Abstract:
It is a non-transitory computer readable medium storing a program causing a computer to execute a process for information processing, the process including: calculating a feature amount of each of document contents to which common attribute information is added; and generating distribution map information by plotting each of document contents in a feature amount space on the basis of the calculated feature amount.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is based on and claims priority under 35 U.S.C. 119 from Japanese Patent Application No. 2013-126383, filed on Jun. 17, 2013 and Japanese Patent Application No. 2013-203659, filed on Sep. 30, 2013. 
       BACKGROUND 
     Technical Field 
       [0002]    The present invention relates to an information processing apparatus and a non-transitory computer readable medium. 
       SUMMARY 
       [0003]    According to an aspect of the present invention, it provides a non-transitory computer readable medium storing a program causing a computer to execute a process for information processing, the process including: calculating a feature amount of each of document contents to which common attribute information is added; and generating distribution map information by plotting each of document contents in a feature amount space on the basis of the calculated feature amount. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0004]    Exemplary embodiment(s) of the present invention will be described in detail based on the following figures, wherein: 
           [0005]      FIG. 1  is a block diagram showing an example of a configuration of an information processing apparatus according to a first embodiment; 
           [0006]      FIGS. 2A and 2B  are schematic diagrams for explaining an example of distribution map generating operation; 
           [0007]      FIGS. 3A and 3B  are schematic diagrams for explaining an example of classifying operation; 
           [0008]      FIGS. 4A to 4C  are schematic diagrams for explaining an example of an operation of an information processing apparatus according to a second embodiment; 
           [0009]      FIGS. 5A and 5B  are schematic diagrams for explaining an example of an operation of an information processing apparatus according to a third embodiment; 
           [0010]      FIGS. 6A and 6B  are schematic diagrams for explaining another example of the operation of the information processing apparatus according to the third embodiment; 
           [0011]      FIG. 7  is a block diagram showing an example of a configuration of an information processing apparatus according to a fourth embodiment; 
           [0012]      FIGS. 8A to 8D  are schematic diagrams showing an example of the configuration of a question document content and an answer document content; 
           [0013]      FIGS. 9A to 9D  are schematic diagrams for explaining features of question document contents and answer document contents; 
           [0014]      FIGS. 10A to 10E  are schematic diagrams for explaining an example of classifying and learning operation of the information processing apparatus; 
           [0015]      FIGS. 11A to 11C  are schematic diagrams for explaining an example of estimating operation of the information processing apparatus; 
           [0016]      FIG. 12  is a flow chart for explaining an example of the classifying and learning operation of the information processing apparatus; 
           [0017]      FIG. 13  is a flow chart for explaining an example of the estimating operation of the information processing apparatus; 
           [0018]      FIG. 14  is a block diagram showing an example of a configuration of an information processing apparatus according to a fifth embodiment; 
           [0019]      FIG. 15  is a schematic diagram showing an example of a configuration of a question document content and answer document contents; 
           [0020]      FIGS. 16A to 16C  are schematic diagrams for explaining an example of an operation of the information processing apparatus; 
           [0021]      FIG. 17  is a flow chart for explaining an example of the operation of the information processing apparatus; and 
           [0022]      FIGS. 18A to 18E  are schematic diagrams for explaining an example of an operation of an information processing apparatus according to a sixth embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0023]    Hereinafter, collecting similar document contents to form a set may be referred to as “classifying”, and the set may be referred to as “class”. 
       First Embodiment 
     Configuration of Information Processing Apparatus 
       [0024]      FIG. 1  is a block diagram showing an example of a configuration of an information processing apparatus  1  according to a first embodiment of the present invention. 
         [0025]    The information processing apparatus  1  includes: a control unit  10  that is formed of a central processing unit (CPU) or the like and controls each unit and executes various kinds of programs; a storage unit  11  as an example of a storage device that is formed of a recording medium, such as a hard disk drive (HDD) or a flash memory, and stores information; and a communication unit  12  that communicates with an external terminal through a network. 
         [0026]    In addition, the information processing apparatus  1  is a server apparatus that receives a request from an external device connected through the communication unit  12  and a network and transmits a response to the request to the external device that is a source of the request. 
         [0027]    The control unit  10  functions as an attribute information receiving portion  100 , a feature amount calculating portion  101 , a distribution map generating portion  102 , a distance calculating portion  103 , a classifying portion  104 , a notifying portion  105 , and the like by executing an information processing program  110  that will be described later. 
         [0028]    The attribute information receiving portion  100  receives attribute information  112  including at least an attribute name, which is added to document content  111 , in response to the attribute information adding request received from the outside. The attribute information  112  indicates a name of class of the document content  111 . The document content  111  shows a content of a document. For example, the content may include question information relating to tax and answer information to the question information. The document content  111  may include text information transmitted by information communication such as an e-mail and a chat, information obtained by converting sound information into the text information, and information obtained by optically scanning a paper document or the like, for example. 
         [0029]    In response to a document content classifying request received from the outside, the feature amount calculating portion  101  performs vector representation of the documents from the document contents  111 , to which the common attribute information  112  is added, using a term frequency (TF-IDF), for example, and calculates feature amounts using multidimensional scaling (MDS). In addition, it is also possible to calculate the feature amounts using other methods. 
         [0030]    The distribution map generating portion  102  generates distribution map information  113  by plotting the document contents  111  of the same class, that is, the document contents  111  to which the common attribute information  112  is added, in a feature amount space on the basis of the feature amounts calculated by the feature amount calculating portion  101 . In addition, the dimension of the feature amount space depends on the number of types of the feature amount calculated by the feature amount calculating portion  101 . 
         [0031]    The distance calculating portion  103  calculates a distance in the feature amount space between the document content  111 , to which the common attribute information  112  is added, on the basis of the feature amounts calculated by the feature amount calculating portion  101 . 
         [0032]    The classifying portion  104  determines whether or not each of the document contents  111  to which the common attribute information  112  is added is in a correct class on the basis of the distance in the feature amount space calculated by the distance calculating portion  103 . When the classifying portion  104  determines one of the document contents  111  to which the common attribute information  112  is added is not in the correct class, the classifying portion  104  classify this document content  111  in to another class (as will hereinafter be described in detail). 
         [0033]    The notifying portion  105  notifies a source of the document content classifying request of the distribution map information  113  generated by the distribution map generating portion  102  and/or the classifying result by the classifying portion  104 . 
         [0034]    The storage unit  11  stores the information processing program  110 , the document content  111 , the attribute information  112 , the distribution map information  113 , distribution history information  114 , and the like. 
         [0035]    The information processing program  110  is a program to make the control unit  10  operate as the portions  100  to  106  described above. 
         [0036]    The distribution history information  114  is information obtained by recording the distribution map information  113  together with date and time information for a certain period. 
         [0037]    In addition, the information processing apparatus  1  is a server apparatus or a personal computer, for example. However, it is also possible to use a mobile phone, a tablet terminal, and the like. 
         [0038]    In addition, the information processing apparatus  1  may be configured to further include an operating unit and a display unit, so that the information processing apparatus  1  operates independently without using an external device. 
         [0039]    (Operation of Information Processing Apparatus) 
         [0040]    Next, an operation of the present embodiment will be divided into (1) Basic operation, (2) Distribution map generating operation, and (3) Classifying operation, and explanation of each operation will be given below. 
         [0041]    (1) Basic Operation 
         [0042]    First, in order to classify the document contents  111  of the storage unit  11 , the user of the information processing apparatus  1  operates an operating unit of a terminal device (not shown) that is connected to the information processing apparatus  1  through a network so that an attribute name is input for adding the attribute information to the document contents  111 . 
         [0043]    Then, the attribute information receiving portion  100  displays an attribute information input field on the display unit of the terminal device, and receives an input of the attribute name. As an example, it is assumed that the attribute name of “tax” is input. 
         [0044]    Then, the attribute information receiving portion  100  adds “tax”, which is the attribute name input to the attribute information input field, to the document contents  111 , and stores the “tax” in the storage unit  11  as the attribute information  112 . 
         [0045]    Then, the user requests the information processing apparatus  1  to classify the document content  111  in order to determine whether or not each of the document content  111  to which the attribute information  112  is added is in a correct class, that is, in order to determine whether or not each of the document contents  111  is added with correct attribute information  112 . 
         [0046]    In addition, not only can the user add the attribute information  112  to the document content  111 , but also the attribute information  112  may be added to the document content  111  automatically by a machine. 
         [0047]    (2) Distribution Map Generating Operation 
         [0048]      FIGS. 2A and 2B  are schematic diagrams for explaining an example of the distribution map generating operation. 
         [0049]    As shown in  FIG. 2A , for example, “tax” as attribute information  112   a  is added to document contents  111   a   1  to  111   a   n  in a set  111   a  by the user. In the present embodiment, as shown in  FIG. 2A  and subsequent drawings, the attribute information is expressed as “tag”. Type, category, industry, and so on are prepared for the attribute information  112   a.    
         [0050]    In response to the document content classifying request, the feature amount calculating portion  101  calculates the feature amount from each of the document contents  111   a   1  to  111   a   n  to which the common attribute information  112   a  is added. 
         [0051]    Then, as shown in  FIG. 2B , the distribution map generating portion  102  generates the distribution map information  13  by plotting each of the document contents  111   a   1  to  111   a   n , to which the common attribute information  112  is added, in the feature amount space on the basis of the feature amount calculated by the feature amount calculating portion  101 . 
         [0052]    In addition, although the distribution map information  113  shows the distribution of the two-dimensional space for the sake of explanation, it is also possible to show the distribution of the n-dimensional space where n&gt;2. 
         [0053]    The distance calculating portion  103  calculates distances in the feature amount space between document contents  111   a   1  to  111   a   n , to which the common attribute information  112  is added, on the basis of the feature amounts calculated by the feature amount calculating portion  101 . The distance d s  shown in  FIG. 2B  is the average of the distance between the document content  111   a   i  and each of the document contents in a set  111   a   m  which excludes the document content  111   a   i . In addition, the distance d n  may be a distance between the document content  111   a   i  and the document content in the set  111   a   m , that is closest to the document content  111   a   i  or may be a distance between the document content  111   a   m , and the document content in the set  111   a   m , that is farthest from the document content  111   a   i , and may be approximately defined. 
         [0054]    (3) Classifying Operation 
         [0055]      FIGS. 3A and 3B  are schematic diagrams for explaining an example of the classifying operation. 
         [0056]    The following is to explain an operation subsequent to “(2) Distribution map generating operation”, and this explanation will be added on the assumption that “tax” that is the common attribute information  112   a   i  is added to the document contents in the set  111   a   m  and the document content  111   a   i  by the user in “(2) Distribution map generating operation”. Referring to the distribution map information  113  shown in  FIG. 3A , the classifying portion  104  determines the document content having a distance equal to or greater than the threshold value d t  set in advance, should be in a different class on the basis of the distance d a  in the feature amount space. In other words, the classifying portion  104  determines attribute information other than “tax” corresponding to the common attribute information  112   a  should be added to the document content  111   a   i  on the basis of the distance d n  in the feature amount space. Accordingly, the classifying portion  104  determines the document contents in the set  111   a   m  to which the common attribute information  112   a  is added, and the document content  111   a   i  to which attribute information different from the attribute information  112   a  is added should be in different classes. 
         [0057]    Specifically, when the classifying result is d a &gt;d t , the classifying portion  104  determines the document contents in the set  111   a   m  and the document content  111   a   i  should be in different classes. The classifying portion  104  deletes the attribute information  112   a  from the document content  111   a   i , as shown in  FIG. 3B . This means the document content  111   a   i  is in a complementary set with respect to the set  111   a   m . In other word, since the attribute information  112   a  is not correct for the document content  111   a   i , the document content  111   a   i  is classified into a different class. The classifying portion  104  may notify the user that the attribute information  112   a  might be added incorrectly, as the classifying result, through the notifying portion  105  to be described below without deleting the attribute information  112   a . In addition, the classifying portion  104  may estimate that attribute information which is added to another document content having a short distance from the document content  111   a   i  in the feature amount space, is attribute information that should be added again to the document content  111   a   i  using the history of the past and the like. And, instead of the attribute information  112   a , the classifying portion  104  adds the estimated attribute information to the document content  111   a   i  from which the attribute information  112   a  has been deleted. Here, “adding again” refers to “adding other attribute information different from attribute information added in advance instead of the attribute information added in advance”. 
         [0058]    The notifying portion  105  notifies the user, who is a request source of the document content classifying request, of the distribution map information  113   a  generated by the distribution map generating portion  102  and/or the classifying result of the classifying portion  104 . 
         [0059]    For example, the distribution map information  113   a  may be displayed on the display unit of the user&#39;s terminal, and the document content  111   a   i  having a distance equal to or greater than the threshold value d t  set in advance may be highlighted as the classifying result and it may be displayed with a message that the attribute information  112   a  has been deleted from the document content  111   a   i . 
       Effect of the First Embodiment 
       [0060]    According to the embodiment described above, the feature amounts of the document contents in the set  111   a  to which the attribute information  112   a  is added are calculated, and the attribute information  112   a  added to one of the document contents is deleted when a distance of the one of the document contents in the feature amount space is equal to or greater than the threshold value set in advance. Therefore, by indicating the document content from which the attribute information  112   a  has been deleted, it can be presented to the user that the attribute information  112   a  added to the document content is incorrect. 
         [0061]    In addition, when there is a document content having a distance in the feature amount space equal to or greater than the threshold value set in advance, the user is notified of the presence of such document content. Therefore, it is possible to present information allowing the user to determine whether or not the attribute information  112   a  added to the document content is correct. 
         [0062]    When there is a document content having a distance in the feature amount space equal to or greater than the threshold value set in advance, the attribute information of the document content is added again. Therefore, by indicating the document content to which the attribute information has been added again, it can be presented to the user that the attribute information  112   a  added to the document content is incorrect. 
         [0063]    In short, according to the embodiment described above, it is possible to present information allowing the user to determine whether or not the attribute information  112   a  added to the document contents is correct. In addition, in the present embodiment, when there is a document content having a distance equal to or greater than the threshold value set in advance, the attribute information is deleted, and the user is notified of the situation or attribute information is added again. However, only when the distance between the document contents is relatively larger in relation to the distance between other document contents, the attribute information may be deleted, and the user may be notified of the situation or attribute information may be added again. 
         [0064]    In addition, the same effect can be obtained even if an operation is performed when there is a document content having a distance greater than the threshold value set in advance, which excludes a case that the distance equals to the threshold value set in advance as the condition of determination. In addition, it may also be notified that attribute information added to the document content having a distance less than the threshold value set in advance is correct. 
       Second Embodiment 
       [0065]    A second embodiment is different from the above in that the distribution map information described in the first embodiment is generated for plural users and then the plurality of pieces of generated distribution map information are combined and classified. 
         [0066]      FIGS. 4A to 4C  are schematic diagrams for explaining an example of the operation of the information processing apparatus  1  according to the second embodiment. 
         [0067]    As shown in  FIG. 4A , a case will be described in which common attribute information  112   a  is added to sets  111   a   A  to  111   a   C  of the document contents by plural users A to C. In addition, since the adding operation is the same as “(1) Basic operation” in the first embodiment, explanation thereof will be omitted. 
         [0068]    Next, as shown in  FIG. 4B , distribution map information  113   A  to  113   C  in the feature amount space is generated for the sets  111   a   A  to  111   a   C  of the document contents. In addition, since the generation of the distribution map information  113   A  to  113   C  is the same as “(2) Distribution map generating operation” in the first embodiment, explanation thereof will be omitted. 
         [0069]    When the distribution map information  113   A  to  113   C  is generated, if the document contents  111   a   Ai  to  111   a   Ci  has distances from the sets  111   a   Am  to  111   a   Cm  equal to or greater than the threshold value d t  set in advance, the document contents  111   a   Ai  to  111   a   Ci  are classified into different classes. 
         [0070]    Then, the distribution map generating portion  102  combines the distribution map information  113   A  to  113   C  to obtain distribution map information  113   A+B+C  as shown in  FIG. 4C . Accordingly, the document contents  111   a   Ai  to  111   a   Ci  are located at a shorter distance from a set  111   a   A+B+C  of other document contents in the feature amount space than the threshold value d t  set in advance. 
       Effect of the Second Embodiment 
       [0071]    According to the embodiment described above, when attribute information is added for the document contents separately by plural users, for example, the distribution of the document contents in the feature amount space is combined. Therefore, for a document content that might be determined to have been classified incorrectly when the number of document contents is small, the combination result helps to determine the class for the document content is not incorrect. In addition, a document content having a distance from other document contents equal to or greater than d t  even after being combined is classified as a document content that should be in a different class as indicated by the original determination. 
         [0072]    In addition, in the second embodiment described above, the classifying has been performed for the distribution map information  113   A+B+C  obtained by combining the distribution map information  113   A  to  113   C . However, the present invention is not limited thereto. When the distribution map information  113   A  to  113   C  is generated, if the distances between the document contents  111   a   Ai  to  111   a   Ci  and the sets  111   a   Ai  to  111   a   Cm  are equal to or greater than the threshold value d t  set in advance, the document contents  111   a   Ai  to  111   a   Ci  are firstly classified as document contents that should be in different classes, and classifying may be performed again for the distribution map information  113   A+B+C  obtained by combining the distribution map information  113   A  to  113   C . Also in this case, the same effect as in the second embodiment can be obtained. 
       Third Embodiment 
       [0073]    A third embodiment is different from the above in that the distribution map information described in the first embodiment is generated and temporally stored and classifying is performed according to the history of the distribution map information. 
         [0074]      FIGS. 5A and 5B  are schematic diagrams for explaining an example of the operation of the information processing apparatus  1  of the third embodiment. 
         [0075]    As shown in  FIG. 5A , a case will be described in which the distribution map generating portion  102  generates distribution map information  113   b  in the feature amount space for a set  111   b  of document contents. In addition, since the generation of the distribution map information  113   b  is the same as “(2) Distribution map generating operation” in the first embodiment, explanation thereof will be omitted. 
         [0076]    In the distribution map information  113   b , a document content  111   b   i  is assumed to be separated from a set  111   b   m  of other document contents by a distance d b  equal to or greater than the threshold value d t . In normal cases, the classifying portion  104  deletes attribute information from the document content  111   b   i  when the classifying result is d b &gt;d t . In the present embodiment, however, the attribute information is not deleted from the document content  111   b   i  for the distribution map information  113   b  firstly generated. The notification indicating that the adding of the attribute information for the document content  111   b   i  is incorrect at this timing may be executed. 
         [0077]    Then, when a document content classifying request for a set  111   b ′ of document contents (set of the document contents indicated by hatching in  FIG. 5B ), to which the common attribute information as the set  111   b  of the document contents is added, is received, the distribution map generating portion  102  generates distribution map information  113   b ′ in the feature amount space for a set obtained by combining the sets  111   b  and  111   b ′ of the document contents as shown in  FIG. 5B . 
         [0078]    In the distribution map information  113   b ′, the document content  111   b   i  is not separated from a set  111   b   ms  of the other document contents by the distance d b  equal to or greater than the threshold value d t . Accordingly, the classifying portion  104  does not delete the attribute information from the document content  111   b   i . 
         [0079]      FIGS. 6A and 6B  are schematic diagrams for explaining another example of the operation of the information processing apparatus  1  of the third embodiment. 
         [0080]    As shown in  FIG. 6A , a case will be described in which the distribution map generating portion  102  generates distribution map information  113   c  in the feature amount space for a set  111   c  of document contents to which the common attribute information is added. In addition, since the generation of the distribution map information  113   c  is the same as “(2) Distribution map generating operation” in the first embodiment, explanation thereof will be omitted. 
         [0081]    In the distribution map information  113   c , a document content  111   c   i  is assumed to be separated from a set  111   c   m  of other document contents by a distance d c  equal to or greater than the threshold value d t . In normal cases, the classifying portion  104  deletes attribute information from the document content  111   c   i  when the classifying result is d c &gt;d t . In the present embodiment, however, the attribute information is not deleted from the document content  111   c   i  for the distribution map information  113   c  firstly generated. The notification indicating that the adding of the attribute information for the document content  111   c   i  is incorrect at this timing may be executed. 
         [0082]    Then, when a document content classifying request for a set  111   c ′ of document contents (set of the document contents indicated by hatching in  FIG. 6B ), to which the common attribute information as the set  111   c  of the document contents is added, is received, the distribution map generating portion  102  generates distribution map information  113   c ′ in the feature amount space for a set obtained by combining the sets  111   c  and  111   c ′ of the document contents as shown in  FIG. 6B . 
         [0083]    In the distribution map information  113   c ′, the document content  111   c   i  is separated from a set  111   c   S1  of other document contents by the distance d c  equal to or greater than the threshold value d t , but is not separated from a set  111   c   S2  that is newly generated by the threshold value d t  or more. In addition, the sets  111   c   S1  and  111   c   S2  are separated from each other by the threshold value d i  or more. Therefore, the classifying portion  104  determines that new attribute information, which is different from the attribute information corresponding to the set  111   c   S1 , is to be added to the set  111   c   S2  of the document contents including the document content  111   c   i . 
         [0084]    When the classifying portion  104  determines that attribute information, which is different from the attribute information corresponding to the set  111   c   S1 , is to be added to the set  111   c   S2  of the document contents including the document content  111   c   i , the notifying portion  105  may notify the user, who is a request source of the document content classifying request, in order to propose to add new attribute information to the set  111   c   S2  of the document contents. 
         [0085]    In addition, the classifying portion  104  does not determine whether or not to delete the attribute information before the document content classifying request is received twice as described in  FIGS. 5A to 6B , but determines whether or not to delete the attribute information when the document content classifying request is received twice. However, the classifying portion  104  may determine whether or not to delete the attribute information when the document content classifying request is received n times where n&gt;2. Alternatively, instead of the number of times, the classifying portion  104  may determine whether or not to delete the attribute information when a certain period of time has passed. As examples of “when a certain period of time has passed”. “when a year has passed” and “every season” may be mentioned. In addition, “when an event set in advance occurs” may be set as the conditions. As an example of the event set in advance, “when a set of a certain number or more of document contents is stored” may be mentioned. In addition, the determination regarding whether or not to delete the attribute information may be also performed when instructed by the user. 
         [0086]    In addition, instead of the determination of the classifying portion  104 , the notifying portion  105  may present the user a temporal change in the distribution map information using animation or the like so that the user determines whether or not to add the attribute information. 
       Effect of the Third Embodiment 
       [0087]    According to the embodiment described above, plural distributions of document contents in the feature amount space generated temporally are combined. Therefore, for a document content classified incorrectly, it is possible to propose a new class via the combination result. 
       Fourth Embodiment 
       [0088]    A fourth embodiment is different from the first embodiment in that the document content is replaced with a combination of a question document content which is a question and an answer document content that is an answer to the question. In the fourth embodiment, the distribution map information described in the first embodiment is generated and classified for the answer document contents, and then the question document contents are classified using the classifying result and learning is performed according to the classifying result of the question document contents. In addition, an answer document content corresponding to the question document content is estimated using the learning result. 
         [0089]    (Configuration of Information Processing Apparatus) 
         [0090]      FIG. 7  is a block diagram showing an example of the configuration of an information processing apparatus  2  according to the fourth embodiment of the present invention. 
         [0091]    The information processing apparatus  2  includes: a control unit  20  that is formed of a CPU or the like and that controls each unit and executes various kinds of programs; a storage unit  21  as an example of a storage device that is formed of a recording medium, such as an HDD or a flash memory, and that stores information; and a communication unit  22  that communicates with an external terminal through a network. 
         [0092]    In addition, the information processing apparatus  2  is a server apparatus that receives a request from an external device connected through the communication unit  22  and a network and transmits a response to the request to the external device that is a source of the request. 
         [0093]    The control unit  20  functions as a feature amount calculating portion  200 , a distribution map generating portion  201 , a distance calculating portion  202 , a classifying portion  203 , a notifying portion  204 , an attribute information adding portion  205 , a learning portion  206 , a question receiving portion  207 , an attribute estimating portion  208 , an answer presenting portion  209 , and the like by executing an information processing program  210  that will be described later. 
         [0094]    In response to a classifying request of question document contents  211 , the feature amount calculating portion  200  performs vector representation of the answer document contents  212  using the term frequency (TF-IDF), for example, and calculates the feature amounts using the multidimensional scaling (MDS). In addition, it is also possible to calculate the feature amounts using other methods. 
         [0095]    Here, the question document content  211  includes text information in the form of questions, and is generated by the user who asks a question (hereinafter, referred to as a “questioner”). For example, it is possible to use text information transmitted by information communication such as an e-mail and a chat, information obtained by converting sound information into the text, and information obtained by optically scanning a paper document or the like. In addition, the answer document content  212  is generated by the user who answers the question (hereinafter, referred to as an “answerer”). The question document content  211  and the answer document content  212  are in the relationship of the answer to the question, and are a set of document contents corresponding to each other in a one-to-one manner. That is, the answerer who has generated the answer document content  212  associates the answer document content  212  with the question document content  211 . 
         [0096]    The distribution map generating portion  201  generates distribution map information  214  by plotting each of the answer document contents  212  in the feature amount space on the basis of the feature amount calculated by the feature amount calculating portion  200 . In addition, the dimension of the feature amount space depends on the number of types of the feature amount calculated by the feature amount calculating portion  200 . 
         [0097]    The distance calculating portion  202  calculates a distance in the feature amount space between the answer document contents  212  on the basis of the feature amounts calculated by the feature amount calculating portion  200 . 
         [0098]    When the distance in the feature amount space calculated by the distance calculating portion  202  is equal to or greater than a distance set in advance, the classifying portion  203  classifies the answer document contents  212  to which different attribute information  213  should be added. 
         [0099]    The notifying portion  204  notifies a request source of the classifying request of the answer document contents  212  classified by the classifying portion  203  and/or the question document contents  211  corresponding to the answer document contents  212 . 
         [0100]    The attribute information adding portion  205  receives the attribute information  213  to be added to the answer document contents  212  in the same class and/or the question document contents  211  in the same class, in response to an attribute information adding request received from the outside. The attribute information  213  includes at least an attribute name. The attribute information  213  indicates a name of class. 
         [0101]    The learning portion  206  performs learning using the question document contents  211  to which the attribute information  213  is added, thereby generating learning information  215 . In addition, the learning herein is machine learning using a support vector machine (SVM), for example. 
         [0102]    The question receiving portion  207  receives the question document content  211  (new question document content) from the outside through the communication unit  22 . 
         [0103]    The attribute estimating portion  208  estimates attribute information added to the question document content  211 , which has been received from the outside by the question receiving portion  207 , using the learning information  215 . 
         [0104]    The answer presenting portion  209  presents the answer document content  212  having the same attribute information, which is estimated by the attribute estimating portion  208 , to the transmission source of the question document content  211 . 
         [0105]    The storage unit  21  stores the information processing program  210 , the question document content  211 , the answer document content  212 , the attribute information  213 , the distribution map information  214 , the learning information  215 , and the like. 
         [0106]    The information processing program  210  is a program to make the control unit  20  operate as the portions  200  to  209  described above. 
         [0107]    In addition, the information processing apparatus  2  is a server apparatus or a personal computer, for example. However, it may be a mobile phone, a tablet terminal, and the like. 
         [0108]    In addition, the information processing apparatus  2  may be configured to further include an operating unit and a display unit, so that the information processing apparatus  2  operates independently without using an external device. 
         [0109]    (Configuration of Question Document Content and Answer Document Content) 
         [0110]      FIGS. 8A to 8D  are schematic diagrams showing an example of the configuration of the question document content  211  and the answer document content  212 . 
         [0111]    A question document content  211   a  shown in  FIG. 8A  is an example of the question document content  211 . For example, the question document content  211   a  is not asked from an expert who has exact knowledge of the field to be asked but asked from a general user (questioner) who does not have sufficient knowledge of the field to be asked. The question document content  211   a  includes the background content  2110  that is premise information of the question, information derived from the question, or the like (that may be not relevant to the question in essence) and the question content  2111  that is the essential content of the question. In addition, the background content  2110  is not always included. However, considering the question document content  211   a  is a question from the general user, the background content  2110  is often included. In addition, the question content  2111  is the essential content of the question. However, some users often fail to point out the exact content. 
         [0112]    In the above assumptions, when the feature amount calculating portion  200  calculates feature amount for the question document content  211   a  and the distribution map generating portion  201  generates distribution map information  214   a   Q  shown in  FIG. 8C  by the same operation as in the first embodiment, i) words included in the background content  2110  or ii) words of the question content  2111  that is not the exact content influence the feature amount extracted from the question document content  211   a . Accordingly, the classifying result of the question document contents  211   a  is not necessarily ideal. 
         [0113]    On the other hand, an answer document content  212   a  shown in  FIG. 8B  is generated by the expert or the like (answerer) who has exact knowledge of the field to be answered. The answer document content  212   a  includes the answer content  2120  that is the content of the answer to the essential content of the question. 
         [0114]    In the above assumptions, when the feature amount calculating portion  200  calculates feature amount for the answer document content  212   a  and the distribution map generating portion  201  generates distribution map information  214   a   A  shown in  FIG. 8D  by the same operation as in the first embodiment, exact words are included in the answer content  2120 . Accordingly, compared with the distribution map information  214   a   Q , the classifying result of the answer document contents  212   a  is ideal in many cases. 
         [0115]    In summary, the fourth embodiment uses the characteristics of the question document content  211  and the answer document content  212  described above and performs classifying of the question document content  211  using the corresponding answer document content  212 . 
         [0116]    Other characteristics of the question document content  211  and the answer document content  212  will be further described below. 
         [0117]      FIGS. 9A to 9D  are schematic diagrams for explaining the features of the question document content  211  and the answer document content  212 . 
         [0118]    Even if question document contents  211   b   1 ,  211   b   3 , and  211   b   5  and question document contents  211   b   2  and  211   b   4  are classified into different classes as shown in distribution map information  214   b   Q  shown in  FIG. 9A , answer document contents  212   b   1 ,  212   b   3 , and  212   b   5  and answer document contents  212   b   2  and  212   b   4  may be classified into the same class as shown in distribution map information  214   b   A  shown in  FIG. 9C . That is, this indicates that answers of the similar content can correspond to different questions. 
         [0119]    On the other hand, even if question document contents  211   c   1 ,  211   c   2 ,  211   c   3 ,  211   c   4 ,  211   c   7 , and  211   c   8  are classified into the same class as shown in distribution map information  214   c   Q  shown in  FIG. 9B , answer document contents  212   c   1 ,  212   c   2 , and  212   c   3  and answer document contents  212   c   4 ,  212   c   7 , and  212   c   8  may be classified into different classes as shown in distribution map information  214   c   A  shown in  FIG. 9D . That is, this indicates that answers of the different content can correspond to similar questions. 
         [0120]    In both the example shown in  FIGS. 9A and 9C  and the example shown in  FIGS. 9B and 9D  described above, the appropriate answer document content  212  can be presented by classifying the question document contents  211  on the basis of the classifying of the answer document contents  212 . 
         [0121]    (Operation of Information Processing Apparatus) 
         [0122]    Then, the operation of the present embodiment will be divided into (1) classifying and learning operation and (2) Estimating operation, and explanation of each operation will be given below. 
         [0123]    (1) Classifying and Learning Operation 
         [0124]      FIGS. 10A to 10E  are schematic diagrams for explaining an example of the classifying and learning operation of the information processing apparatus  2 .  FIG. 12  is a flow chart for explaining an example of the classifying and learning operation of the information processing apparatus  2 . 
         [0125]    First, in order to classify question document contents  211   d   1 ,  211   d   2 ,  211   d   3 , . . . shown in  FIG. 10A  and add attribute information to the question document contents  211   d   1 ,  211   d   2 ,  211   d   3 , . . . , the user who manages the information processing apparatus  2  (hereinafter, referred to as a “manager”) operates an operating unit of a terminal device (not shown), which is connected to the information processing apparatus  2  through a network, in order to firstly classify corresponding answer document contents  212   d   1 ,  212   d   2 ,  212   d   3 , . . . . In addition, the manager may be an answerer. 
         [0126]    According to the operation details of the manager, the information processing apparatus  2  acquires a set of the question document contents  211   d   1 ,  211   d   2 ,  211   d   3 , . . . and the answer document contents  212   d   1 ,  212   d   2 ,  212   d   3 , . . . , which are associated with each other, from the storage unit  21  (S 1 ). 
         [0127]    Then, the feature amount calculating portion  200  calculates the feature amount of the answer document contents  212   d   1 ,  212   d ,  212   d   3 , . . . (S 2 ). 
         [0128]    Then, as shown in  FIG. 108 , the distribution map generating portion  201  generates distribution map information  214   d  by plotting each of the answer document contents  212   d   1 ,  212   d   2 ,  212   d   3 , . . . in the feature amount space on the basis of the feature amount calculated by the feature amount calculating portion  200  (S 3 ). 
         [0129]    Then, the distance calculating portion  202  calculates a distance d d  in the feature amount space between the answer document contents  212  on the basis of the feature amounts calculated by the feature amount calculating portion  200  (S 4 ). 
         [0130]    When the distance in the feature amount space calculated by the distance calculating portion  202  is equal to or greater than a distance set in advance, as shown in  FIG. 10C , the classifying portion  203  classifies the answer document contents  212   d   1  to  212   d   5  and the answer document contents  212   d   6  to  212   d   8  to which different pieces of attribute information  213  should be added (S 5 ). 
         [0131]    The notifying portion  204  notifies the terminal device, which is a request source of the classifying request, of the answer document contents  212   d   1  to  212   d   5  and the answer document contents  212   d   6  to  212   d   8  classified by the classifying portion  203  (S 6 ). 
         [0132]    The user of the information processing apparatus  2  checks the content sent to the terminal device, and performs an operation to add attribute information  213   d , and attribute information  213   d   2  to the answer document contents  212   d   1  to  212   d   5  and the answer document contents  212   d   6  to  212   d   8 , respectively. 
         [0133]    The attribute information adding portion  205  receives an attribute information adding request from the terminal device (S 7 ), and adds the attribute information  213   d   1  to the answer document contents  212   d   1  to  212   d   5  and the attribute information  213   d   2  to the answer document content  212   d   6  to  212   d   8  as shown in  FIG. 10C  in response to the attribute information adding request (S 8 ). 
         [0134]    In addition, as shown in  FIG. 10D , the attribute information adding portion  205  similarly adds the attribute information  213   d   1  and the attribute information  213   d   2  to the question document contents  211   d   1  to  211   d   5  and the question document contents  211   d   6  to  211   d   8  corresponding to the answer document content  212   d   1  to  212   d   5  and the answer document content  212   d   6  to  212   d   5  (S 8 ). 
         [0135]    Then, the learning portion  206  generates the learning information  215  by performing learning using the question document contents  211   d   1  to  211   d   5  and the question document contents  211   d   6  to  211   d   8  to which the attribute information  213   d   1  and the attribute information  213   d   2  are added as shown in  FIG. 10D  (S 9 ). 
         [0136]    Then, as shown in  FIG. 10E , on the basis of the operation of the manager, answer document content  212   d   a  obtained by generalizing the answer document contents  212   d   1  to  212   d   5  and answer document content  212   d   b  obtained by generalizing the answer document contents  212   d   6  to  212   d   8  are generated and associated with the attribute information  213   d   1  and the attribute information  213   d   2 , respectively. In addition, the answer document content  212   d   a  and the answer document content  212   d   b  may be automatically generated by extracting the common words from the answer document contents  212   d   1  to  212   d   5  and the answer document contents  212   d   6  to  212   d   8 . 
         [0137]    (2) Estimating Operation 
         [0138]      FIGS. 11A to 11C  are schematic diagrams for explaining an example of the estimating operation of the information processing apparatus  2 .  FIG. 13  is a flow chart for explaining an example of the estimating operation of the information processing apparatus  2 . 
         [0139]    First, in order to ask a question to the information processing apparatus  2 , a questioner generates a question document content  211   d   m  (new question document content) shown in  FIG. 11A , and transmits the question document content  211   d   m  to the information processing apparatus  2  from another terminal device (not shown) connected to the information processing apparatus  2  through a network. 
         [0140]    The question receiving portion  207  of the information processing apparatus  2  receives the question document content  211   d   m  from the questioner (S 20 ). In addition, the question document content  211   d   m  is not associated with the answer document content  212 . 
         [0141]    Then, the attribute estimating portion  208  estimates attribute information to be added to the question document content  211   d   m  using the learning information  215 , and adds the attribute information  213   d   2  as shown in  FIG. 11B  (S 21 ). 
         [0142]    Then, the answer presenting portion  209  presents the answer document content  212   d   b  associated with the attribute information  213   d   2  by displaying the answer document content  212   d   b  as an answer to the question document content  211   d   m  on the terminal device that the questioner uses, as shown in  FIG. 11C  (S 22 ). In addition, instead of being directly displayed on the terminal device that the questioner uses, the answer document content  212   d   b  may be presented to the questioner after being displayed on the terminal device that the answerer uses so that the answerer checks or modifies the answer document content  212   d   b . 
       Effect of the Fourth Embodiment 
       [0143]    According to the embodiment described above, the corresponding question document content  211  is classified using the answer document content  212  having the answer content  2120  that is the content of the answer to the essential content of the question. Therefore, even if the question document content  211  does not necessarily have the essential content of the question, it is possible to obtain the classifying result that is not influenced by the feature amount in which words included in the background content  2110  of the question document content  211  or words of the question content  2111  that is not exact content are extracted, compared with a case where the question document content  211  is directly classified. 
         [0144]    In addition, the attribute information  213  is added to the question document content  211  classified by the answer document content  212 , the learning information  215  is generated by performing learning for a set of question document contents  211  and attribute information  213  by the learning portion  206 , and the answer document content  212  to be presented is specified by attribute estimation using the learning information  215 . Therefore, it is possible to present an answer according to the classifying of the answer document contents  212 . 
       Fifth Embodiment 
       [0145]    In a fifth embodiment, a determination content as a point of view when the answerer generates answer document content is added as an answer to the question document content of the fourth embodiment. In addition, an answer document content is classified using the determination content, and attribute information is added according to the classifying. 
         [0146]    (Configuration of Information Processing Apparatus) 
         [0147]      FIG. 14  is a block diagram showing an example of the configuration of an information processing apparatus  3  according to the fifth embodiment of the present invention. 
         [0148]    The information processing apparatus  3  includes: a control unit  30  that is formed of a CPU or the like and that controls each unit and executes various kinds of programs; a storage unit  31  as an example of a storage device that is formed of a recording medium, such as an HDD or a flash memory, and that stores information; and a communication unit  32  that communicates with an external terminal through a network. 
         [0149]    In addition, the information processing apparatus  3  is a server apparatus that receives a request from an external device connected through the communication unit  32  and a network and transmits a response to the request to the external device that is a source of the request. 
         [0150]    The control unit  30  functions as a determination content adding portion  300 , a determination content classifying portion  301 , a feature amount calculating portion  302 , a distribution map generating portion  303 , a distance calculating portion  304 , a classifying portion  305 , a determination content notifying portion  306 , an attribute information adding portion  307 , and the like by executing an information processing program  310  that will be described later. 
         [0151]    The determination content adding portion  300  adds a determination content  314  by associating the point of view when generating a answer document content  312  for a question document content  311  with the question document content  311  and the answer document content  312 . 
         [0152]    When there are plural combinations of the question document contents  311  and the answer document contents  312 , the determination content classifying portion  301  classifies the combinations of the question document contents  311  and the answer document contents  312  into plural sets on the basis of the determination content  314  added to the question document contents  311  and the answer document contents  312 . 
         [0153]    The feature amount calculating portion  302  performs vector representation of answer document contents  312  included in each set using the term frequency (TF-IDF), for example, and calculates the feature amount using the multidimensional scaling (MDS). In addition, it is also possible to calculate the feature amount using other methods. 
         [0154]    The distribution map generating portion  303  generates distribution map information  315  by plotting each of the answer document contents  312  included in each set in the feature amount space on the basis of the feature amount calculated by the feature amount calculating portion  302 . In addition, the dimension of the feature amount space depends on the number of types of the feature amount calculated by the feature amount calculating portion  302 . 
         [0155]    The distance calculating portion  304  calculates a distance in the feature amount space between the plurality of pieces of answer document content  312  included in each set on the basis of the feature amount calculated by the feature amount calculating portion  302 . 
         [0156]    The classifying portion  305  classifies the answer document contents  312  included in each set on the basis of the distance in the feature amount space calculated by the distance calculating portion  304 . 
         [0157]    The determination content notifying portion  306  notifies the writer, that is, the answerer(s) of the answer document contents  312  of the determination content of the set in which the answer document contents  312  in the feature amount space are classified with good separation by the classifying portion  305 , as recommended determination content. 
         [0158]    The attribute information receiving portion  307  receives the attribute information  313  added to the answer document contents  312  in the same class or the question document content  311  in the same class, in response to the attribute information request received from the outside. The attribute information  313  includes at least an attribute name. The attribute information  313  indicates a name of class. 
         [0159]    The storage unit  31  stores the information processing program  310 , the question document content  311 , the answer document content  312 , the attribute information  313 , the determination content  314 , the distribution map information  315 , and the like. 
         [0160]    The information processing program  310  is a program to make the control unit  30  operate as the portions  300  to  307  described above. 
         [0161]    In addition, the information processing apparatus  3  is a server apparatus or a personal computer, for example. However, it may be a mobile phone, a tablet terminal, and the like. 
         [0162]    In addition, the information processing apparatus  3  may be configured to further include an operating unit and a display unit, so that the information processing apparatus  3  operates independently without using an external device. 
         [0163]    (Configuration of Question Document Content and Answer Document Content) 
         [0164]      FIG. 15  is a schematic diagram showing an example of the configuration of the question document content  311  and the answer document content  312 . 
         [0165]    A question document content  311   e  shown in Part (a) of  FIG. 15  is an example of the question document content  311 . For example, the question document content  311   e  is not asked from an expert who has exact knowledge of the field to be asked but from a general user (questioner) who does not have sufficient knowledge of the field to be asked. The question document content  311   e  includes the background content  3110  that is the premise information of the question, information derived from the question, or the like (that is not relevant to the question in essence) and the question content  3111  that is the essential content of the question. In addition, the background content  3110  is not always included. However, considering the question document content  311   e  is a question from the general user, the background content  3110  is often included. In addition, the question content  3111  is the essential content of the question. However, some users often fail to point out the exact content. 
         [0166]    On the other hand, answer document contents  312   e   A  and  312   e   B  shown in Part (b) and Part (c) of  FIG. 15  is generated by the expert or the like (answerers  5 A and  5 B) who have exact knowledge of the field to be answered. The answer document contents  312   e   A  and  312   c   B  include the answer content  3121  and  3122 , respectively, which is the content of the answer to the essential content of the question. 
         [0167]    However, even if the answerers  5 A and  5 B are experts who have exact knowledge of the field to be answered, when the question content  3111  does not show the exact content, the answerers  5 A and  5 B may generate the answer document contents  312   e   A  and  312   e   B  from different points of view. That is, the answer document content  312   e   A  has been generated by the answerer  5 A from the point of view of “area”, and the answer document content  312   e   B  has been generated by the answerer  5 B from the point of view of “industry”. 
         [0168]    In the above assumptions, it is not preferable that the points of view of the answerer when generating the answer document content  312 , that is, the determination contents  314  are different, and it is preferable for the questioner that the answer document content  312  be generated in the same point of view. Therefore, an operation to present the optimal determination content  314  to answerers when there are determination contents  314  so that the answerers generate the answer document contents  312  from the common point of view, will be described below. 
         [0169]    (Operation of Information Processing Apparatus) 
         [0170]    Then, an operation of the present embodiment will be described. 
         [0171]      FIGS. 16A to 16C  are schematic diagrams for explaining an example of the operation of the information processing apparatus  3 .  FIG. 17  is a flow chart for explaining an example of the operation of the information processing apparatus  3 . 
         [0172]    First, as shown in  FIG. 16A , the answerer generates answer document contents  312   f   1 ,  312   f   2 ,  312   f   3 , . . . for question document contents  311   f   1 ,  311   f   2 ,  311   f   3 , . . . , and operates an operating unit of a terminal device (not shown), which is connected to the information processing apparatus  3  through a network, in order to associate a determination content  314   f   1  or  314   f   2  as a point of view when generating the answer document contents  312   f   1 ,  312   f   2 ,  312   f   3 , . . . . In addition, the same attribute information  313   f   1  is added to the question document contents  311   f   1 ,  311   f   2 ,  311   f   3 , . . . and the answer document contents  312   f   1 ,  312   f   2 ,  312   f   3 , . . . . 
         [0173]    According to the operation details of the answerer, the determination content adding portion  300  of the information processing apparatus  3  adds a determination content  314   f   1  to the question document contents  311   f   1  to  311   f   8  and the answer document content  312   f   1  to  312   f   8 , and adds the determination content  314   f   2  to the question document content  311   f   11  to  311   f   18  and the answer document content  312   f   11  to  312   f   18 . 
         [0174]    When there are sets of the question document contents  311 , the answer document contents  312 , and the determination contents  314  described above, the determination content classifying portion  301  acquires the sets (S 30 ), and classifies the question document contents  311  and the answer document contents  312  into plural sets according to the determination contents  314  (S 31 ). That is, in the example described above, the sets of the question document contents  311  and the answer document contents  312  are classified into a set (corresponding to the determination content  314   f   1 ) of the question document contents  311   f   1  to  311   f   8  and the answer document contents  312   f   1  to  312   f   8  and a set (corresponding to the determination content  314   f   2 ) of the question document contents  311   f   11  to  311   f   16  and the answer document contents  312   f   11  to  312   f   18 . 
         [0175]    Then, the feature amount calculating portion  302  calculates the feature amounts of the answer document contents  312   f   1  to  312   f   8  and the answer document contents  312   f   11  to  312   f   18  (S 32 ). 
         [0176]    Then, as shown in  FIGS. 16B and 16C , the distribution map generating portion  303  generates distribution map information  315   f   1  and  315   f   2  by plotting each of the answer document contents  312   f   1  to  312   f   8  and the answer document contents  312   f   11  to  312   f   18  in the feature amount space for each of the determination contents  314   f   1  and  314   f   2  on the basis of the feature amount calculated by the feature amount calculating portion  302  (S 33 ). 
         [0177]    Then, the distance calculating portion  304  calculates distances df 1  and df 2  in the feature amount space between the answer document contents  312   f   1  to  312   f   8  and the answer document contents  312   f   11  to  312   f   18  on the basis of the feature amounts calculated by the feature amount calculating portion  302  (S 34 ). 
         [0178]    Then, when the distances in the feature amount space calculated by the distance calculating portion  304  is equal to or greater than a distance set in advance, as shown in FIGS.  16 B and  16 C, the classifying portion  305  classifies the answer document contents  312   f   1  to  312   f   8  and the answer document contents  312   f   11  to  312   f   18  (S 35 ). 
         [0179]    Then, the determination content notifying portion  306  compares and evaluates the distribution map information  315   f   1  and  315   f   2 , and notifies another terminal device used by the answerer(s) of the determination content  314   f   2  of the distribution map information  315   f   2  revealing good classifying separation as a remarkable point of view when generating an answer document content (S 36 ). Specifically, when the distance df 2  calculated by the distance calculation portion  304  is equal to or greater than a distance set in advance, the distribution map information  315   f   2  for the determination content  314   f   2  is deemed to reveal good classifying separation. Similarly, when the distance df, calculated by the distance calculation portion  304  is equal to or greater than the distance set in advance, the distribution map information  315   f   1  for the determination content  314   f   1  is deemed to reveal good classifying separation. And, the determination content  314   f   1  or  314   f   2  corresponding to longer one of the distance df 1  and df 2  is deemed to be the remarkable point of view. In addition, when there are plural distances equal to or greater than the distance set in advance, the determination content notifying portion  306  may notifies the determination contents in accordance with a descending order of the distances. In addition, the notification timing may be a timing when the answerer receives the answer document content  312  estimated that the attribute information  313   f   1  is added thereto, or may be an arbitrary timing requested by the answerer. 
       Effect of the Fifth Embodiment 
       [0180]    According to the embodiment described above, sets of question document contents  311  and answer document contents  312  are classified using the determination contents  314  associated with the question document contents  311  and the answer document contents  312 , the distribution map information  315  is generated for each of the determination contents  314  and the determination contents  314  are evaluated on the basis of the excellence of classifying separation of the answer document contents  312 , and the determination content  314  of the distribution map information  315  with good classifying separation is sent to another terminal device used by the answerer as a remarkable point of view when generating an answer document content. Therefore, it is possible to generate the answer document content  312  from the same point of view shared between a plurality of answerers. 
       Sixth Embodiment 
       [0181]    In a sixth embodiment, in the same configuration as in the fifth embodiment, the concept of the determination content  314  is subdivided, and the concept of the answer document content  312  is subdivided using the subdivision of the determination content  314 . 
         [0182]    (Operation of Information Processing Apparatus) 
         [0183]    Then, an operation of the present embodiment will be described. 
         [0184]      FIGS. 18A to 18E  are schematic diagrams for explaining an example of the operation of the information processing apparatus  3  of the sixth embodiment. 
         [0185]    First, as shown in  FIG. 18A , the answerer generates answer document contents  312   g   1 ,  312   g   2 ,  312   g   3 , . . . , and  312   g   6  for question document contents  311   g   1 ,  311   g   2 ,  311   g   3 , . . . , and  311   g   6  and operates an operating unit of a terminal device (not shown), which is connected to the information processing apparatus  3  through a network, in order to associate a determination content  314   g   1  (industry) as a point of view when generating the answer document content  312   g   1 ,  312   g   2 ,  312   g   3 , . . . , and  312   g   6 . In addition, the same attribute information  313   g   1  is added to the question document contents  311   g   1 ,  311   g   2 ,  311   g   3 , . . . , and  311   g   6  and the answer document content  312   g   1 ,  312   g   2 ,  312   g   3 , . . . , and  312   g   6 . 
         [0186]    According to the operation details of the answerer, the determination content adding portion  300  of the information processing apparatus  3  adds the determination content  314   g   1  to the question document contents  311   g   1  to  311   g   6  and the answer document contents  312   g   1  to  312   g   6 . 
         [0187]    Then, as shown in  FIG. 18B , the answerer operates an operating unit of the terminal device, which is connected to the information processing apparatus  3  through a network, in order to associate determination content  314   g   11  (industry A) and determination content  314   g   12  (industry B), which are further subdivided from the determination content  314   g , (industry), as a point of view when generating the question document contents  311   g   1  to  311   g   6  and the answer document contents  312   g   1  to  312   g   6 . For example, a case will be described in which the answerer associates the determination content  314   g   11  (industry A) as a point of view when generating the answer document content  312   g   3  and associates the determination content  314   g   12  (industry B) as a point of view when generating the answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6 . 
         [0188]    According to the operation details of the answerer, the determination content adding portion  300  of the information processing apparatus  3  adds the determination content  314   g   11  to the question document content  311   g   3  and the answer document content  312   g   3 , and adds the determination content  314   g   12  to the question document contents  311   g   1 ,  311   g   2 , and  311   g   4  to  311   g   6  and the answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6 . 
         [0189]    When there are sets of the question document contents  311 , the answer document content  312 , and the determination contents  314  described above, the determination content classifying portion  301  acquires the sets and classifies the set of question document contents  311  and answer document contents  312  into plural sets according to the determination contents  314  as in the fifth embodiment. That is, in the example described above, the sets of the question document contents  311  and the answer document contents  312  are classified into a set (corresponding to determination content  314   g   11 ) of question document content  311   g   3  and answer document content  312   g   3  and a set (corresponding to determination content  314   g   12 ) of question document contents  311   g   1 ,  311   g   2 , and  311   g   4  to  311   g   6  and answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6 . 
         [0190]    Then, the feature amount calculating portion  302  calculates the feature amounts of the answer document content  312   g   3  and the answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6  (S 32 ). 
         [0191]    Then, as shown in  FIG. 18C , the distribution map generating portion  303  generates distribution map information  315   g   11  and  315   g   12  by plotting each of the answer document content  312   g   3  and answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6  in the feature amount space for each of the determination contents  314   g   11  and  314   g   12  on the basis of the feature amount calculated by the feature amount calculating portion  302  (S 33 ). 
         [0192]    Then, the distance calculating portion  304  calculates a distance d 12  in the feature amount space between the answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6  on the basis of the feature amounts calculated by the feature amount calculating portion  302  (S 34 ). In addition, for the answer document content  312   g   3 , distance calculation is not performed since there is only the answer document content  312   g   3 . 
         [0193]    Then, when the distance d 12  in the feature amount space calculated by the distance calculating portion  304  is equal to or greater than a distance set in advance, as shown in  FIG. 18D , the classifying portion  305  classifies the answer document contents  312   g   1 ,  312   g   2 , and  312   g   4  to  312   g   6  into a set of the answer document contents  312   g   1  and  312   g   2  and a set of the answer document contents  312   g   4  to  312   g   6 . 
         [0194]    Then, the determination content notifying portion  306  notifies the answerer(s) of the aforementioned classifying result, so that attribute information further subdivided from the attribute information  313   g   1  is to be added for each set. 
         [0195]    As shown in  FIG. 18D , the answerer adds attribute information  313   g   11  to the question document content  311   g   3  and the answer document content  312   g   3 , adds attribute information  313   g   12  to the question document contents  311   g   1  and  311   g   2  and the answer document contents  312   g   1  and  312   g   2 , and adds attribute information  313   g   12 ′ to the question document contents  311   g   4  to  311   g   6  and the answer document contents  312   g   4  to  312   g   6 . 
         [0196]    The relationship between the subdivided attribute information  313   g   11 ,  313   g   12 , and  313   g   12 ′ and the attribute information  313   g   1  is shown in  FIG. 18E , and the information processing apparatus  3  stores a tree structure as additional information of the attribute information  313 . In addition, the relationship between the subdivided determination contents  314   g   11  and  314   g   12  and the determination contents  314   g   1  is also the tree structure similar to the above, and this structure may be presented to the answerer when generating answer document content so that the answerer selects appropriate determination content. 
         [0197]    In addition, as in the fourth embodiment, the learning portion  206  may generate the learning information  215  by learning the question document content  311   g   3  to which the subdivided attribute information  313   g   11  is given, the question document contents  311   g   1  and  311   g   2  to which the subdivided attribute information  313   g   12  is given, and the question document content  311   g   4  to  311   g   6  to which the subdivided attribute information  313   g   12 ′ is given. 
       Effect of the Sixth Embodiment 
       [0198]    According to the embodiment described above, the determination content  314  is subdivided to classify the sets of the question document contents  311  and the answer document contents  312 , and the distribution map information  315  is generated for each subdivided determination content  314  and is further classified. Therefore, the subdivided attribute information  313  can be added to the question document contents  311  and the answer document contents  312 . In addition, since the structure of the subdivided determination contents  314  is presented to the answerer, the answerer can select the appropriate subdivided determination content  314  according to the structure. 
         [0199]    In addition, in the case of classifying document contents by plural users as in the second embodiment, when the present embodiment is applied so that document contents are classified by subdividing the determination content  314  and the attribute information  313  by a plurality of answerers (it is assumed that the users A to C in  FIG. 4A  are answerers A to C and the document contents  111   a   A  to  111   a   C  are answer document content  111   a   A  to  111   a   C ), the determination content  314  and the attribute information  313  that are not subdivided may be adopted without adopting the determination content  314  and the attribute information  313  that are subdivided by each answerer if document content is not classified when pieces of answer document contents of a plurality of answerers are combined (refer to  FIG. 4C ) even if the determination content  314  and the attribute information  313  subdivided by each answerer are added (refer to  FIG. 4B ). 
       Other Embodiments 
       [0200]    Although each function of the portions  100  to  105  in the control unit  10  is realized by the program in the embodiments described above, all or a part of the portions may be realized by hardware, such as an ASIC. In addition, the program used in the embodiments described above may be provided in a state recorded on a recording medium, such as a CD-ROM. In addition, replacement, deletion, addition, and the like of the above steps described in the above embodiments are possible within the scope that does not change the spirit of the present invention. 
         [0201]    The foregoing description of the exemplary embodiments of the present invention has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations will be apparent to practitioners skilled in the art. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, thereby enabling others skilled in the art to understand the invention for various embodiments and with the various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the following claims and their equivalents.