Patent Publication Number: US-11023474-B2

Title: Search method and search device

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2018-91663, filed on May 10, 2018, the Japanese Patent Application No. 2017-199753, filed on Oct. 13, 2017, and the Japanese Patent Application No. 2018-2822, filed on Jan. 11, 2018, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiments discussed herein are related to a search method and a search device. 
     BACKGROUND 
     A system that displays search results based on a query input by a user on a radar chart is known in the related art. In the system of the related art, for example, when the radar chart is displayed, it is known that axis items (word) are extracted from a set of search results based on the query input by the user. 
     Japanese Laid-open Patent Publication No. 2008-003869 is an example of the related art. 
     SUMMARY 
     According to an aspect of the embodiments, an apparatus receives, via an input device, query input data including a word or a phrase, and acquires search result set data using the query input data. The apparatus acquires, for a value indicating a strength of a relationship between each impression word included in an impression word group and each word included in the query input data, and extracts the first feature word group according to the value indicating the strength of the relationship with each word, from the impression word group. The apparatus displays the search result set data using the first feature word group as an item. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram that illustrates an example of a system configuration of a search system of a first embodiment; 
         FIG. 2  is a diagram that illustrates an example of a hardware configuration of a search device of the first embodiment; 
         FIG. 3  is a diagram that describes functions of the search device of the first embodiment; 
         FIG. 4  is a flowchart that describes processing by an index generation unit; 
         FIGS. 5A and 5B  are example tables of index information generated by the index generation unit; 
         FIG. 6  is an example table of an impression word map of the first embodiment; 
         FIG. 7  is a flowchart that describes processing by an impression space learning unit; 
         FIG. 8  is an example table of an impression word score list of the first embodiment; 
         FIG. 9  is a flowchart that describes processing of a search processing unit of the first embodiment; 
         FIG. 10  is a flowchart that describes processing of an impression word score output unit and an axis determination unit of the first embodiment; 
         FIG. 11  is a diagram describing processing of the impression word score output unit and the axis determination unit; 
         FIG. 12  is a first diagram illustrating a display example of a radar chart of the first embodiment; 
         FIG. 13  is a diagram illustrating an example of a radar chart as a comparative example; 
         FIG. 14  is a second diagram that illustrates a display example of a radar chart of the first embodiment; 
         FIG. 15  is a diagram describing functions of a search device of a second embodiment; 
         FIG. 16  is a flowchart that describes processing of a search processing unit of the second embodiment; 
         FIG. 17  is a flowchart describing processing of an axis determination unit of the second embodiment; 
         FIGS. 18A and 18B  are graphs describing variation in scores in an impression word map of a third embodiment; 
         FIG. 19  is a diagram describing functions of a search device of the third embodiment; 
         FIG. 20  is a table illustrating an example of the impression word map of the third embodiment; 
         FIG. 21  is an example table of an impression word score list of the third embodiment; 
         FIG. 22  is a flowchart describing processing of a search processing unit of the third embodiment; 
         FIG. 23  is a flowchart describing processing of an impression word score output unit and an axis determination unit of the third embodiment; 
         FIG. 24  is a diagram describing extraction of a candidate axis by the search processing unit of the third embodiment; 
         FIG. 25  is a first diagram illustrating a display example of the third embodiment; 
         FIG. 26  is a second diagram illustrating a display example of the third embodiment; 
         FIG. 27  is a diagram describing functions of a search device of a fourth embodiment; 
         FIG. 28  is a flowchart describing axis change processing in the fourth embodiment; 
         FIGS. 29A to 29C  are diagrams describing change of axis; 
         FIG. 30  is a diagram describing functions of a search device of a fifth embodiment; 
         FIG. 31  is a flowchart describing processing of a correction list generation unit of the fifth embodiment; 
         FIG. 32  is a diagram illustrating an example of a correction list of the fifth embodiment; 
         FIG. 33  is a flowchart describing processing of an impression word score output unit, an axis determination unit, and a score correction unit of the fifth embodiment; 
         FIG. 34  is a diagram describing an outline of a sixth embodiment; 
         FIGS. 35A and 35B  are diagrams describing change in score of impression words; 
         FIG. 36  is a diagram describing functions of a search device of the sixth embodiment; 
         FIG. 37  is a first flowchart describing processing of a search processing unit of the sixth embodiment; 
         FIG. 38  is a second flowchart describing processing of the search processing unit of the sixth embodiment; 
         FIG. 39  is a third flowchart describing processing of the search processing unit of the sixth embodiment; 
         FIGS. 40A and 40B  are diagrams describing correspondence between a score and a slider value of the sixth embodiment; 
         FIG. 41  is an example correspondence table of the sixth embodiment; 
         FIG. 42  is a diagram illustrating a display example of the sixth embodiment; 
         FIGS. 43A and 43B  are diagrams illustrating another display example of the sixth embodiment; 
         FIG. 44  is a graph describing correspondence between a score and a slider value of a seventh embodiment; 
         FIG. 45  is a flowchart describing processing of a search processing unit of the seventh embodiment; and 
         FIG. 46  is an example correspondence table of the seventh embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the related art, since the axis words of the radar chart are extracted from the set of search results, there may be a situation in which the correspondence between the query and axes is not clear despite the fact that the radar chart is displayed with appropriate axes, depending on the query input by a user. In such a situation, it is difficult for the user to promptly evaluate whether or not the search results meet the intention of the user. 
     It is desirable to properly evaluate search results. 
     First Embodiment 
     Hereinafter, the first embodiment will be described with reference to the drawings.  FIG. 1  is a diagram that illustrates an example of a system configuration of a search system of a first embodiment. 
     A search system  100  of the present embodiment includes a search device  200  and a terminal device  300 . The search device  200  and the terminal device  300  are connected via a network. The search device  200  of the present embodiment communicates with a search target document database  400  in which information to be searched by the search device  200  is stored. The information stored in the search target document database  400  of the present embodiment is, for example, text data such as document data. 
     The search target document database  400  of the present embodiment may include, for example, a plurality of databases. The search target document database  400  of the present embodiment may be included in the search system  100 . 
     When a query is input by a user in the terminal device  300 , the search device  200  of the present embodiment searches the search target document database  400  based on the input query. The search device  200  causes the terminal device  300  to display the search result acquired as a result of the search processing by a radar chart. The query indicates, for example, a word or a phrase (compound word) input when the user of the terminal device  300  or the search device  200  performs a search request. 
     At this time, the search device  200  of the present embodiment displays a radar chart with words extracted based on the query input from the terminal device  300  as axes. 
     In the present embodiment, it is possible to make the correspondence between the query and the axis of the radar chart clear by using the words extracted based on the query input by the user as axes of the radar chart. Therefore, in the present embodiment, it is possible to allow the user to easily evaluate whether or not the search result matches the intention of the user. According to the present embodiment, it is possible to properly evaluate the search result. 
     The search device  200  of the present embodiment includes a search target index storage unit  210 , an impression word map storage unit  220 , a search target score storage unit  230 , and a search processing unit  240 . 
     The search target index storage unit  210  stores index information in which each document data stored in the search target document database  400  is associated with words included in each document data. The impression word map storage unit  220  stores an impression word map indicating a relationship between a word and an impression word. The search target score storage unit  230  stores an impression word score list indicating the relationship between the words and impression words included in each document data on the document data included in the search target document database  400 . Details of each storage unit will be described later. 
     When the search processing unit  240  receives input of a query from the terminal device  300 , the search processing unit  240  searches the search target document database  400  with reference to the search target index storage unit  210 , and acquires document data as a search result. In the following description, the search result acquired by the search processing of the search processing unit  240  is referred to as search result set data. 
     The search processing unit  240  selects impression words each having a strong relationship with the content of the query as the axes of the radar chart with reference to the impression word map storage unit  220 , and causes the terminal device  300  to display the radar chart for each search result set data. 
     In the example of  FIG. 1 , the query is input from the terminal device  300  to the search device  200 , and the radar chart is displayed on the terminal device  300 , but not limited thereto. The query may be input by an input device of the search device  200 , and the radar chart may be displayed on an output device (display) or the like of the search device  200 . 
     Hereinafter, the search device  200  of the present embodiment will be described.  FIG. 2  is a diagram that illustrates an example of a hardware configuration of a search device of the first embodiment. 
     The search device  200  of the present embodiment is an information processing device including an input device  21 , an output device  22 , a drive device  23 , an auxiliary storage device  24 , a memory device  25 , an arithmetic processing device  26 , and an interface device  27  mutually connected via a bus B. 
     The input device  21  is a device to input various kinds of information, and is realized by, for example, a keyboard, a pointing device, or the like. The output device  22  is a device to output various kinds of information, and is realized by, for example, a display or the like. The interface device  27  includes a LAN card or the like, and is used to be connected to a network. 
     A search program is at least a part of various programs that control the search device  200 . The search program is provided by, for example, distribution of a storage medium  28 , download from a network, or the like. Various types of storage media may be used as the storage medium  28  in which the search program is recorded; a storage medium that optically, electrically, or magnetically records information, such as a CD-ROM, a flexible disk, or a magneto-optical disk, a semiconductor memory that electrically records information, such as a ROM or a flash memory, or the like. 
     When the storage medium  28  in which the search program is recorded is set in the drive device  23 , the search program is installed from the storage medium  28  to the auxiliary storage device  24  via the drive device  23 . The search program downloaded from the network is installed in the auxiliary storage device  24  via the interface device  27 . 
     The auxiliary storage device  24  stores significant files, data, and the like while storing the installed search program. The memory device  25  reads and stores the search program from the auxiliary storage device  24  when the search device  200  is activated. The arithmetic processing device  26  realizes various types of processing as described later according to the search program stored in the memory device  25 . 
     The hardware configuration of the terminal device  300  of the present embodiment is the same as that of the search device  200 , and description thereof is omitted. The terminal device  300  may be, for example, a tablet type terminal device, a smart phone, or the like, and may have a display operation device realized by a touch panel or the like instead of the input device  21  and the output device  22 . 
     Next, with reference to  FIG. 3 , the functions of the search device  200  of the present embodiment will be described.  FIG. 3  is a diagram that describes the functions of the search device of the first embodiment. 
     The search device  200  of the present embodiment includes the search target index storage unit  210 , the impression word map storage unit  220 , the search target score storage unit  230 , and the search processing unit  240 . 
     Index information  211  is stored in the search target index storage unit  210 . The index information  211  of the present embodiment is created by preliminary processing by an index generation unit  247  described later. 
     An impression word map  221  is stored in the impression word map storage unit  220 . The impression word map  221  of the present embodiment is given in advance by a manager or the like of the search device  200 . 
     An impression word score list  231  is stored in the search target score storage unit  230 . The impression word score list  231  is created by processing of the search processing unit  240  described later. 
     The search processing unit  240  of the present embodiment includes an input receiving unit  241 , an analysis unit  242 , a search unit  243 , an impression word score output unit  244 , an axis determination unit  245 , an output unit  246 , the index generation unit  247 , and an impression space learning unit  248 . Each unit of the search processing unit  240  is realized by the arithmetic processing device  26  of the search device  200  reading and executing the search program stored in the memory device  25  or the like. 
     The input receiving unit  241  of the present embodiment receives various inputs to the search device  200 . For example, the input receiving unit  241  receives input of the query to the search device  200 . 
     The analysis unit  242  of the present embodiment performs word segmentation, modification analysis, meaning analysis, and the like to keywords and natural sentences. For example, the analysis unit  242  specifies a search word group based on the query, or specifies a word from the document data of the search result acquired from the search target document database  400 . For example, the analysis unit  242  of the present embodiment fulfills the function of a specification unit that specifies a search word group from the query. 
     The search unit  243  of the present embodiment refers to the search target index storage unit  210 , specifies the document data including the search word group acquired from the analysis unit  242 , and acquires the search result set data from the search target document database  400 . 
     The impression word score output unit  244  converts each word included in the search word group acquired from the query to a vector by a distributed representation. The impression word score output unit  244  outputs a score indicating strength of a relationship between the search word group and the impression words included in the impression word map  221  based on each word converted to a vector and a conversion model  250  described later. 
     For example, the impression word score output unit  244  outputs the score indicating the strength of the relationship between the word represented in a vector as an input to the conversion model  250  and each impression word included in the impression word map  221 . 
     A method based on the distributed representation of words of the present embodiment is a method that estimates a semantic similarity relationship (similarity) between each word using a large amount of learning data given separately, and represents meaning of the word by a vector by disposing each word in a vector space of a predetermined number of dimensions so as to be consistent with the estimated inter-word similarity. In the following description, the vector representing the meaning of a word converted by the distributed representation of words is referred to as a semantic vector. 
     The impression word of the present embodiment is a word that reminds impression of the search result such as an adjective, an adjective verb, and an adverb. 
     The axis determination unit  245  determines the impression words as the axes of the radar chart to display the search result according to the score of each word output by the impression word score output unit  244 . The axis of the radar chart is, for example, one of the items to display the search result set data. 
     The output unit  246  of the present embodiment displays the radar chart of the search result set data using the impression words determined by the axis determination unit  245  as the axes. 
     For example, the output unit  246  of the present embodiment displays the impression words extracted according to the strength of the relationship with the search word group as the item when displaying the search result set data acquired by search processing by the search word group based on the query. 
     The index generation unit  247  of the present embodiment creates the index information  211  stored in the search target index storage unit  210  from the search target document database  400 . 
     The impression space learning unit  248  outputs the conversion model  250  based on the search target document data stored in the search target document database  400  and the impression word map  221  stored in the impression word map storage unit  220 . 
     The processing of the index generation unit  247  and the impression space learning unit  248  of the present embodiment may be performed in advance as the preliminary processing of search processing performed after the query is input. 
     The preliminary processing of this embodiment will be described below. First, generation of the index information  211  by the index generation unit  247  will be described with reference to  FIGS. 4 to 58 . 
       FIG. 4  is a flowchart that describes the processing by an index generation unit. 
     The index generation unit  247  of the present embodiment refers to the search target document database  400 , and determines whether or not a certain word is included in the document data for each document data stored in the search target document database  400  (step S 401 ). 
     In the present embodiment, a document ID is included in each document data stored in the search target document database  400  as identification information to specify the document data. 
     The index generation unit  247  acquires a set of document data including the word, generates the index information  211  in which the word, the number of acquired document data pieces, and the document ID as the identification information that specifies the document data are associated with each other for each word (step S 402 ), and ends the processing. 
       FIGS. 5A and 5B  are example tables of index information generated by the index generation unit.  FIG. 5A  is a table illustrating intermediate data acquired as a result of processing in step S 401 .  FIG. 5B  is an example table of the index information  211 . 
     The index generation unit  247  of the present embodiment includes, for example, “this”, “pen”, and “apple” in the document data. 
     In this case, the index generation unit  247  determines whether or not each word of “this”, “pen”, and “apple” is included in the document data for each document data stored in the search target document database  400 , and stores intermediate data  205  indicating the determination result. 
     In the intermediate data  205  illustrated in  FIG. 5A , it is found that the word “this” is included in the document data with document ID “1”, the document data with document ID “2”, and the document data with document ID “N”. It is found that the word “pen” is included in the document data with the document ID “1”. It is found that the word “apple” is included in the document data with the document ID “2”. 
     Next, the index generation unit  247  generates the index information  211  in which the number of document data pieces including the word is associated with the document ID for each word. 
     The index information  211  illustrated in  FIG. 5B  includes keys, the number of match documents, and an array of documents as items of information. The value of the item “key” indicates the word estimated to be included in the document data. The value of the item “number of match documents” indicates the number of document data pieces including words as the keys. The value of the item “array of documents” indicates the document ID of the document data including the words as the keys. The number of the document IDs included in the value of the item “array of documents” is equal to the number as the value of the item “number of match documents”. 
     In the index information  211  of  FIG. 5B , the number of document data pieces including the word “this” is 239, and the document ID of the document data including the word “this” is document ID “1”, “2”, . . . , “N”. 
     As described above, the index generation unit  247  of the present embodiment generates the index information  211 , and stores the information in the search target index storage unit  210 . 
     Next, before describing the processing by the impression space learning unit  248 , the impression word map  221  stored in the impression word map storage unit  220  of the present embodiment will be described. 
       FIG. 6  is an example table of an impression word map of the first embodiment. The impression word map  221  of the present embodiment is, for example, created by a manager or the like of the search system  100 , and given to the search device  200  in advance. For the impression word map  221 , for example, a map that is created based on a result of a questionnaire or the like conducted on a large number of people to answer the strength of the relationship between words and impression words may be given to the search device  200 . 
     In the impression word map  221  of the present embodiment, a score (value) indicating the strength of the relationship between the impression word prepared in advance and each word is associated with each word. The word included in the impression word map  221  may be any word estimated to be included in the document data stored in the search target document database  400 . 
     The score of the present embodiment is, for example, a value from 0 to 10. In a case where the score is “0”, it indicates that there is no relationship between the word and the impression word, and the larger the score, the stronger the relationship between the word and the impression word. 
     In the impression word map  221  illustrated in  FIG. 6 , “premier”, “lively”, “hideaway”, “quiet”, “healthy”, and the like are listed as an impression word. 
     For example, in the impression word map  221 , the score indicating the strength of the relationship between a word “date” and the impression word “premier” is “9.21”, and the score indicating the strength of the relationship between the word “date” and the impression word “lively” is “3.23”. 
     Therefore, it is found that the word “date” has a stronger relationship with the impression word “premier” than the impression word “lively”. 
     Next, the processing of the impression space learning unit  248  of the present embodiment will be described with reference to  FIG. 7 .  FIG. 7  is a flowchart that describes the processing by an impression space learning unit. 
     The impression space learning unit  248  of the present embodiment refers to the search target document database  400 , inputs each document data included in the search target document database  400 , and learns the distributed representations of words included in each document data with unsupervised learning (step S 701 ). For example, the impression space learning unit  248  internally stores the semantic vector of words included in each document data. 
     The impression space learning unit  248  refers to the impression word map  221 , and learns the score corresponding to the impression words included in the impression word map  221  with respect to the distributed representation of each word with supervised learning (step S 702 ). For example, the impression space learning unit  248  refers to the impression word map  221  to machine learn the relationship between the semantic vector of each word and the score indicating the strength of the relationship with each impression word associated with the word. 
     The impression space learning unit  248  outputs and stores the learned result as the conversion model  250  (step S 703 ). For example, the conversion model  250  of the present embodiment is a model in which a semantic vector of a word acquired by the distributed representation is input, and the score for each impression word associated with the word is output. 
     The described-above is the preliminary processing by the index generation unit  247  and preliminary processing by the impression space learning unit  248 . 
     Next, the impression word score list  231  stored in the search target score storage unit  230  of the present embodiment will be described with reference to  FIG. 8 . 
       FIG. 8  is an example table of an impression word score list. The impression word score list  231  of the present embodiment is, for example, a list acquired by processing of the analysis unit  242  and the impression word score output unit  244  of the search processing unit  240 . The impression word score list  231  of the present embodiment is referred to when the search result by the search unit  243  is displayed as the radar chart by the output unit  246 . 
     The impression word score list  231  indicates the score indicating the strength of the relationship between the document ID of document data included in the search target document database  400  and each impression word included in the impression word score list  231 . The impression words included in the impression word score list  231  are the same as the impression words included in the impression word map  221 . 
     For example, the search processing unit  240  of the present embodiment extracts a word from the document data stored in the search target document database  400  by the analysis unit  242 . The search processing unit  240  inputs the extracted word into the conversion model  250  after the extracted word is converted to a semantic vector by the impression word score output unit  244 , and acquires the score indicating the strength of the relationship between each impression word and each word. 
     In the impression word score list  231  of the present embodiment, the score may be a score of each impression word associated with the document ID. 
     In this way, for example, in a case where a single word is extracted from the document data stored in the search target document database  400 , the score indicating the strength of the relationship between the word and each impression word becomes the score indicating the strength of the relationship between the document data and each impression word. 
     For example, in a case where a plurality of words are extracted from the document data, the score indicating the strength of the relationship between each impression word and each word is output for the plurality of words. Therefore, in the present embodiment, for each impression word, a maximum score or an average score of the plurality of words may be acquired, and the maximum score or the average score of each impression word may be the score indicating the strength of the relationship between the document data and each impression word. 
     For example, it is assumed that two words “date” and “delicious” are extracted from the document data. In this case, the search processing unit  240  acquires the score indicating the strength of the relationship between the word “date” and each impression word included in the impression word map  221 , and the score indicating the strength of the relationship between the word “delicious” and each impression word included in the impression word map  221 . 
     The search processing unit  240  acquires the maximum value of the score corresponding to the word “date” and the score corresponding to the word “delicious” for each impression word. For example, it is assumed that the score indicating the strength of the relationship between the word “date” and the impression word “premier” is 9.21, and the score indicating the strength of the relationship between the word “delicious” and the impression word “premier” is 9.38. In this case, the search processing unit  240  may take the maximum value 9.38 between 9.21 and 9.38 as the score indicating the strength of the relationship between the document data and the impression word “premier”. 
     Next, the processing of the search processing unit  240  of the present embodiment will be described with reference to  FIG. 9 .  FIG. 9  is a flowchart that describes the processing of a search processing unit of the first embodiment. 
     When the input receiving unit  241  receives an input of a query, the search processing unit  240  of the present embodiment acquires a search word group by morphological analysis or the like from the query by the analysis unit  242 , searches the index information  211  with the search word group by the search unit  243 , and specifies the document ID corresponding to the search word group (step S 901 ). In the present embodiment, the document data indicated by the document ID specified in step S 901  is the search result set data. 
     The search processing unit  240  extracts the n number of impression words with clear relationship with the query as the axis based on the input query by the impression word score output unit  244 , and sets the n number of impression words as an α axis by the axis determination unit  245  (step S 902 ). The axis in step S 902  is an axis of the radar chart, and is one of the items to be an attribute of the search result set data. Details of processing of step S 902  will be described later. The α axis of the present embodiment is an axis in which an impression word that has a large score indicating the strength of the relationship with the query, has a strong relationship with the query, and the correspondence is clear is set. 
     The search processing unit  240  extracts the m number of preset words by the axis determination unit  245 , and sets them as a β axis (step S 903 ). The preset word may be, for example, selected and set from the impression words included in the impression word map  221 . The preset word may be selected and set from the words included in the index information  211 . For example, the preset word may be a word included in the search result set data. 
     The β axis of the present embodiment indicates an axis other than the α axis among all of the axes in the radar chart. For example, the β axis is an axis in which an impression word not strongly related to the query is set compared to the α axis. 
     The output unit  246  uses the α axis and the β axis as an output axis (step S 904 ), draws the search result set data indicated by the document ID acquired by the search unit  243  in step S 901  by the output unit  246  on a radar chart (step S 905 ), and ends the processing. 
     Next, the processing of step S 902  of  FIG. 9  will be described with reference to  FIG. 10 .  FIG. 10  is a flowchart that describes the processing of an impression word score output unit and an axis determination unit of the first embodiment. The processing in  FIG. 10  indicates the processing of the impression word score output unit  244  and the axis determination unit  245 . 
     The impression word score output unit  244  of the present embodiment converts each word included in the search word group acquired in step S 901  to the distributed representations (step S 1001 ). For example, the impression word score output unit  244  converts each word included in the search word group to a semantic vector by the distributed representation. 
     The impression word score output unit  244  refers to the conversion model  250 , and acquires the score indicating the strength of the relationship between each impression word and each word (step S 1002 ). For example, the impression word score output unit  244  inputs the semantic vector of each word acquired in step S 1001  to the conversion model  250 , and acquires the score of each impression word for each word output from the conversion model  250 . 
     The axis determination unit  245  acquires the maximum score of each impression word corresponding to each word (step S 1003 ). 
     The axis determination unit  245  extracts the impression words corresponding to the top n maximum scores of each impression word as the α axis related to the query (step S 1004 ). For example, the axis determination unit  245  extracts the n number of impression words in descending order of the impression words having the maximum score for each impression word, and set the n impression words as the α axis. 
     Hereinafter, the processing of the impression word score output unit  244  and the axis determination unit  245  of the present embodiment will be described in details with reference to  FIG. 11 .  FIG. 11  is a diagram describing processing of the impression word score output unit and the axis determination unit. 
     In  FIG. 11 , the query input by the user will be described as “quiet premier restaurant”. 
     The search processing unit  240  of the present embodiment acquires a search word group  111  by the analysis unit  242  when the input of the query is received. In the search word group, the word “quiet”, “premier”, are “restaurant” included. 
     Next, the search processing unit  240  acquires a semantic vector  112  for each word included in the search word group  111  by the impression word score output unit  244 . 
     In the semantic vector  112  illustrated in  FIG. 11 , each word “quiet”, “premier”, and “restaurant” are indicated as a vector. 
     When the semantic vector  112  is acquired, the impression word score output unit  244  inputs the semantic vector  112  to the conversion model  250 , and acquires the score of each impression word included in the impression word map  221  for each word. In  FIG. 11 , the information in which each word included in the search word group is associated with the score of each impression word is referred to as an impression word score  113  for each word. 
     Next, the axis determination unit  245  acquires the maximum score for each impression word from the impression word score  113  for each word. 
     For example, in the impression word score  113  for each word, with respect to the impression word “premier”, the score indicating the strength of the relationship with the word “quiet” is 3.49, the score indicating the strength of the relationship with the word “premier” is 10.00 (full marks), and the score indicating the strength of the relationship with the word “restaurant” is 7.95. 
     Therefore, the axis determination unit  245  acquires “10.00” as the maximum score of the impression word “premier”. 
     In the impression word score  113  for each word, with respect to the impression word “healthy”, the score indicating the strength of the relationship with the word “quiet” is 3.48, the score indicating the strength of the relationship with the word “premier” is 8.33, and the score indicating the strength of the relationship with the word “restaurant” is 6.72. 
     Therefore, the axis determination unit  245  acquires “8.33” as the maximum score of the impression word “healthy”. 
     In the impression word score  113  for each word, with respect to the impression word “hideaway”, the score indicating the strength of the relationship with the word “quiet” is 9.38, the score indicating the strength of the relationship with the word “premier” is 6.23, and the score indicating the strength of the relationship with the word “restaurant” is 3.46. 
     Therefore, the axis determination unit  245  acquires “9.38” as the maximum score of the impression word “hideaway”. 
     The axis determination unit  245  of the present embodiment may store the maximum score acquired for each impression word as an impression word score list  114  corresponding to the query. The impression word score list  114  may be referred to, for example, when displaying the radar chart described later. 
     The axis determination unit  245  extracts the n number of impression words in descending order of the impression words having the maximum score in the impression word score list  114 , and set the n impression words as the α axis. In the example of  FIG. 11 , n is 2. 
     In the impression word score list  114 , the impression word having the maximum score is “premier”, and the impression word having the next maximum score is “hideaway”. 
     Therefore, the axis determination unit  245  of the present embodiment determines the impression word “premier” and the impression word “hideaway” as the α axis of the radar chart. For example, the axis determination unit  245  sets the impression word “premier” and the impression word “hideaway” as the items when displaying the search result set data. 
     In this way, in the present embodiment, the impression word having a strong relationship with the query is extracted based on the search word group acquired from the input query. For example, the impression word extracted here may be a feature word group (hereinafter, also referred to as first feature word group) indicating the feature of the content estimated from the query. Therefore, the axis determination unit  245  of the present embodiment fulfills the function of an extraction unit that extracts the first feature word group from the query. 
     When the impression word score  113  for each word is acquired, the axis determination unit  245  of the present embodiment determines the impression word as the α axis according to the maximum score for each impression word, but not limited thereto. For example, when the impression word score  113  for each word is acquired, the axis determination unit  245  may extract the n number of impression words in descending order of values obtained by normalizing the score of each impression word. When the impression word score  113  for each word is acquired, the axis determination unit  245  may extract the n number of impression words in descending order of the average score for each impression word. 
     For example, in the impression word score list  114  of the present embodiment, the value associated with each impression word is not limited to the maximum score for each impression word in the impression word score  113  for each word. In the impression word score list  114 , the value associated with each impression word may be a value obtained by normalizing the score of each impression word in the impression word score  113  for each word, or may be an average score of each impression word in the impression word score  113  for each word. 
     In the present embodiment, the impression word corresponding with the score is extracted as the α axis, but not limited thereto. 
     The axis determination unit  245  may extract, for example, the word having the maximum score indicating the strength of the relationship with the impression word as the α axis. 
     For example, the axis determination unit  245  extracts the impression word “premier” and “hideaway” in descending order of the score of each impression word. At this time, the axis determination unit  245  may extract the word “quiet” having the maximum score indicating the strength of the relationship with the impression word “hideaway” as the α axis in the impression word score  113  for each word. 
     In this way, in the present embodiment, the semantic vector of the word is acquired from the search word group included in the query input by the user, and the semantic vector is input to the conversion model  250 . In the present embodiment, the score indicating the strength of the relationship between each of impression words and each word included in the search word group is acquired as an output of the conversion model  250 . 
     Therefore, in the present embodiment, even if a new word other than the words prepared in advance is included in the query, it is possible to acquire the score indicating the strength of the relationship between the new word and the impression words. For this reason, according to the present embodiment, it is possible to display the search result using an impression word (item) that has a strong relationship with the query and the correspondence is clear with respect to any queries input by the user. 
     Next, the display of the radar chart by the search processing unit  240  of the present embodiment will be described with reference to  FIGS. 12 to 14 . 
       FIG. 12  is a first diagram illustrating a display example of a radar chart of the first embodiment. A screen  121  illustrated in  FIG. 12  displays a radar chart of “AA restaurant” acquired as the search result set data in a case where the input query is “quiet premier restaurant”. 
     In the example of  FIG. 12 , only “AA restaurant” is displayed as an example of the search result set data, but not limited thereto. As search result set data, in a case where a plurality of search results are acquired, a radar chart for each search result may be displayed on the screen  121 . 
     In the example of  FIG. 12 , the search result set data may be document data indicating a description of “AA restaurant” or the like, and the description of “AA restaurant” may be displayed on a search result display field  123 . 
     In the present embodiment, a rank may be given to each search result included in the search result set data. For example, in the present embodiment, total points of scores for each axis of the radar chart or an area of a polygon illustrated as a graph may be calculated as a value indicating the strength of the relationship between the query and the search result set data for each search result set data, and the rank may be given to the search result set data in descending order of the values. 
     For example, in the present embodiment, the rank is given to the search result set data in the descending order of the strength of the relationship with the query. In the present embodiment, by assigning the rank in this way, it is possible to present the search results in descending order of the strength of the relationship with the query to the user. 
     The screen  121  of the present embodiment includes a query display field  122 , the search result display field  123 , and a search result display field  124 . 
     The query input by the user is displayed in the query display field  122 . A radar chart  125  that corresponds to the search result included in the search result set data is displayed in the search result display field  123 . In the radar chart  125 , a graph  126  that indicates the relationship between the search result set data and the axis item of the radar chart  125  are displayed. A bar graph  127  that indicates the value of each axis of the radar chart  125  is displayed in the search result display field  124 . 
     The radar chart  125  of the present embodiment has five axes, an α1 axis, an α2 axis, a β1 axis, a β2 axis, and a β3 axis. For example, the radar chart  125  displays the search result of “AA restaurant” using five items, two impression words extracted based on the query, and three impression words other than the impression words selected based on the query. 
     The item corresponding to the α1 axis is “hideaway”, and the item corresponding to the α2 axis is “premier”. The item corresponding to the β1 axis is “calm”, the item corresponding to the β2 axis is “plentiful”, and the item corresponding to the β3 axis is “healthy”. 
     The items corresponding to the α1 axis and the α2 axis are the impression words extracted in step S 902  of  FIG. 9 . For example, the items corresponding to the α1 axis and the α2 axis are impression words having clear relationship with the query. 
     The items corresponding to the β1 axis, the β2 axis, and the β3 axis are the words set in step S 903  of  FIG. 9 . For example, the items corresponding to the β1 axis, the β2 axis, and the β3 axis are preset words. 
     In the example of  FIG. 12 , the items corresponding to the β1 axis, the β2 axis, and the β3 axis are impression words selected from the impression word map  221 , but not limited thereto. 
     The items corresponding to the β1 axis, the β2 axis, and the β3 axis may be, for example, a word selected from the index information  211 . For example, the items corresponding to the β1 axis, the β2 axis, and the β3 axis may be words extracted from the search result set data. The word extracted from the search result set data may be a feature word group (second feature word group) indicating the feature of the search result set data. 
     Therefore, in the radar chart  125  of the present embodiment, the search result set data is displayed using the feature word group extracted from the query and the feature word group extracted from the search result set data as items. 
     The display of the graph  126  by the output unit  246  after the item corresponding to each axis of the radar chart  125  are determined in the search processing unit  240  of the present embodiment will be described. 
     The output unit  246  of the present embodiment refers to the impression word score list  231  when the item corresponding to each axis of the radar chart  125  is determined. 
     The output unit  246  acquires the score corresponding to the impression word set as an item among the scores for each impression word corresponding to the document ID of the document data acquired as the search result set data in the impression word score list  231 , and sets the score as the value of an item in the radar chart. 
     For example, it is assumed that the acquired document data as search result set data is document data with document ID “1”. 
     In this case, the output unit  246  refers to the impression word score list  231 , acquires score “9.2” corresponding to “hideaway” which is an item corresponding to the α1 axis among the scores of each impression word corresponding to the document ID “1”, and sets the score as the value of the α1 axis of the radar chart  125 . The output unit  246  acquires score “9.0” corresponding to “premier” which is an item corresponding to the α2 axis among the scores for each impression word corresponding to the document ID “1”, and set the score as the value of the α2 axis in the radar chart  125 . 
     Similarly, the output unit  246  of the present embodiment acquires the value of the β1 axis to β3 axis from the impression word score list  231 . The output unit  246  displays the graph  126  based on the value of each axis. 
     The impression word score list  231  of the present embodiment may be generated by preliminary processing. When the search result set data is acquired from the search by the search unit  243 , the impression word score list  231  may acquire the score for each impression word from the search result set data and store the score in the impression word score list  231 . 
     In a case where the score for each impression word is acquired from the search result set data, the search processing unit  240  may perform processing from step S 1001  to step S 1003  of  FIG. 10  after acquiring a word group from the search result set data by the analysis unit  242 . The search processing unit  240  may store the score for each impression word which is the result of performing the processing in the impression word score list  231  associated with the document ID indicating the search result set data. 
     As described above, in the present embodiment, it is possible to allow the user to easily determine that the search result is what the user intended by setting the impression word with a strong relationship with the query extracted based on the query as the axis of the radar chart  125 . Therefore, according to the present embodiment, it is possible to provide the user of the search system  100  with satisfaction to the search result. 
     Hereinafter, a comparative example of a case that the present embodiment is not applied will be described with reference to with reference to  FIG. 13 .  FIG. 13  is a diagram illustrating an example of a radar chart as a comparative example. 
     A query display field  132  in which an input query is displayed, a search result display field  133  in which the search result is displayed, and a radar chart  134  are displayed on a screen  131  illustrated in  FIG. 13 . 
     The items corresponding to the axes  1  to  5  illustrated in the radar chart  134  illustrated in  FIG. 13  are words selected from the document data associated with “AA restaurant” acquired as the search result. 
     For example, the item corresponding to the axis  1  is “cheap”, the item corresponding to the axis  2  is “feeling of freedom”, the item corresponding to the axis  3  is “private room available”, the item corresponding to the axis  4  is “lively”, and the item corresponding to the axis  5  is “plentiful”. 
     The query input with respect to the items corresponding to the axes  1  to  5  is “quiet premier restaurant”. 
     For this reason, for example, the item “lively” corresponding to the axis  4  is an item hard to associate from the query, and the correspondence with the query is not clear. 
     As described above, in the radar chart indicating the search result, in a case where a word hard to associate from the query input by the user is used as an item, it is not easy for the user to determine whether or not the search result meet the intention of the user, and the user may not feel satisfaction with respect to the search result. 
     On the other hand, in the present embodiment, it is possible to properly evaluate the search result since the search result set data is displayed by the radar chart using the impression word, as the item, with a strong relationship with the query extracted based on the query. 
     Next, a display example of the radar chart of the present embodiment will be described with reference to  FIG. 14 .  FIG. 14  is a second diagram that illustrates a display example of a radar chart of the first embodiment. 
     A screen  121 A illustrated in  FIG. 14  includes a search result display field  123 A and a search result display field  124 A. 
     The graph  126  and a graph  128  are displayed in a radar chart  125 A displayed in the search result display field  123 A. 
     The graph  128  is a graph indicating the relationship between the query and the axis items. The graph  128  may be displayed, for example, with reference to the impression word score list  114  acquired by the processing that extracts items corresponding to an α1 axis and an α2 axis of the radar chart  125 A (see  FIG. 11 ). 
     For example, the output unit  246  may acquire the score of impression word set as an item corresponding to each of the α1 axis, the α2 axis, a β1 axis, a β2 axis, and a β3 axis in the impression word score list  114 , and display the graph  128  using the score as the value of each axis. 
     In the search result display field  124 A of the screen  121 A, a bar graph  129  indicating the value of each axis of the query is displayed. 
     In the present embodiment, as described above, the graph  126  indicating the relationship between the search result set data and the axis items and the graph  128  indicating the relationship between the query and the axis items are displayed in the radar chart  125 A. Therefore, according to the present embodiment, it allows the user to easily grasp the degree of relationship between the search result set data and the query. 
     Second Embodiment 
     Hereinafter, the second embodiment will be described with reference to the drawings. The second embodiment is different from the first embodiment in that the item corresponding to the axis β is determined based on the query. In the following description of the second embodiment, only the differences from the first embodiment will be described. Those having the same functional configuration as those of the first embodiment are denoted by the same reference numerals as those used in the description of the first embodiment, and descriptions thereof will be omitted. 
       FIG. 15  is a diagram describing functions of a search device of a second embodiment. A search device  200 A of the present embodiment includes a search processing unit  240 A. 
     The search processing unit  240 A of the present embodiment includes the input receiving unit  241 , the analysis unit  242 , the search unit  243 , the impression word score output unit  244 , an axis determination unit  245 A, the output unit  246 , the index generation unit  247 , and the impression space learning unit  248 . 
     When the axis determination unit  245 A of the present embodiment acquires the impression word score  113  for each word by the impression word score output unit  244 , the axis determination unit  245 A acquires and stores the impression word score list  114  indicating the maximum score of each impression word included in the impression word score  113  for each word. The axis determination unit  245 A of the present embodiment refers to the impression word score list  114 , and determines the impression word corresponding to the axis α and axis β of the radar chart. 
       FIG. 16  is a flowchart that describes the processing of a search processing unit of the second embodiment. Since the processing of step S 1601  of  FIG. 16  is similar to the processing of step S 901  of  FIG. 9 , description thereof is omitted. 
     The search processing unit  240 A extracts the n number of impression words having a clear relationship with the query as an axis based on the input query by the impression word score output unit  244 , generates and stores the impression word score list  114  by the axis determination unit  245 A, and set them as the α axis (step S 1602 ). 
     The axis determination unit  245 A refers to the impression word score list  114  generated based on the query, extracts the m number of items corresponding to the β axis, and sets them as the β axis (step S 1603 ). Details of the processing of step S 1603  will be described later. 
     Since the processing of step S 1604  and step S 1605  of  FIG. 16  is similar to the processing of step S 904  and step S 905  of  FIG. 9 , description thereof is omitted. 
     Next, the processing of the axis determination unit  245 A of the present embodiment will be described with reference to  FIG. 17 .  FIG. 17  is a flowchart describing processing of an axis determination unit of the second embodiment. The processing of  FIG. 17  indicates the details of the processing of step S 1603  of  FIG. 16 . 
     When the impression word score list  114  is generated in step S 1602 , the axis determination unit  245 A of the present embodiment extracts and stores each score of size from top n+1th to the mth score and the impression word corresponding to corresponding to each score among the scores associated with each impression word from the impression word score list  114  (step S 1701 ). 
     The axis determination unit  245 A acquires the z number of search result set data pieces in descending order of the degree of coincidence with the search word group from the search result set data, and acquires the score corresponding to the impression word stored in step S 1701  with reference to the impression word score list  231  on the z number of search result set data pieces (step S 1702 ). 
     Hereinafter, the processing of step S 1702  will be described. The axis determination unit  245 A of the present embodiment acquires the z number of search result set data pieces in descending order of the degree of coincidence with the search word group from the search result set data acquired by the search of the search unit  243  in step S 1601 . The degree of coincidence with the search word group may be calculated based on the number of search word groups included in the search result set data, the appearance frequency of each word included in the search word group in the search result set data. As the degree of coincidence, the degree of coincidence in the case of performing information search may be used generally. 
     When the axis determination unit  245 A acquires the z number of search result set data pieces, the axis determination unit  245 A refers to the impression word score list  231  and acquires the score associated with the impression word stored in step S 1701  for each document ID of each search result set data. 
     For example, it is assumed that impression words stored in step S 1701  are “healthy”, “quiet”, “fancy”, “lively”, “plentiful”, and “calm”. It is assumed that the search result set data with document ID “2” is included in the z number of search result set data pieces. 
     In this case, the axis determination unit  245 A refers to the impression word score list  231 , and acquires the score 9.53 of impression word “healthy” corresponding to the document ID “2”, the score 0.12 of the impression word “quiet”, and the score 8.93 of the impression word “fancy” in step S 1702  (see  FIG. 8 ). Similarly, the axis determination unit  245 A acquires the corresponding score of impression words, “lively”, “plentiful”, and “calm”. 
     The axis determination unit  245 A of the present embodiment acquires and stores the score for each impression word as described above for each of the z number of search result set data pieces. 
     The axis determination unit  245 A calculates the dispersion of the score for each search result set data acquired in step S 1702  for each impression word stored in step S 1701  (step S 1703 ). 
     The axis determination unit  245 A sorts the impression words extracted in step S 1701  by the dispersion value of the score, extracts the k number of impression words in descending order of impression words with large dispersion values, and sets the extracted impression word as the β axes (step S 1704 ). k is the number obtained by subtracting n from the number of axes set in the radar chart, and is equal to the number of the β axes. 
     As described above, the axis determination unit  245 A of the present embodiment extracts the impression word having variations in the strength of the relationship with the search result set data among the impression words with a small score indicating the strength of the relationship with the query compared with the α axis, and set as an item corresponding to the β axis. 
     In the present embodiment, by setting the item of the β axis in this way, the value of each axis being close to each other, the radar charts of search result set data pieces being similar to each other in the radar chart are suppressed. Therefore, according to the present embodiment, it is possible to display the features of each search result set data easy to understand when the search result is properly evaluated, and the radar chart of each search result set data is displayed. 
     Third Embodiment 
     Hereinafter, the third embodiment will be described with reference to the drawings. The third embodiment is different from the first embodiment in that the dispersion obtained when acquiring the score indicating the strength of the relationship between the word and the impression word is used when determining the axis. In the following description of the third embodiment, only the differences from the first embodiment will be described. Those having the same functional configuration as those of the first embodiment are denoted by the same reference numerals as those used in the description of the first embodiment, and descriptions thereof will be omitted. 
     First, the creation of the impression word map of the present embodiment to be described later will be described with reference to  FIGS. 18A and 18B . 
     The impression word map of the present embodiment is created, for example, based on the result of a questionnaire or the like that is conducted on a large number of people to answer a score indicating the strength of the relationship between words and impression words. 
     For example, in the present embodiment, the results of the above-described questionnaire or the like are collected and the average, the maximum value, and the like of score indicating the strength of the relationship between the word and the impression word are stored in the impression word maps as the score indicating the relationship between the word the and impression word. 
     There are some words that have impression words that many people feel strongly related to the word, and impression words that feeling of the strength of the relationship to the word is different by people. In the former impression word, it is presumed that the variation of the score indicating the strength of the relationship with the word becomes small. In the latter impression word, it is presumed that the variation of the score indicating the strength of the relationship between the word and the impression word becomes large. In the present embodiment, attention is paid to the variation of the score of each impression word. 
       FIGS. 18A and 18B  are graphs describing variation in scores in an impression word map of a third embodiment.  FIG. 18A  is a graph that illustrates the variation of the score indicating the strength of the relationship between the word “natural” and the impression word “delicious”.  FIG. 18B  is a graph that illustrates the variation of the score indicating the strength of the relationship between the word “natural” and the impression word “smell nice”. 
     In  FIGS. 18A and 18B , the horizontal axis indicates the value of the score answered in the questionnaire. In the examples of  FIGS. 18A and 18B , the score indicating the strength of the relationship between the word and the impression word is answered from 0 to 5. As the score is larger, it is assumed that the strength of the relationship that the respondents of the questionnaire feel between the word and the impression word is stronger. 
     In  FIGS. 18A and 18B , the vertical axis indicates the ratio of the number of responses for each score to the number of all respondents of the questionnaire. 
     In  FIG. 18A , regarding the strength of the relationship between the word “natural” and the impression word “delicious”, the number of responses with the score “4” is the largest, and the distribution of score is unimodal. For example, in  FIG. 18A , it is found that the variation of the score is small. This indicates that many people feel that word “natural” and the impression word “delicious” has a strong relationship. 
     In  FIG. 18B , regarding the strength of the relationship between the word “natural” and the impression word “smell nice”, there are many numbers of responses with the score “2” and score “4”, and the distribution of score is bimodal. For example, in  FIG. 18B , compared with  FIG. 18A , it is found that score variation is large. This indicates that the feeling of the strength of the relationship between word “natural” and the impression word “smell nice” is different by people. 
     In the present embodiment, considering the variation in the feeling of people, for example, the average of the scores acquired as the result of questionnaire responses is associated with the value indicating the score variation with each combination of the word and the impression word, and are stored in an impression word map. 
     In the examples of  FIGS. 18A and 18B , with the combination of the word “natural” and the impression word “delicious”, the average of the score acquired as a response of the questionnaire is 3.8, and a standard deviation indicating the variation is 0.68. Therefore, in the present embodiment, the score “3.8” indicating the strength of the relationship between the word “natural” and the impression word “delicious” and the standard deviation “0.68” are associated with and stored in the impression word map. 
     In the example of  FIGS. 18A and 18B , with the combination of the word “natural” and the impression word “smell nice”, the average of the score acquired as a response to the questionnaire is 3.25, and the standard deviation indicating the variation is 1.16. Therefore, in the present embodiment, the score “3.25” and the standard deviation “1.16” indicating the strength of the relationship between the word “natural” and the impression word “smell nice” are associated with each other and stored in the impression word map. 
     In the present embodiment, in this way, by using the impression word map with which the score and the variation of the score are associated, for example, it is possible to extract another impression word to replace the axis when an impression word having a large standard deviation is used as an axis. For example, in the present embodiment, in a case where an impression word that the feeling of the strength of the relationship with the word is different by people is extracted as an axis, it is possible to extract another impression word to replace the impression word, and present to the user. 
     Therefore, according to the present embodiment, when displaying the search result set data acquired by the search processing by the search word group based on the query, it is possible to display the impression word that reflects the intention of the user as a candidate of an item (axis). 
     Hereinafter, a search device of the present embodiment will be described.  FIG. 19  is a diagram describing functions of a search device of the third embodiment. A search device  200 B of the present embodiment includes the search target index storage unit  210 , an impression word map storage unit  220 A, a search target score storage unit  230 A, and a search processing unit  240 B. 
     An impression word map  221 A is stored in the impression word map storage unit  220 A. In the impression word map  221 A, a score indicating the relationship between the impression word and the word and the standard deviation acquired when the score is acquired are associated with each other for each impression word. Details of the impression word map  221 A will be described later. 
     An impression word score list  231 A is stored in the search target score storage unit  230 A. The impression word score list  231 A is created by the processing of the search processing unit  240 B. Details of the impression word score list  231 A will be described later. 
     The search processing unit  240 B of the present embodiment includes the input receiving unit  241 , the analysis unit  242 , the search unit  243 , an impression word score output unit  244 A, the axis determination unit  245 A, the output unit  246 , the index generation unit  247 , an impression space learning unit  248 A, and a candidate notification unit  249 . 
     The impression word score output unit  244 A of the present embodiment outputs the score indicating the strength of the relationship between the word represented by a vector and the impression word and the standard deviation of the score based on the impression word map  221 A and a conversion model  250 A output by the impression space learning unit  248 A described later. 
     The axis determination unit  245 A of the present embodiment outputs an impression word as an axis of a radar chart for displaying the search result and an impression word that is replaceable with the impression word based on the score and the standard deviation for each impression word output by the impression word score output unit  244 A. The impression word replaceable with the impression word determined as an axis is referred to as a candidate axis. The impression word as a candidate axis of the present embodiment is, for example, a third feature word group that is a candidate of the feature word group (first feature word group) indicating the feature of the content estimated from the query. 
     The impression space learning unit  248 A of the present embodiment outputs the conversion model  250 A based on the document data of the search target stored in the search target document database  400  and the impression word map  221 A. The conversion model  250 A of the present embodiment is a model that inputs a semantic vector of a word acquired by distributed representation, and outputs the score for an impression word associated with the word and the standard deviation of the scores. 
     The candidate notification unit  249  of the present embodiment notifies the impression word as the candidate axis output by the axis determination unit  245 A. 
     Hereinafter, the impression word map  221 A of the present embodiment will be described with reference to  FIG. 20 .  FIG. 20  is a table illustrating an example of the impression word map of the third embodiment. 
     The impression word map  221 A of the present embodiment is created in advance and given to the search device  200 B. In the impression word map  221 A of the present embodiment, the score (value) indicating the strength of the relationship with an impression word prepared in advance and the standard deviation indicating the variation of the score are associated with each other for each word. 
     In  FIG. 20 , for example, the score indicating the strength of the relationship between the word “restaurant” and the impression word “lively” is 1.6, and the standard deviation σ thereof is 0.5. The score indicating the strength of the relationship between the word “restaurant” and the impression word “stuffed” is 3.1, and the standard deviation σ thereof is 1.7. Therefore, between the impression word “lively” and the impression word “stuffed”, many people feel that the impression word “lively” has a higher strength of the relationship than that of the word “restaurant”. 
     In the present embodiment, a value indicating the variation of the score indicating the strength of the relationship between the word and the impression word as the standard deviation, but not limited thereto. The value indicating the variation of the score indicating the strength of the relationship between the word and the impression word may be held as dispersion. 
     Next, the impression word score list  231 A of the present embodiment will be described with reference to  FIG. 21 . 
       FIG. 21  is an example table of an impression word score list of the third embodiment. In  FIG. 21 , an example of the impression word score list  231 A acquired from the document data related to restaurants and the like in the search target document database  400  is illustrated. The impression word score list  231 A may be created in advance and given to the search device  200 B. 
     In the impression word score list  231 A, a document ID that specifies the document data, a name of the store indicated by the document data, text data indicating the content of the document data, and a score indicating the strength of the relationship with each impression word included in the impression word score list  231 A are associated with one another. The impression words included in the impression word score list  231 A are the same as the impression word included in the impression word map  221 A. 
     Next, the processing of the search processing unit  240 B of the present embodiment will be described with reference to  FIG. 22 .  FIG. 22  is a flowchart describing processing of a search processing unit of the third embodiment. 
     The search processing unit  240 B of the present embodiment acquires the search word group by morphological analysis or the like from the query by the analysis unit  242 , searches the index information  211  with the search word group by the search unit  243 , and specifies the document ID corresponding to the search word group (step S 2201 ). 
     The search processing unit  240 B extracts the n number of impression words that are assumed to have a strong relationship with the query as axes and set the impression words as a axes based on the query input by the impression word score output unit  244 A, and extract the impression word as a candidate axis (step S 2202 ). Details of the processing of step S 2202  will be described later. 
     Since the processing from step S 2203  to step S 2205  of  FIG. 22  is similar to the processing from step S 903  to step S 905  of  FIG. 9 , description thereof is omitted. 
     When the output unit  246  draws a radar chart in step S 2205 , the search processing unit  240 B determines whether or not the impression word as a candidate axis is extracted by the candidate notification unit  249  in step S 2202  (step S 2206 ). In step S 2206 , in a case where the impression word is not extracted, the search processing unit  240 B ends the processing. 
     In step S 2206 , in a case where the impression word is extracted, the candidate notification unit  249  outputs the impression word extracted as a candidate axis in step S 2202  on a screen that a radar chart is displayed, notifies that it is possible to replace the axis of radar chart with a candidate axis (step S 2207 ), and ends the processing. 
     Next, details on the processing of step S 2202  of  FIG. 22  will be described with reference to  FIG. 23 .  FIG. 23  is a flowchart describing processing of an impression word score output unit and an axis determination unit of the third embodiment. 
     The impression word score output unit  244 A of the present embodiment converts each word included in the search word group acquired in step S 2201  to the distributed representation (step S 2301 ). 
     The impression word score output unit  244 A refers to the conversion model  250 A, acquires the score that indicates the strength of the relationship between each word and each impression word and the standard deviation for each word (step S 2302 ), and proceeds to step S 2303 . For example, the impression word score output unit  244  inputs the semantic vector for each word acquired in step S 2301  with respect to the conversion model  250 A, and acquires the score of each impression word and the standard deviation for each word output from the conversion model  250 A. 
     Since the processing of step S 2303  and step S 2304  are similar to the processing of step S 1003  and step S 1004  of  FIG. 10 , the description thereof is omitted. 
     Following step S 2304 , the search processing unit  240 B determines whether or not there is an impression word that has the standard deviation value larger than a threshold among the standard deviations of the extracted impression words in step S 2304  by the axis determination unit  245 A (step S 2305 ). 
     In step S 2305 , in a case where there is no corresponding impression word, the search processing unit  240 B ends the processing and proceeds to step S 2203 . 
     In step S 2305 , in a case where there is a corresponding impression word, the axis determination unit  245 A specifies the impression word that has the standard deviation larger than the threshold from the extracted impression words (step S 2306 ). 
     The axis determination unit  245 A extracts the m number of impression words that has the maximum score of n+1th rank or lower from top, and the maximum score is larger than the value obtained by subtracting the standard deviation from the maximum score of the impression word specified in step S 2306  (step S 2307 ). 
     The axis determination unit  245 A stores the m number of extracted impression words as candidate axes replaceable with the impression word specified in step S 2306  (step S 2308 ), and proceeds to step S 2203 . 
     Hereinafter, the extraction of a candidate axis by the search processing unit  240 B of the present embodiment will be described in details with reference to  FIG. 24 .  FIG. 24  is a diagram describing extraction of a candidate axis by the search processing unit of the third embodiment. 
     In  FIG. 24 , a case that a query “restaurant using natural materials and vegetables” is input, and “natural”, “material”, “vegetable”, “restaurant” are extracted by the analysis unit  242  as a search word group  111 A is illustrated. 
     The search processing unit  240 B acquires the semantic vector of each word included in the search word group  111 A and input as the conversion model  250 A by the impression word score output unit  244 A, and acquires the score and the standard deviation for each impression word included in the impression word map  221 A for each word. 
     In  FIG. 24 , information in which each word included in the search word group  111 A is associated with the score and the standard deviation for each impression word is referred to as an impression word score  113 A for each word. 
     Next, the axis determination unit  245 A acquires the maximum score for each impression word from the impression word score  113 A for each word. 
     In the impression word score  113 A for each word of  FIG. 24 , the score indicating the strength of the relationship between the impression word “delicious” and the word “restaurant” and the standard deviation are 3.4 and 0.2, and the score indicating the strength of the relationship between the impression word “delicious” and the word “vegetable” and the standard deviation are 3.7 and 0.3. The score indicating the strength of the relationship between the impression word “delicious” and the word “natural” and the standard deviation are 3.8 and 0.7, and the score indicating the strength of the relationship between the impression word “delicious” and the word “material” and standard are 3.2 and 0.5. 
     Therefore, the axis determination unit  245 A acquires “3.8” as the maximum value of the score of the impression word “delicious” and acquires “0.7” as the standard deviation of the impression word “delicious”. 
     In the impression word score  113 A for each word, the score indicating the strength of the relationship between the impression word “healthy” and the word “restaurant” and the standard deviation are 3.0 and 0.5, and the score indicating the strength of the relationship between the impression word “healthy” and the word “vegetable” and the standard deviation are 4.2 and 0.3. The score indicating the strength of the relationship between the impression word “healthy” and the word “natural” and the standard deviation are 3.1 and 1.2, and the score indicating the strength of the relationship between the impression word “healthy” and the word “material” and the standard deviation are 3.0 and 0.7. 
     Therefore, the axis determination unit  245 A acquires “4.2” as the maximum score of the impression word “healthy” and “0.3” as the standard deviation of the impression word “healthy”. 
     The axis determination unit  245 A of the present embodiment stores the maximum value of the score and the standard deviation for each impression word acquired in this manner as an impression word score list  114 A. 
     The axis determination unit  245 A extracts the n number of impression words in descending order of the impression words having the maximum score in the impression word score list  114 A, and set them as the a axis. In the example of  FIG. 24 , n is 3. 
     In the impression word score list  114 A, the impression word having the maximum score is “healthy”, the impression word having the next maximum score is “delicious”, and the impression word having the next maximum score is “smell nice”. 
     Therefore, the axis determination unit  245 A of the present embodiment determines the impression word “healthy”, the impression word “delicious”, and the impression word “smell nice” as the α axis. For example, the axis determination unit  245 A uses the impression word “healthy”, the impression word “delicious”, and the impression word “smell nice” as an item when displaying the search result set data. 
     The axis determination unit  245 A of the present embodiment refers to the impression word score list  114 A, and determines whether or not the standard deviation of each of the impression word “healthy”, the impression word “delicious”, and the impression word “smell nice” determined as the α axis is larger than a predetermined threshold. 
     The predetermined threshold of the standard deviation will be described as 1.0. Any value may be set as the predetermined threshold. 
     In the impression word score list  114 A, the standard deviation of the impression word “healthy” is 0.3, the standard deviation of the impression word “delicious” is 0.7, and both values are equal to or less than the predetermined threshold. 
     On the other hand, the standard deviation of the impression word “smell nice” is 1.2, which is larger than the predetermined threshold. This indicates that the feeling of the strength of the relationship between the impression word “smell nice” and the word included in the search word group  111 A is different depending on the people. 
     In the present embodiment, with reference to the impression word score list  114 A, the m number of impression words that have the score of n+1th rank or lower from top, and the score is the value obtained by subtracting the standard deviation 1.2 from the score 3.2 of the impression word “smell nice” is equal to or higher than 2.0 are extracted in descending order of the score as a candidate axis. 
     In the example of  FIG. 24 , m is 2. Therefore, the axis determination unit  245 A extracts two impression words that have a score 4th ranked or lower from the top in the impression word score list  114 A in descending order of the score, and 2.0 or higher. 
     In  FIG. 24 , the impression word “stuffed” with the score of 3.1 and the impression word “stomach-friendly” with the score of 2.8 are extracted as a candidate axis. 
     The output unit  246  of the search processing unit  240 B displays the impression word “healthy”, the impression word “delicious”, and the impression word “smell nice” as an item (axis)  116  that indicates the search result set data on a screen  115  that displays the search result set data. The output unit  246  displays a bar graph  117  indicating the value of each item (axis) on the screen  115 . In the example of  FIG. 24 , an example in which “Kyoto cuisine BB” is extracted as the search result set data is displayed. 
     At this time, the candidate notification unit  249  displays a notification field  118  indicating that, in association with the impression word “smell nice”, the impression word is changeable with the impression word “stuffed” or the impression word “stomach-friendly” extracted as a candidate axis. 
     In the present embodiment, by displaying the notification field  118  as described above, for example, in a case where the user feels that the relationship between the search word group  111 A and the impression word “smell nice” displayed as an item is weak, the user may change the item to another impression word. In the present embodiment, by displaying the notification field  118 , it is possible to inform the user who thinks that the relationship between the search word group  111 A and the impression word “smell nice” is weak, that it is possible to reflect the intention (feeling) of the user. 
     Hereinafter, a display example of the present embodiment will be described with reference to  FIGS. 25 and 26 .  FIG. 25  is a first diagram illustrating a display example of the third embodiment. 
     On a screen  115 A illustrated in  FIG. 25 , a bar graph  119  indicating the value of each axis of the query is displayed in addition to the item (axis)  116  indicating the search result set data, the bar graph  117  indicating the value of each axis (item), and the notification field  118  of a candidate axis replaceable with the impression word “smell nice”. 
       FIG. 26  is a second diagram illustrating a display example of the third embodiment. 
     In a screen  115 B illustrated in  FIG. 26 , the search result set data is displayed by a radar chart  130 . 
     In the radar chart  130 , a graph  117 A and a graph  119 A are displayed. The graph  117 A indicates the relationship between the search result set data and the item as an axis, and the graph  119 A indicates the relationship between the query and the item as an axis. 
     In the radar chart  130 , an α1 axis is “healthy”, an α2 axis is “delicious”, and an α3 axis is “smell nice”, and the impression words “stuffed” and “stomach-friendly” extracted as candidate axis are set as a β1 axis and a β2 axis respectively. 
     As described above, in the present embodiment, when displaying the radar chart, the impression word extracted as a candidate axis may be the β axis. In the present embodiment, when displaying the radar chart, the β axis is determined based on the same method in the first and second embodiment, and another candidate axis associated with the impression word having the standard deviation larger than the predetermined threshold among the impression words as the α axis may be notified. 
     In the screen  115 B of  FIG. 26 , for example, the β axis of the radar chart  130  is, for example, the word extracted from the search word group  111 A extracted from the query, and the notification field  118  is displayed in association with the α3 axis. 
     As described above, in the present embodiment, in a case where there is a certain variation or more in the score of the impression word as an axis, it is possible to display the search result in which the intention of the user is reflected by notifying the user about another impression word may be used as an axis. 
     Fourth Embodiment 
     Hereinafter, the fourth embodiment will be described with reference to the drawings. The fourth embodiment is different from the third embodiment in that the axes are changed after receiving the selection of the impression word displayed as a candidate axis. In the description of the fourth embodiment below, only the differences from the third embodiment will be described. Those having the same functional configuration as those of the third embodiment are denoted by the same reference numerals as those used in the description of the third embodiment, and descriptions thereof will be omitted. 
       FIG. 27  is a diagram describing functions of a search device of a fourth embodiment. A search device  200 C of the present embodiment includes a search processing unit  240 C. 
     The search processing unit  240 C of the present embodiment includes an axis change unit  251  in addition to each unit of the search processing unit  240 B. 
     When the selection of the candidate axis displayed in association with the axis is received in the terminal device  300  and the like, for example, the axis change unit  251  of the present embodiment displays the graph in which the axis is changed according to the selection with respect to the output unit  246 . In a case where an axis is specified by the user, the axis change unit  251  of the present embodiment receives the selection of the candidate axis, and changes the impression word having the minimum value among the impression words with axis other than the specified axes with the impression word as the selected axis. 
     Hereinafter, the processing of the axis change unit  251  of the search processing unit  240 C of the present embodiment will be described with reference to  FIG. 28 . 
       FIG. 28  is a flowchart describing axis change processing in the fourth embodiment. In the search processing unit  240 C of the present embodiment, the axis change unit  251  determines whether or not to receive the selection of the candidate axis in the notification of the candidate axis (step S 2801 ). In step S 2801 , in a case where the selection is not received, the axis change unit  251  waits until the selection is received. 
     In step S 2801 , in a case where the selection of the axis is received, the axis change unit  251  determines whether or not there is the axis specified by the user (step S 2802 ). “specified” is the specification for maintaining the display. Therefore, in the present embodiment, the display of the specified axis is maintained. 
     In step S 2802 , in a case where there is a specified axis, the axis change unit  251  changes the axis with the smallest maximum score to the selected candidate axis among the axes (impression word) that are not specified by the user (step S 2803 ). 
     In step S 2802 , in a case where there is no specified axis, the axis change unit  251  changes the axis with the lowest score to the selected candidate axis among the displayed axes of the graph (step S 2804 ). 
     Following steps S 2803  and S 2804 , the axis change unit  251  instructs the display of the graph in which the axis is changed to the output unit  246  (step S 2805 ), and ends the processing. 
     Hereinafter, the change of the axis will be described in details with reference to  FIGS. 29A to 29C .  FIGS. 29A to 29C  are diagrams describing the change of axis. 
     In  FIG. 29A , a screen  291  in which a specification field  292  to specify the impression word extracted as the α axis is displayed in the screen  115  that the displays search result set data. 
     A screen  291 A illustrated in  FIG. 29B  is an example screen transitioning from the screen  291  in a case where the specification of an axis in the specification field  292  is not performed, and the candidate axis “stuffed” displayed in the notification field  118  is selected. 
     In the screen  291 , the impression words extracted as the axes  116  are “healthy”, “delicious”, and “smell nice”. In the notification field  118 , when “stuffed” is selected, since there is no specification of the specification field  292 , the axis change unit  251  changes the impression word having the smallest maximum score to the impression word “stuffed” among the impression words “healthy”, “delicious”, “smell nice” (see  FIG. 24 ). 
     Among the axes  116 , the impression word having the smallest maximum score is “smell nice”. Therefore, in the screen  291 A, axes  116 A in which the impression word “smell nice” is changed to the impression word “stuffed” is displayed. In the screen  291 A, a bar graph  117 A matched with the axes  116 A is displayed. 
     In the present embodiment, the changed impression word “smell nice” and the impression word “stomach-friendly” set as a candidate axis in the notification field  118  are displayed in a notification field  118 A as a candidate axis replaceable with the impression word “stuffed” in the screen  291 A. 
     In the present embodiment, as described above, since the changed axis is notified as a candidate axis, in a case of displaying the graph before the change, the changed axis “smell nice” displayed in the notification field  118 A may be selected and it is possible to easily return to the display before changing the axis. 
     A screen  291 B illustrated in  FIG. 29C  is an example screen transitioning from the screen  291  in a case where the impression word “stuffed” is selected as a candidate axis in the specification field  292  of the screen  291  in a state that the impression word “smell nice” as an axis is specified. 
     In a specification field  292 A of the screen  291 B, a check mark that specifies the impression word “smell nice” is displayed. In the screen  291 B, the display of the impression word “smell nice” as an axis is maintained. 
     Among the axes  116 , the impression word having the smallest maximum score and the impression word other than the impression word “smell nice” is “delicious”. Therefore, on the screen  291 B, axes  116 B in which the impression word “delicious” is changed to the impression word “stuffed” are displayed. In the screen  291 B, a bar graph  117 B matched to the axes  116 A is displayed. 
     In the screen  291 B, the changed impression word “delicious” and the impression word “stomach-friendly” specified as the candidate axis in the notification field  118  are displayed in a notification field  118 B as a candidate axis replaceable with the impression word “stuffed”. 
     As described above, according to the present embodiment, in the case of changing axis, it is possible to maintain the axis specified by the user. 
     In  FIGS. 29A to 29C , an example of a bar graph is described, but is possible to maintain the axis similarly specified as the change axis on the screen in which a radar chart is displayed. 
     Fifth Embodiment 
     Hereinafter, the fifth embodiment will be described with reference to the drawings. The fifth embodiment is different from the fourth embodiment in that in a case where the axis is changed, the score of the impression word map is corrected in response to the change. In the description of the fifth embodiment below, only the differences from the fourth embodiment will be described. Those having the same functional configuration as those of the fourth embodiment are denoted by the same reference numerals as those used in the description of the fourth embodiment, and descriptions thereof will be omitted. 
       FIG. 30  is a diagram describing functions of a search device of a fifth embodiment. A search device  200 D of the present embodiment includes the search target index storage unit  210 , an impression word map storage unit  220 B, the search target score storage unit  230 A, a search processing unit  240 D, and a user information storage unit  270 . 
     The impression word map storage unit  220 B of the present embodiment stores the impression word map  221 A and a correction list  222 . The impression word score output unit  244 A refers to the correction list  222  when correcting the acquired score. 
     The search processing unit  240 D of the present embodiment includes a correction list generation unit  252  and a score correction unit  253  in addition to the search processing unit  240 C of the fourth embodiment. 
     In a case where the axis is changed by the axis change unit  251 , the correction list generation unit  252  updates the correction list  222  to correct the score output from the impression word score output unit  244 A according to the change of axis or the maintenance of the display. Details of the correction list  222  will be described later. 
     The score correction unit  253  corrects the score output from the impression word score output unit  244 A based on the correction list  222 . 
     The user information storage unit  270  of the present embodiment stores a user information  271 . The user information  271  is information for identification of the user using a search system including the search device  200 D. For example, the user information  271  is a user ID for identifying the user, a password, and the like. The user information  271  of the present embodiment may be given to the search device  200 D in advance. 
     Next, the processing of the correction list generation unit  252  of the present embodiment will be described with reference to  FIG. 31 .  FIG. 31  is a flowchart describing processing of a correction list generation unit of the fifth embodiment. 
     Since the processing from step S 3101  to step S 3105  of  FIG. 31  is similar to the processing from step S 2801  to step S 2805  of  FIG. 28 , the description thereof is omitted. 
     When the candidate axis is changed, the search processing unit  240 D updates the correction list  222  according to the user information of the user who made the change and the change of axis or the specification of the axis by the correction list generation unit  252  (step S 3106 ), and ends the processing. 
     Hereinafter, the processing of step S 3106  will be further explained. First, the case in which the specification of the axis by the user is not performed when changing the axis will be described. 
     In this case, the correction list generation unit  252  stores the impression word indicating the axis to be changed, the value to be subtracted from the score of the impression word, the search word group acquired from the query, and the user information in association with each other in the correction list  222 . The impression word indicating the axis to be changed is the impression word having the smallest maximum score among the impression words extracted by the impression word score output unit  244 A as the axis. 
     Next, a case where the specification of the axis is performed by the user when changing the axis will be described. In this case, the correction list generation unit  252  stores the impression word indicating the axis specified by the user, the value added to the score of the impression word, the search word group acquired from the query, and the user information in association with each other in the correction list  222 . 
     Hereinafter, the correction list  222  of the present embodiment will be described with reference to  FIG. 32 .  FIG. 32  is a diagram illustrating an example of a correction list of the fifth embodiment. 
     The correction list  222  of the present embodiment includes the user ID, the search word group, and the change point as an information item in association with each other. The value of the item “user ID” indicates the user ID that identifies the user. The value of the item “search word group” indicates the search word group acquired from the input query. The value of the item “change point” indicates the changed impression word and the value to be subtracted from the score of the impression word, or the impression word specified by the user as an axis that maintains the display and the value to be added to the score of the impression word. 
     In the following description, in the correction list  222 , information including the value of the item “user ID” and the other values is referred to as correction information. In the description below, the value (first correction value) to be added to the score of the impression word or the value (second correction value) to be subtracted from the score of the impression word is referred to as a score correction value. The score correction value may be set in advance and stored in the correction list generation unit  252 . 
     In the example of  FIG. 32 , in correction information  222 - 1  including user ID “0001”, the values of the item “search word group” are “natural”, “material”, “vegetable”, and “restaurant”, and the value of the item “change point” is “smell nice” and “−0.1”. 
     When the user of the user ID “0001” displays the search result set data searched with the search word group “natural”, “material”, “vegetable”, and “restaurant” acquired from the input query, the correction information  222 - 1  indicates that impression word “smell nice” set as an axis is changed to another impression word. At this time, the correction information  222 - 1  indicates that there is no impression word set by the user to maintain the display in the axes, and the score correction value of “0.1” is subtracted from the impression word “smell nice”. 
     In correction information  222 - 2  of  FIG. 32 , when the user of the user ID “0002” displays the search result set data searched with the search word group “plentiful” and “Chinese” acquired from the input query, it is indicated that the display of the impression word “stuffed” is maintained among the axes by the user. The correction information  222 - 2  indicates that the score correction value of “0.1” is added to the score of the impression word “stuffed”. 
     Next, the processing of the impression word score output unit  244 A, the axis determination unit  245 A, and the score correction unit  253  of the present embodiment will be described with reference to  FIG. 33 .  FIG. 33  is a flowchart describing processing of an impression word score output unit, an axis determination unit, and a score correction unit of the fifth embodiment. 
     In the search device  200 D of the present embodiment, the input of the user information is received and the login processing is performed before the user inputs the query. 
     Since the processing from step S 3301  to step S 3302  of  FIG. 33  is similar to the processing from step S 2301  to step S 2302  of  FIG. 23 , the description thereof is omitted. 
     In step S 3302 , when the score and the standard deviation for each impression word are acquired, the score correction unit  253  determines whether or not there is the correction information including the input user ID in the correction list  222  (step S 3303 ). In step S 3303 , in a case where there is no corresponding correction information, the search processing unit  240 D proceeds to step S 3306  described later. 
     In step S 3303 , in a case where there is corresponding correction information, the score correction unit  253  determines whether or not there is search word group acquired from the input query and correction information that matches with the search word group in the corresponding correction information (step S 3307 ). In step S 3307 , in a case where there is no corresponding correction information, the search processing unit  240 D proceeds to step S 3306  described later. 
     In step S 3307 , in a case where there is corresponding correction information, the score correction unit  253  refers to the value of the item “change point” of the correction information, corrects the score for each impression word acquired in step S 3302  (step S 3305 ), and proceeds to step S 3306 . 
     Since the processing from step S 3306  to step S 3311  is similar to the processing from step S 2303  to step S 2308  of  FIG. 23 , the description thereof is omitted. 
     Hereinafter, the processing of  FIG. 33  will be described in details with reference to  FIG. 24 . For example, a case in which the user of the user ID “0001” inputs a query of “restaurant using natural materials and vegetables” and the search word group  111 A is acquired will be described. 
     In this case, the search processing unit  240 D acquires the impression word score  113 A for each word by the impression word score output unit  244 A. At this time, the score correction unit  253  refers to the correction list  222 , and determines whether or not there is correction information including the user ID “0001”. 
     In the correction list  222 , there is the correction information  222 - 1  that includes user ID “0001” (see  FIG. 32 ). Therefore, the score correction unit  253  determines whether or not the value of the item “search word group” of the correction information  222 - 1  and the search word group  111 A acquired from the input query matches. 
     Since they match, the score correction unit  253  refers to item “change point” of the correction information  222 - 1 , and performs a correction of subtracting 0.1 from the score corresponding to the impression word “smell nice” of the impression word score  113 A for each word. 
     As described above, in the present embodiment, in a case where the user changes the impression word as the axis once, or specified the impression word to maintain the display, the operation history is stored for each user as the correction information. In the present embodiment, in a case where the search word group acquired from the query matches, it is possible to reflect the intention of the user on the item when displaying the search result set data by correcting the score of the impression word using the correction information. 
     Sixth Embodiment 
     Hereinafter, the sixth embodiment will be described with reference to the drawings. The sixth embodiment is different from the first embodiment in that the appropriate number of search result set data pieces is displayed when the operation to change the score of the impression word is received on the output axis. In the following description of the sixth embodiment, only the differences from the first embodiment will be described. Those having the same functional configuration as those of the first embodiment are denoted by the same reference numerals as those used in the description of the first embodiment, and descriptions thereof will be omitted. 
       FIG. 34  is a diagram describing a display example of the sixth embodiment. On a screen  341  illustrated in  FIG. 34 , a plurality of output axes  342  and search result set data pieces  343  are displayed. 
     In the screen  341 , the output axes  342  indicates the impression word with a strong relationship with the search word group based on the input query. 
     On the screen  341 , a scale  344  and a slider  345  to change the score of the impression word for each impression word as the output axes  342  are displayed. In the present embodiment, when the slider  345  is moved on the scale  344  and the slider  345  stops on a graduation on the scale  344  set in advance, the score of the impression word is changed to the value corresponding to the position where the slider  345  is stopped. When the score of the impression word is changed, the search result set data  343  becomes the search result using the score after the change. 
     In the description below, the slider on the scale stops on the graduation, and the graduation marked on the scale is referred to as a slider stop position. In the description below, the value (graduation) indicated by the slider stop position on the scale may be represented as a slider value. 
     The search result set data  343  is document data indicated by the document ID specified by the search processing. In the screen  341 , it is displayed as the score of each output axis “premier”, “calm”, and “plentiful” and other document data extracted as search result set data pieces in the document data with highest degree of match with the query. 
     The display of the search result set data in a case where the score of the impression word is changed will be described. The number of search results (search result set data pieces) corresponding to the score of the impression word is different depending on the distribution of the score in the plurality of extracted search result set data. In a case where the value of the score is changed by moving the slider on the scale, a large number of search result set data pieces may be extracted, or only a small number of search result set data pieces may be extracted. 
       FIGS. 35A and 35B  are diagrams describing change in score of impression words.  FIG. 35A  is a graph indicating the distribution of each score of the impression words “calm” and “plentiful” as an output axis.  FIG. 35B  is a diagram illustrating the relationship between the score indicated by the slider and the search result set data. 
     As illustrated in  FIG. 35A , the impression word “calm” has a small deviation of score with respect to the search result set data, and the impression word “plentiful” has a large deviation of score with respect to the search result set data. 
     As illustrated in  FIG. 35B , for example, a case where five slider stop positions (slider value “1” to “5”) are at equal interval are provided between the slider stop position (slider value “0”) as a start of the scale and the slider stop position (slider value “6”) at the end point of the scale, and the score is changed by moving the slider to each slider stop position is considered. 
     In this case, since the impression word “calm” has a small deviation of score, regardless of the slider stop position of the slider, a certain number of search result set data pieces is output. 
     On the other hand, since the impression word “plentiful” has a large deviation of score, depending on the slider stop position, the search result set data may not be output, or a lot of search result set data may be output. For example, in a case where the score of the impression word “plentiful” is changed by moving the slider on the scale, the search result set data may not be output properly. 
     In the present embodiment, the distribution of the impression word as the output axis is calculated, and the slider stop position on the scale is associated with the variation range of the score according to the score distribution. For example, according to the present embodiment, each slider stop position on the scale and the variation range of the score in association with each other is displayed so that the number of search result set data pieces displayed corresponding to the variation range of the score to be a predetermined number for each output axis. The predetermined number may be a fixed number or a number within a predetermined range. For example, the number of search result set data pieces displayed corresponding to the variation range of the score may be 10, or 8 to 12. 
     In the present embodiment, by performing the association, a certain number of search result set data pieces may be displayed when the slider is moved on the scale. 
     According to the present embodiment, the score of the output axes (impression words) is changed, and the search result set data corresponding to the changed score is presented to the user. Therefore, according to the present embodiment, for example, in a case where information desired by the user is not acquired from the score estimated from the query, new search result in which the score of the impression word is changed may be provided to the user. 
     Hereinafter, the functions of a search device  200 E of the present embodiment will be described with reference to  FIG. 36 .  FIG. 36  is a diagram describing functions of a search device of the sixth embodiment. 
     The search device  200 E of the present embodiment includes the search target index storage unit  210 , the impression word map storage unit  220 , the search target score storage unit  230 A, and a search processing unit  240 E. 
     The search processing unit  240 E of the present embodiment includes the input receiving unit  241 , the analysis unit  242 , the search unit  243 , the impression word score output unit  244 , the axis determination unit  245 , an output unit  246 A, the index generation unit  247 , the impression space learning unit  248 , a score distribution calculation unit  260 , and a scale calculation unit  261 . 
     The score distribution calculation unit  260  of the present embodiment calculates the score distribution for each impression word determined as the output axis by the axis determination unit  245 . For example, the score distribution calculation unit  260  generates a histogram indicating the score distribution in the impression word score list  231 A for each impression word determined as the output axis by the axis determination unit  245 . 
     The scale calculation unit  261  of the present embodiment calculates the variation range of the score corresponding to the slider stop position attached on the scale indicating the score of the impression word based on the distribution of the score calculated by the score distribution calculation unit  260 . For example, the scale calculation unit  261  creates and stores a correspondence table  410  in which the slider value is associated with the variation range of the score. 
     Details of the processing of the score distribution calculation unit  260  and the scale calculation unit  261  and the correspondence table  410  will be described later. 
     The output unit  246 A of the present embodiment refers to the correspondence table  410 , and displays a radar chart of the search result set data. 
     Hereinafter, the processing of the search processing unit  240 E of the present embodiment will be described with reference to  FIG. 37 .  FIG. 37  is a first flowchart describing processing of a search processing unit of the sixth embodiment. 
     Since the processing from step S 3701  to step S 3704  of  FIG. 37  is similar to the processing from step S 901  to step S 904  of  FIG. 9 , the description thereof is omitted. 
     In step S 3704 , when the n number of impression words that has a clear relationship with the query are extracted and set as a axes by the axis determination unit  245 , the search processing unit  240 E calculates the score distribution for each output axis (a axis) in search result set data pieces specified in step S 3701  by the score distribution calculation unit  260  (step S 3705 ). 
     The search processing unit  240 E associates the slider stop position with the variation range of the score for each output axis by the scale calculation unit  261  (step S 3706 ). 
     The search processing unit  240 E sets the score of the impression word when the α axis is set as an initial value on the scale by the output unit  246 A (step S 3707 ). 
     The output unit  246 A draws the search result set data with the slider and the scale that correspond with the impression word as the output axis (step S 3708 ), and ends the processing. 
     Next, the processing of the score distribution calculation unit  260  of the present embodiment will be described with reference to  FIG. 38 .  FIG. 38  is a second flowchart describing processing of the search processing unit of the sixth embodiment. In  FIG. 38 , the details of the processing of step S 3705  of  FIG. 37  is illustrated. 
     The score distribution calculation unit  260  of the present embodiment selects one axis among the output axes determined by the axis determination unit  245 , and fixes the score of the other output axes (step S 3601 ). The score distribution calculation unit  260  changes the score of the selected output axis (step S 3602 ). The score of the selected output axis may be increased or decreased for each predetermined interval. For example, in a case where the minimum value of the score of the output axis is 0, and the maximum value is 10, the score is changed to 0, 1, 2, . . . , 10, and so on. 
     The score distribution calculation unit  260  performs vector matching between the score of each output axis and the score of each impression word corresponding to the document data extracted as the search result set data in a state that the score of the selected output axis is changed (step S 3603 ). For example, the score distribution calculation unit  260  may perform vector matching based on cosine similarity. It is assumed that a plurality of document data are extracted as the search result set data. 
     The score distribution calculation unit  260  specifies the document ID in which the result of the vector matching is equal to or greater than the predetermined threshold, and acquires the number of specified document IDs (step S 3604 ). 
     The score distribution calculation unit  260  determines whether or not the score of the selected output axis is changed from the minimum value to the maximum value (step S 3605 ). For example, the score distribution calculation unit  260  determines whether or not the vector matching is performed for each predetermined interval from the minimum value to the maximum score. 
     In step S 3605 , in a case where the vector matching is not performed for each predetermined interval from the minimum score to the maximum score, the score distribution calculation unit  260  returns to step S 3602 . 
     In step S 3605 , in a case where the vector matching is performed for each predetermined interval from the minimum score to the maximum score, the score distribution calculation unit  260  generates a histogram from the number of document IDs of each predetermined interval (step S 3606 ). 
     The score distribution calculation unit  260  determines whether or not the processing from step S 3601  to step S 3606  is performed for every output axes (step S 3607 ). The score distribution calculation unit  260  may determine whether or not the processing from step S 3601  to step S 3606  is performed for the α axis among the output axes. 
     In step S 3607 , in a case where processing is not performed for all output axes, the score distribution calculation unit  260  returns to step S 3601 . In step S 3607 , when the processing is performed for all output axes, the score distribution calculation unit  260  ends the processing. 
     Next, the processing of the scale calculation unit  261  of the present embodiment will be described with reference to  FIG. 39 .  FIG. 39  is a third flowchart describing processing of the search processing unit of the sixth embodiment. In  FIG. 38 , details of the processing of step S 3706  in  FIG. 37  is illustrated. 
     The scale calculation unit  261  of the present embodiment acquires a score range (from minimum score to maximum score), a histogram, and the X number of document IDs acquired when generating the histogram for each output axis (step S 3901 ). 
     The scale calculation unit  261  acquires the N number of slider values (graduation) on the scale for each output axis (step S 3902 ). 
     The scale calculation unit  261  selects an output axis (step S 3903 ), and creates a correspondence table so that the number of document IDs corresponding the number of slider values is set to be X/N (step S 3904 ). 
     The scale calculation unit  261  determines whether or not the correspondence table is created for all output axes (step S 3905 ). In step S 3905 , in a case where the correspondence table is not created for all output axes, the scale calculation unit  261  returns to step S 3903 . 
     In step S 3905 , in a case where the correspondence table is created for all output axis, the scale calculation unit  261  ends the processing. 
     The processing of the score distribution calculation unit  260  and the scale calculation unit  261  of the present embodiment will be described in details with reference to  FIGS. 40A and 40B .  FIGS. 40A and 40B  are diagrams describing correspondence between a score and a slider value of the sixth embodiment.  FIG. 40A  is an example histogram, and  FIG. 40B  is an example table illustrating the association between the score and the slider value. 
     In  FIG. 40A , the horizontal axis indicates the score of the output axis “premier”, and the vertical axis indicates the number of document IDs that the result of vector matching is equal to or greater than the threshold value. 
     In the output axis “premier”, as is known from  FIG. 40A , the score range is from 0 to 10. While the score is 2 to 5, there are many numbers of corresponding search result set data, but in a case where the score is 8 or higher or less than 2, there is almost no corresponding search result set data. 
     Therefore, in the present embodiment, in a case where the output axis “premier” is displayed as scale, the slider value (graduation) and the number of document IDs are associated with each other. 
       FIG. 40B  is an example of a correspondence table related to the output axis “premier”. In this case, the slider value “0” and the score “0 to 3.5”, the slider value “1” and the score “3.5 to 3.8”, and the slider value “2” and the score “3.8 to 4.6” are associated with each other. 
     For example, in the scale of the output axis “premier”, in a case where the slider is at the position of the slider value “0”, document data corresponding to the document ID in which the score of the impression word “premier” is 0 to 3.5 among the extracted search result set data is displayed. In the scale of the output axis “premier”, when the slider is at a position of the slider value “1”, document data corresponding to the document ID in which the score of the impression word “premier” is 3.5 to 3.8 among the extracted search result set data is displayed. 
     As described above, in the present embodiment, by associating the slider value attached on the scale of the output axis and the variation range of the score, regardless of which slider value the slider is stopped on the scale, it is possible to equalize the number of output search result set data pieces. In the present embodiment, the association is performed for every output axes, and stored as the correspondence table  410 . 
       FIG. 41  is an example correspondence table of the sixth embodiment. In the correspondence table  410  illustrated in  FIG. 41 , the slider value and the variation range of the score are associated with each other for each output axis “premier”, “hideaway”, and “healthy”. 
     In the correspondence table  410 , with respect to the slider value “0”, the output axis “premier” is associated with the score “0 to 3.5”, the output axis “hideaway” is associated with the score “0 to 2.8”, and the output axis “healthy” is associated with the score “0 to 2.9”. 
     In the correspondence table  410 , with respect to the slider value “1”, the output axis “premier” is associated with the score “3.5 to 3.8”, the output axis “hideaway” is associated with the score “2.8 to 3.6”, and the output axis “healthy” is associated with the score “2.9 to 3.2”. 
       FIG. 42  is a diagram illustrating a display example of the sixth embodiment. A screen  421  illustrated in  FIG. 42  is displayed in, for example, the terminal device  300  and the like. 
     On the screen  421 , an input field  422  and a result display field  423  are displayed. In the input field  422 , a query input field  424  to input the query, a search button  425  that performs search request, and a plurality of scales  426  associated with each output axis are displayed. On the plurality of scales  426 , a slider  426   a  is displayed respectively, and the score of the corresponding output axis is changed by moving the slider on the scale. 
     In the result display field  423 , the document data specified based on the input query in the query input field  424  is displayed as the search result set data. In the example of  FIG. 42 , search result set data  427 ,  428 , and  429  is displayed as the search result. 
     In the present embodiment, on at least one of the plurality of scales  426 , when the slider  426   a  is operated and the score is changed, the search result set data displayed in the result display field  423  changes as the score changes. 
     As described above, in the present embodiment, when changing the score of the impression word estimated from the search word group based on the query and performing the search, it is possible to display the appropriate number of search result set data pieces according to the change in the score of the impression word. 
     In the present embodiment, as a display mode for changing the score of the impression word, a mode in which the score is changed on the scale to which the slider value is attached is described, but the display mode is not limited this. In the present embodiment, as long as it is a mode that receives the change in score, and displays the search result set data corresponding to the changed score, any display mode may be used. For example, it may be a mode that an input field to input score is provided, and the change in the score of the impression word is received by input of the score with respect to the input field. 
     In the present embodiment, in a case where there is a deviation in the score distribution, the range selected by the slider may be limited, or the display mode of the scale may be displayed in a hue matching with the score distribution. 
       FIGS. 43A and 43B  are diagrams illustrating another display example of the sixth embodiment. In  FIG. 43A , a display example of the slider in which the range in which the score is selected by the slider is limited is illustrated, and  FIG. 43B  is a display example in a case where the scale is displayed in a hue matching with the score distribution. 
     In  FIG. 43A , a scale  431 - 1  corresponding to the output axis “premier”, a scale  431 - 2  corresponding to the output axis “hideaway”, and a scale  431 - 3  corresponding to the output axis “healthy” are displayed. 
     The scale  431 - 1  includes an unselectable area  432 - 1  that is not selectable by the slider, and a selectable area  433 - 1  that is selectable by the slider. In this case, the scale  431 - 1  indicates that there is no search result set data in the score range indicated by the unselectable area  432 - 1 , and the search result set data is distributed in the score range indicated by the selectable area  433 - 1 . 
     In this case, even if any position of the selectable area  433 - 1  is selected by the slider, the scale calculation unit  261  may set the number of output search result set data pieces to be the same, or substantially the same. 
     Similarly, the scale  431 - 2  includes an unselectable area  432 - 2  and a selectable area  433 - 2 , and the scale  431 - 3  includes an unselectable area  432 - 3  and a selectable area  433 - 3 . 
     In the scales  431 - 1 ,  431 - 2 , and  431 - 3 , the unselectable areas  432 - 1 ,  432 - 2 , and  432 - 3  may be displayed in a different manner from the selectable areas  433 - 1 ,  433 - 2 , and  433 - 3 . 
     In the present embodiment, as described in  FIG. 43A , it is possible to make the user to visually recognize that the range in which the score is selected by the slider is limited depending on the display mode of the scale. 
     In  FIG. 43B , a scale  434 - 1  corresponding to the output axis “premier”, a scale  434 - 2  corresponding to the output axis “hideaway”, and a scale  434 - 3  corresponding to the output axis “healthy” are displayed. 
     In the example of  FIG. 43B , the selectable areas  435 - 1 ,  435 - 2 , and  435 - 3  in each scale are displayed with red density being increased in order from an area where the number of search result set data pieces is high. 
     In the present embodiment, by displaying the scale in this way, it is possible to make the user to visually recognize the score range where the number of the search result set data pieces is large. In this case, the user may select the dark red area with the slider in a case where the user desires to display other search result set data. 
     As described above, according to the present embodiment, even in a case where the impression word as an item displaying the search result set data, and the score indicating the strength of the relationship with the query are changed, it is possible to output the appropriate number of search result set data pieces. 
     Seventh Embodiment 
     Hereinafter, the seventh embodiment will be described with reference to the drawings. The seventh embodiment is different from the sixth embodiment in that the range of the score associated with the slider value is limited. In the description of the seventh embodiment below, only the differences from the sixth embodiment will be described. Those having the same functional configuration as those of the sixth embodiment are denoted by the same reference numerals as those used in the description of the sixth embodiment, and descriptions thereof will be omitted. 
       FIG. 44  is a graph describing correspondence between a score and a slider value of a seventh embodiment. 
     In  FIG. 44 , the horizontal axis indicates the score of the output axis “premier”, and the vertical axis indicates the number of document IDs where the result of vector matching is equal to or greater than the threshold value. In the example of  FIG. 44 , the initial value of the score of the output axis “premier” is “6”. The initial value is the score of the impression word “premier” corresponding with the document ID with the strongest relationship with the query. 
     In the example of  FIG. 44 , the slider value and the variation range of the score are associated with each other within a predetermined range H centered on the initial value as the score range. The predetermined range H may be set in advance. 
     For example, in the present embodiment, the range of the score “4 to 8” is expressed on the scale with the minimum value of the score being “4” and the maximum value being “8” 
       FIG. 45  is a flowchart describing processing of a search processing unit of the seventh embodiment. The scale calculation unit  261  of the present embodiment acquires the score range (from minimum score to maximum score), the histogram, and the X number of document IDs acquired when the histogram is generated for each output axis (step S 4501 ). 
     The scale calculation unit  261  acquires the Y number of search result set data pieces corresponding to the predetermined score range set in advance for each output axis (step S 4502 ). 
     Since the processing of step S 4503  and step S 4504  of  FIG. 45  are similar to the processing of step S 3902  and step S 3903  of  FIG. 39 , description thereof is omitted. 
     The scale calculation unit  261  creates a correspondence table so that the number of document IDs corresponding to the slider value is set to be Y/N for the selected output axis (step S 4505 ). 
     Since the processing of step S 4506  of  FIG. 45  is similar to the processing of step S 3905  of  FIG. 39 , description thereof is omitted. 
       FIG. 46  is an example correspondence table of the seventh embodiment. In a correspondence table  460  illustrated in  FIG. 46 , the score 4 to 8 is associated with the slider value 0 to 5. 
     In the present embodiment, in the scale of the output axis “premier”, the search result set data with score 4 or less or 8 or higher is not output even if the slider value is moved from 0 to 5. 
     In the present embodiment, as described above, by limiting the range of the score associated with the slider value, the output of the search result set data with a low relationship with the input query may be suppressed. 
     In the disclosed technology, a mode such as that described below may be considered. 
     The present embodiments are not limited to the specifically disclosed embodiments, and various modifications and changes may be made without departing from the scope of the claims. 
     All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.