based on gender are calculated respectively. Thereafter, the sum of these suitability values is calculated, and the translator having the largest sum is determined as the official translation request destination. The sum of aptitude values based on nationality, age, and gender is an index that indicates how close the identity information of the translator is to the identity information of the original author as a whole. It can be said that it is higher. The higher the degree of commonality, the more common knowledge and the similar way of thinking and expression of sentences. Therefore, according to the present embodiment, it is possible to select a translator who is similar in concept and expression to the original author, and to provide translated content that accurately reflects the original text.

  In the present embodiment, the server 10 corresponds to the “server” or “translator selection server” of the present invention, and the user terminal 20 corresponds to the “client terminal”. The translator selection process (FIG. 7) performed by the CPU 11 of the server 10 corresponds to the “translator selection method” of the present invention. The step of extracting the original author's identity information in S4 of FIG. 7 corresponds to the “original author information acquisition step”, and the step of reading the translator information in S5 or S10 corresponds to the “translator information acquisition step”. The step of comparing the identity information of the original author and the translator in S6 to S14 and selecting a translator with a higher degree of commonality of the identity information corresponds to the “translator selection step”. Further, the CPU 11 of the server 10 that extracts the original author information in S4 of FIG. 7 corresponds to “original author information acquisition means”, and the CPU 11 that reads out the translator information in S5 or S10 serves as the “translator information acquisition means”. Equivalent to. The CPU 11 that compares the identity information of the original author and the translator in S6 to S14 and selects a translator with a higher degree of commonality of the identity information corresponds to the “translator selection means”.

<Second Embodiment>
Next, a second embodiment of the present invention will be described with reference to FIGS. The second embodiment is characterized in that, in selecting a translator, information to be preferentially considered in the identity information is changed according to the content of the original text content to be translated.

  Since the configuration of the translation system of the present embodiment is the same as that of the translation system 1 of the first embodiment, description thereof is omitted here. Further, the configuration of the server on which the translator selection process is executed is the same as the configuration of the server 10 of the first embodiment except for the storage areas of the HDD 14 and the RAM 13. Accordingly, only the storage areas of the HDD 14 and the RAM 13 according to this embodiment will be described here.

  First, the HDD 14 will be described with reference to FIGS. 9 and 10. FIG. 9 is a block diagram of the server 10 according to the present embodiment. FIG. 10 is an explanatory diagram of the keyword database stored in the keyword database storage area 144.

  As shown in FIG. 9, in this embodiment, the HDD 14 has a keyword database (DB) in addition to the same program storage area 141, translator DB storage area 142, and translation request storage area 143 as in the first embodiment. A keyword DB storage area 144 for storing is provided. As shown in FIG. 10, the keyword DB is a database that stores an ID that is an identification code of a keyword and a keyword in association with each other. In the present embodiment, the keyword DB stores keywords used for analyzing and classifying the contents of the original text content. In this embodiment, a large number of words classified into three categories of region, culture, and religion are stored together with an ID that can identify the category to which the word belongs. Specifically, the ID consists of a 6-digit number, and the left one digit represents “1 = region”, “2 = culture”, and “3 = religion”, respectively. That is, “Japan”, “Tokyo”, “Chiyoda-ku”, and the like are words classified into the “region” category, and “culture”, “tradition”, “festival”, etc. are the categories of “culture”. It is a word classified into. Further, “religion”, “Cathholic”, “catedral” and the like are words classified into the category of “religion”.

  Next, the RAM 13 of this embodiment will be described with reference to FIG. FIG. 11 is an explanatory diagram of a storage area of the RAM 13 according to the present embodiment. As shown in FIG. 11, in this embodiment, the RAM 13 includes an original content storage area 1301, an original author information storage area 1302, a translator information storage area 1303, a category storage area 1304, a first condition storage area 1305, a second A plurality of storage areas including a condition storage area 1306, a first candidate storage area 1307, and a second candidate storage area 1308 are provided. The original content storage area 1301, the original author information storage area 1302, and the translator information storage area 1303 are the same as the original content storage area 131, the original author information storage area 132, and the translator information storage area 133 of the first embodiment, respectively. Therefore, the description here is omitted. The category storage area 1304 stores the category number of the category to which the original text content belongs as a result of analyzing the text of the original text content as will be described later. The first condition storage area 1305 stores the first condition set according to the category into which the original text content is classified. Specifically, a condition to be considered with the highest priority is stored when determining a translator as a translation request destination. The second condition storage area 1306 stores a second condition set according to the category into which the original text content is classified. Specifically, when determining a translator as a translation request destination, a condition to be considered when one translator cannot be determined only by the first condition is stored. In the first candidate storage area 1307, the translator extracted based on the first condition is stored as the first candidate. In the second candidate storage area 1308, the translator extracted based on the second condition is stored as the second candidate.

  Below, with reference to FIGS. 12-14, the translator selection process performed in the server 10 of this embodiment is demonstrated. This process is a process of analyzing and classifying the contents of the original text content and preferentially considering a nationality or gender according to the classified category and determining a translator as a translation request destination. FIG. 12 is a flowchart of a main process for selecting a translator according to the second embodiment. FIG. 13 is a flowchart of the original content analysis process performed during the main process of FIG. FIG. 14 is a flowchart of a category-based translator determination process performed during the main process of FIG. 12 to 14 is executed by the CPU 11 in accordance with the program stored in the program storage area 141.

  When a translation content provision request is received from any of the user terminals 20, the processing shown in FIG. 12 is started. Since the process of S21-S24 after a process start is the same as the process of S1-S4 of 1st Embodiment, description here is abbreviate | omitted. When the original author's identity information is extracted as the original author information from the original text content (S24), the CPU 11 analyzes the content of the HTML document stored in the original text content storage area 1301 and performs category classification. (S30, FIG. 13). As shown in FIG. 13, in the original text content analysis process, all the counters corresponding to the category numbers 1 to 3 stored in a predetermined storage area (not shown) of the RAM 13 are set to 0 (S31). As described above, the left digit of the keyword ID stored in the keyword DB represents “1 = region”, “2 = culture”, and “3 = religion”, respectively. Accordingly, the category numbers are defined as “1 = region”, “2 = culture”, and “3 = religion” accordingly. After all the counters are set to 0, the keyword stored first is read from the keyword DB (see FIG. 10) stored in the keyword DB storage area 144, and a predetermined storage area (shown in the figure) of the RAM 13 is read. (Outside) (S32).

  Subsequently, the CPU 11 determines whether or not the read keyword is included in the HTML document as the original text content (S33). When a keyword is included (S33: YES), the number is counted (S34), and the number is counted in the category counter to which the keyword belongs, that is, the category number indicated by the one-digit number on the left of the keyword. Are added and stored (S35), and the process proceeds to S36. For example, from the keyword DB shown in FIG. 10, “Japan” with ID “110000” is read as the first keyword. Therefore, in S35, the number of “Japan” included in the original text content is added to the counter of the category number “1” indicating “region”. On the other hand, if the original keyword does not include the first keyword (S33: NO), the process proceeds to S36 as it is. In S36, it is determined whether or not the analysis processing has been completed for all keywords stored in the keyword DB. At this point, since all the keywords have not been processed yet (S36: NO), the next stored keyword is read into the RAM 13 (S37). Thereafter, the CPU 11 performs the above-described processing of S33 to S35 for the newly read keyword, and again determines whether or not the analysis processing has been completed for all keywords (S36). If the process is not completed (S36: NO), the process returns to S33 and the process is repeated in the same manner.

  When the processing of all keywords is finally completed (S36: YES), the CPU 11 sets the category of the original text content based on the value of the counter corresponding to the category numbers 1 to 3 (S38). Specifically, the category number of the counter in which the maximum value is stored is stored in the category storage area 1304 of the RAM 13 as the category number indicating the category to which the original text content belongs. For example, if the counter value of category number “2” is larger than the counter values of other category numbers, “2” is stored in category storage area 1304. That is, it means that the category to which the original content belongs is set as “culture”. When the values of a plurality of counters indicate the same maximum value, the category numbers of the plurality of counters are stored. If there is no keyword stored in the keyword DB in the original text content and the counter values of category numbers 1 to 3 remain all “0”, the category storage area 1304 stores the category. The number “0” is stored. The category number “0” indicates that the original content belongs to the “other” category that is not any of “region”, “culture”, and “religion”. When the category is set in this way, the original text content analysis process shown in FIG. 13 is completed and the process returns to the main process of FIG. 12, and then the translator determination process based on the category is performed (S50, FIG. 14).

  As shown in FIG. 14, in the category-based translator determination process, it is first determined whether or not the category to which the original text content belongs is “region” or “culture” (S51). Specifically, if at least one of category numbers “1” and “2” is stored in category storage area 1304, it is determined that the category of the original content is “region” or “culture” (S51: YES) In this case, the CPU 11 sets, as “nationality”, the first condition to be considered with the highest priority when determining one translator as a translation request destination, and stores it in the first condition storage area 1305 of the RAM 13 (S52). ). Further, the second condition to be considered when one person cannot be determined only by the first condition is set as “sex” and is stored in the second condition storage area 1306 of the RAM 13 (S52). On the other hand, if it is determined that the category of the original content is not “region” or “culture” (S51: NO), on the contrary, the first condition is “sex” and the second condition is “nationality”. The data is stored in the first condition storage area 1305 and the second condition storage area 1306 (S53). In this way, when the category of the original content is “Region” or “Culture”, the priority is given to “Nationality” in the identity information. The content related to “Region” and “Culture” This is because people in the same country have more common knowledge, and it is considered that they can translate more accurately than people in other countries. On the other hand, if the content is not related to “region” or “culture”, it is not necessary to place much emphasis on the knowledge of the country, so it is better to be a person of the same gender than a person in the same country as the original author. Prioritize.

  When the first condition and the second condition are set in S52 or S53, the CPU 11 includes the translator ID, nationality, and the translator information of all translators stored in the translator DB (see FIG. 5). The sex is acquired in the translator information storage area 1303 of the RAM 13 (S54). Then, based on the acquired translator information and the original author information acquired in the original author information storage area 1302 in S24 of FIG. 12, a translator that satisfies the first condition stored in the first condition storage area 1305 is selected. Extract as one candidate (S55). Specifically, if the original content is related to “region” or “culture” (S51: YES) and “nationality” is stored as the first condition, the translation has the same nationality as the original author All the translators are extracted as first candidates, and their translator IDs are stored in the first candidate storage area 1307 (S55). In addition, if the original content is not related to "region" or "culture" (S51: NO) and "gender" is stored as the first condition, all translators having the same gender as the original author's gender are selected. The first candidate is extracted and the translator ID is stored in the first candidate storage area 1307 (S55). Here, the method for identifying the nationality or gender from the original author information described by vCard, hCard, FOAF, etc. is the same as in the first embodiment.

  Subsequently, the CPU 11 determines whether or not the first candidate exists based on whether or not there is a translator ID stored in the first candidate storage area 1307 (S56). If there is no translator whose nationality or gender, which is the first condition, matches the original author, and no first candidate has been extracted (S56: NO), the CPU 11 stores the translator information storage area 1303. One translator ID is randomly selected from all the translator IDs stored in (S57). Here, for example, the selection may be made using a random number generation program, as in the first embodiment. Then, the CPU 11 determines the translator having the selected translator ID as a translator who officially requests translation, and stores it in the translation request list (see FIG. 6) in the translation request storage area 143 (S64). Thereafter, the translator determination process based on the category of FIG. 14 is terminated, the process returns to the main process of FIG. 12, and the main process is also terminated.

  On the other hand, when the first candidate is extracted in S55 and the translator ID is stored in the first candidate storage area 1307 (S56: YES), the CPU 11 counts the number of stored translator IDs. Based on the above, it is determined whether or not there is only one first candidate (S58). If there is only one person (S58: YES), the translator of the translator ID stored in the first candidate storage area 1307 is determined as the translator who officially requests translation, and the translation request storage area 143 It is stored in the translation request list (see FIG. 6) (S64). Thereafter, the translator determination process based on the category of FIG. 14 is terminated, the process returns to the main process of FIG. 12, and the main process is also terminated.

  When a plurality of translator IDs are stored in the first candidate storage area 1307 and there is not only one first candidate (S58: NO), the second condition is referred to by referring to the translator information storage area 1303. A first candidate that satisfies the condition is extracted as a second candidate (S59). Specifically, if the original text content is related to “region” or “culture” (S51: YES) and “gender” is stored as the second condition, the first gender having the same gender as the gender of the original author is stored. All candidates are extracted as second candidates, and their translator IDs are stored in the second candidate storage area 1308 (S59). If the original content is not related to "region" or "culture" (S51: NO) and "nationality" is stored as the second condition, a translator having the same nationality as the original author is selected. All are extracted as second candidates, and their translator IDs are stored in the second candidate storage area 1308 (S59).

  Subsequently, the CPU 11 determines whether or not there is a second candidate based on whether or not there is a translator ID stored in the second candidate storage area 1308 (S60). If there is no translator whose nationality or gender, which is the second condition, matches the original author and no second candidate has been extracted (S60: NO), the CPU 11 stores the first candidate storage area. One translator ID is selected at random from the translator IDs of all the first candidates stored in 1307 (S61). Again, one person may be selected using a random number generation program. Then, the CPU 11 determines the translator having the selected translator ID as a translator who officially requests translation, and stores it in the translation request list (see FIG. 6) in the translation request storage area 143 (S64). Thereafter, the translator determination process based on the category of FIG. 14 is terminated, the process returns to the main process of FIG. 12, and the main process is also terminated.

  On the other hand, when the second candidate is extracted in S59 and the translator ID is stored in the second candidate storage area 1308 (S60: YES), the CPU 11 determines the number of stored translator IDs. Based on the above, it is determined whether there is only one second candidate (S62). If there is only one person (S62: YES), the translator of the translator ID stored in the second candidate storage area 1308 is determined as the translator who officially requests translation, and the translation request storage area 143 It is stored in the translation request list (see FIG. 6) (S64). Thereafter, the translator determination process based on the category of FIG. 14 is terminated, the process returns to the main process of FIG. 12, and the main process is also terminated.

  When a plurality of translator IDs are stored in the second candidate storage area 1308 and there is not only one second candidate (S62: NO), all second IDs stored in the second candidate storage area 1308 are stored. One translator ID is randomly selected from the two candidate translator IDs (S63). Again, one person may be selected using a random number generation program. Then, the CPU 11 determines the translator having the selected translator ID as a translator who officially requests translation, and stores it in the translation request list (see FIG. 6) in the translation request storage area 143 (S64). Thereafter, the translator determination process based on the category of FIG. 14 is terminated, the process returns to the main process of FIG. 12, and the main process is also terminated.

  Since the processing performed by the server 10 after the translator who has requested the original content is determined is the same as that described in the first embodiment, the description thereof is omitted here.

  As described above, in the server 10 of the present embodiment, the content of the original text content is analyzed, and the original text content is classified into one of the categories “region”, “culture”, “religion”, and “others”. Is done. Then, according to the classified category, the first condition, which is the highest priority condition when selecting the translation request destination, and the second condition to be considered when one person cannot be determined only by the first condition are set. The request destination is determined based on the above conditions. The identity information includes various elements such as nationality, age, gender, religion, etc., but the important information varies depending on the content of the original content. For example, if an article introduces an event in a country in a country, people in the same country would be able to translate it properly, and if it was a teenage female blog, it would be better for a teenage female to translate it. Conceivable. Therefore, as in this embodiment, if the information used as the first condition and the second condition to be preferentially considered when selecting a translator is changed according to the contents of the original text content, a translator capable of more accurate translation can be obtained. You can choose. In this embodiment, only “nationality” and “gender” of the identity information are adopted as the first condition and the second condition depending on whether the content of the original content is related to “region” or “culture”. However, the same processing is possible even if the content of the original text content is other. For example, a modification described below can be used.

  As a modification of the second embodiment, an example in which the first condition and the second condition are changed according to whether or not the content of the original text content is related to “religion” will be described with reference to FIG. . First, in S51 of FIG. 14, it is determined whether or not “1” or “2” is included in the category number stored in the category storage area 1304, instead of “1” or “2”. As described above, the category number “3” means that the original content is related to “religion”. If the original content is related to “religion” (S51: YES), “religion” is set instead of “nationality” as the first condition, and “sex” is set as the second condition. “Nationality” is set (S52). On the other hand, if the original content is not related to “religion” (S51: NO), “nationality” is set instead of “gender” as the first condition, and “nationality” is set as the second condition. “Religion” is set (S53). Subsequent processing is the same as that described in the second embodiment. At this time, if “religion” is set in the first condition or the second condition, the judgment as to whether or not the first condition or the second condition is satisfied is based on “religion” included in the original author information and the translator This is performed depending on whether or not the “religion” included in the information is the same. Note that vCard and hCard do not have an element that defines “religion”, but FOAF can describe topics of interest to the original author and various other information. Therefore, if the original author information is identity information in the FOAF format, information relating to the religion contained therein may be used as the religion that the original author believes. Thus, according to the modification of the second embodiment, when the original text content is related to “religion”, a translator who believes in the same religion as the original author is preferentially selected. Translators who believe in the same religion as the original author have more knowledge in common with the original author than translators who believe in other religions, and are thought to have similar ways of thinking and expression. Therefore, it is possible to select a translator who can translate the original author's idea more accurately.

  In the second embodiment and its modifications, the server 10 corresponds to the “server” or “translator selection server” of the present invention, and the user terminal 20 corresponds to the “client terminal”. The translator selection process (FIG. 12) performed by the CPU 11 of the server 10 corresponds to the “translator selection method” of the present invention. The step of extracting the original author information in S23 of FIG. 12 corresponds to the “original author information acquisition step”. The step of reading the translator information in S54 of FIG. 14 corresponds to the “translator information acquisition step”. S30 in FIG. 12 and the original text content analysis process in FIG. 13 correspond to a content analysis step. The step of setting the first condition and the second condition in S52 or S53 of FIG. 14 corresponds to a “priority information setting step” and a “priority order setting step”. The translator determination process based on S50 in FIG. 13 and the category in FIG. 14 corresponds to a “translator selection step”. Further, the CPU 11 of the server 10 that extracts the original author information in S23 of FIG. 12 corresponds to “original author information acquisition means”, and the CPU 11 that reads out the translator information in S54 of FIG. 14 is “translator information acquisition means”. It corresponds to. The CPU 11 that performs the translator determination processing based on the categories 50 and 14 in FIG. 12 corresponds to “translator selection means”.

  The configuration of the translation system 1 shown in the above-described embodiment is an exemplification, and it goes without saying that the present invention can be variously modified. For example, in the first and second embodiments described above, nationality, age, gender, and religion are given as examples of identity information, but other information that represents the birth and circumstances of a person, such as family composition and school of origin. If it is.

  In the first embodiment, the maximum fitness value based on nationality is “100”, the maximum fitness value based on age is “30”, and the maximum fitness value based on gender is “50”. Yes. That is, it can be said that the priority as a condition to consider is higher in order of nationality, gender, and age. However, these orders are not limited to the example of the first embodiment, and can be changed to any order. For example, the maximum aptitude value based on nationality is “30”, the maximum aptitude value based on age is “70”, the maximum aptitude value based on gender is “50”, and in order of age, gender, nationality, The priority may be increased. In the first embodiment, the suitability value based on the nationality is set as an alternative of “100” or “0”. However, for example, “100” if the nationality is the same, “50” if the region (Asia, Western Europe, etc.) to which the country belongs is the same even if the nationality is different, and “0” if the region is different. A plurality of aptitude values may be set in stages.

  In the second embodiment, the first condition and the second condition are set based on only one criterion for determining whether the content of the original text content is related to “region” or “culture”. However, there may be a plurality of determination criteria instead of only one. For example, when the original content is not related to “region” or “culture”, it is further determined whether or not it is related to “religion”, and if it is related to “religion”, “religion” is set as the first condition. The condition may be set in two stages, such as

  Further, the first embodiment and the second embodiment can be combined. Specifically, first, the original content is acquired and analyzed as in the second embodiment. In the second embodiment, the number of keywords included in the original text content is counted, the counted number is totaled for each category to which the keyword belongs, and the category having the maximum total value belongs to the original text content. It is a category. However, in this case, the counter value of each category may be used for the calculation of the aptitude value described in the first embodiment. For example, assume that the counter value of category number 1 corresponding to “region” is “20”, the counter value corresponding to “culture” is “15”, and the counter value corresponding to “religion” is “2”. . In this case, with regard to “Region” and “Culture”, it is considered that a translator with the same nationality as the original author can translate more accurately. The total of the counter values is “35”, and “0” is calculated when the nationalities are different. With regard to “culture”, it is considered that a person with an age close to that of the original author can perform a more accurate translation. Calculate the value divided by +1). As for “religion”, if the religion is the same as the original author, it is considered that more accurate translation is possible, so if the religion is the same, the counter value “2” for “religion”, 0 "is calculated. Then, by summing the aptitude values based on the nationality, age, and religion calculated in this way, a translator having the maximum total value may be selected.

1 is a system configuration diagram showing a schematic configuration of a translation system 1. FIG. 2 is a block diagram showing an electrical configuration of a server 10. FIG. 3 is an explanatory diagram of a storage area of a RAM 13. FIG. It is explanatory drawing of an aptitude value list. It is explanatory drawing of translator information. It is explanatory drawing of a translation request list. It is a flowchart of the translator selection process which concerns on 1st Embodiment. It is a schematic diagram which shows an example of the identity information described in vCard format. It is a block diagram of the server 10 which concerns on 2nd Embodiment. It is explanatory drawing of a keyword database. It is explanatory drawing of the storage area of RAM13 which concerns on 2nd Embodiment. It is a flowchart of the main process of the translator selection which concerns on 2nd Embodiment. 13 is a flowchart of original text content analysis processing performed during the main processing of FIG. It is a flowchart of the translator determination process based on a category performed during the main process of FIG.

Explanation of symbols

1 Translation System 3 Internet 10 Server 11 CPU
13 RAM
14 Hard disk device 20 User terminal 30 Translator terminal

Claims (9)

A translator selection method processed by a server that provides content translated into a language different from the original content to a client terminal connected via a network,
An original author information acquisition step for acquiring information on the identity of the original author who is the author of the original content that is the content to be translated;
A candidate information acquisition step of acquiring information on the identity of a plurality of translator candidates;
The plurality of translators by comparing the information on the identity of the original author obtained in the original author information obtaining step with the information on the identity of the translator candidate obtained in the candidate information obtaining step. A translator selection step comprising: selecting a translator candidate having information on the identity having a higher degree of commonality with information on the identity of the original author from candidates, as a translator Method. The information on the identity includes information on nationality,
The translator selection method according to claim 1, wherein in the translator selection step, a translator candidate having the same nationality as the original author is preferentially selected. The information about the identity includes information about the age,
The translator selection method according to claim 1 or 2, wherein in the translator selection step, a translator candidate whose age is closer to that of the original author is preferentially selected. The information on the identity includes information on gender,
4. The translator selection method according to claim 1, wherein in the translator selection step, a translator candidate having the same gender as that of the original author is preferentially selected. The information about the identity includes information about religion,
5. The translator selection method according to claim 1, wherein in the translator selection step, translator candidates who believe in the same religion as the original author are preferentially selected. A content analysis step of analyzing the content of the original text content;
A priority information setting step for setting priority information, which is information to be preferentially considered when selecting the translator, from information included in the information on the identity based on the analysis result of the content analysis means; In addition,
In the translator selection step, considering the priority information set in the priority information setting step, a translator candidate having information on the identity having a higher degree of commonality with the information on the identity of the original author, 2. The translator selection method according to claim 1, wherein the translator is selected as the translator. The information on the identity includes information on at least one of nationality, age, sex, and religion,
The priority information setting step includes a priority order setting step for giving a priority to the priority information set in the priority information setting step,
In the translator selecting step, information on the identity having a higher degree of commonality with the information on the identity of the original author is considered in order from the priority information having the higher priority set in the priority setting step. The translator selection method according to claim 6, wherein a translator candidate having the translator candidate is selected as the translator. A translated content providing server that provides content translated into a language different from the original content to a client terminal connected via a network,
Original author information acquisition means for acquiring information about the identity of the original author who is the author of the original content that is the content to be translated;
Candidate information acquisition means for acquiring information on the identity of a plurality of translator candidates;
The plurality of translators by comparing the information on the identity of the original author obtained by the original author information obtaining unit with the information on the identity of the translator candidate obtained by the candidate information obtaining unit. A translation content provision comprising: a translator selection means for selecting, as a translator, a translator candidate having information on the identity having a higher degree of commonality with information on the identity of the original author from candidates server.   The translator selection program which makes a computer perform the various process steps of the translator selection method in any one of Claims 1-7.

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