Patent Publication Number: US-2020285808-A1

Title: Synonym dictionary creation apparatus, non-transitory computer-readable recording medium storing synonym dictionary creation program, and synonym dictionary creation method

Description:
TECHNICAL FIELD 
     The present invention relates to a synonym dictionary creation apparatus, a synonym dictionary creation program, and a synonym dictionary creation method that create a synonym dictionary. 
     BACKGROUND ART 
     A synonym dictionary is used to absorb an orthographic variant when a document is searched or analyzed, for example. 
     In creating a synonym dictionary, a similarity between multiple words collected from a text is obtained, the similarity is used to perform synonym determination, and a synonym dictionary is created that registers words determined as synonyms by the synonym determination. The synonym determination may be performed based on a history of word use in search, analysis, or the like in a document, or on word attributes, such as context, notation, pronunciation, and a part of speech. A technique described in Patent Document 1 is an example of the former, and a technique described in Patent Document 2 is an example of the latter. 
     In the technique described in Patent Document 1, an interval relevance dictionary that defines relevance between words based on a search time interval of an identical user is created. Then, a time series relevance dictionary that defines relevance between words based on a time series correlation of frequency of use of each search words is created. The interval relevance dictionary and the time series relevance dictionary are used to group synonyms, and a synonym dictionary is created (paragraphs 0012, 0014, and 0033). 
     In the technique described in Patent Document 2, a reference vocabulary is acquired, and a synonym index for the reference vocabulary and a related vocabulary are obtained using the similarity of context, notation, pronunciation, and a part of speech. Then, based on the size of the synonym index, it is determined whether the related vocabulary is a synonym of the reference vocabulary. Thus, a synonym dictionary is output (paragraphs 0013, 0018, and 0022). 
     PRIOR ART DOCUMENTS 
     Patent Documents 
     Patent Document 1: Japanese Patent Application Laid-Open No. 11-312168 (1999) 
     Patent Document 2: Japanese Patent Application Laid-Open No. 2013-16011 
     SUMMARY 
     Problems to be Solved by the Invention 
     A conventional process of creating a synonym dictionary has such problems that a highly precise synonym determination cannot be performed, a synonym dictionary may not be created, and the creation of a synonym dictionary takes a lot of time. 
     For example, in the technique described in Patent Document 1, without a past search history, a highly precise synonym determination cannot be performed, or the synonym dictionary cannot be created. 
     In the technique described in Patent Document 2, since the reference vocabulary needs to be acquired, it takes time to acquire the reference vocabulary, and it takes an enormous amount of time to output a synonym dictionary. In addition, omission of a word occurs when a word that is included in a text and should be included in the synonym dictionary is not included in the synonym dictionary. A repetitive maintenance is required every time a word is omitted, taking an enormous amount of time to create the synonym dictionary. Furthermore, a highly precise synonym determination cannot be performed on the text including a vocabulary, such as a technical term, whose pronunciation and part of speech are not registered, or the text including synonyms whose pronunciation and part of speech are different from each other, for example. 
     The present invention is made to solve the above-described problems. To solve the problems, the present invention provides a synonym dictionary creation apparatus, a synonym dictionary creation method, and a synonym dictionary creation program that automatically generate a synonym dictionary from a text by a highly precise synonym determination. 
     Means to Solve the Problems 
     The present invention is directed to a synonym dictionary creation apparatus, a synonym dictionary creation program, and a synonym dictionary creation method. 
     In creating the synonym dictionary, morpheme analysis is performed on a text, the text is segmented into multiple words, and thereby a morpheme-analyzed text is obtained. 
     A topic classification is carried out on the morpheme-analyzed text, at least one topic word belonging to each topic is selected from the multiple words, and a reference word characterizing each topic is extracted from the at least one topic word. 
     The multiple words are multidimensionally vectorized, and thereby multiple vectors respectively expressing the multiple words are obtained. 
     At least one similar word is selected from the multiple words. In this case, a similarity between a vector expressing the reference word and a vector expressing each similar word of the at least one similar word exceeds a set reference. 
     The synonym dictionary is created in which at least a part of the at least one similar word has been registered. 
     Effects of the Invention 
     The present invention is to provide a synonym dictionary creation apparatus, a synonym dictionary creation method, and a synonym dictionary creation program that carry out a highly precise synonym determination and automatically generate a synonym dictionary from a text. 
     The object, features, aspects, and advantages of the present invention will be more apparent from the following detailed description and the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a hardware configuration of a synonym dictionary creation apparatus according to a first embodiment. 
         FIG. 2  is a block diagram illustrating a functional configuration of the synonym dictionary creation apparatus according to the first embodiment. 
         FIG. 3  is a flowchart showing processing performed by the synonym dictionary creation apparatus according to the first embodiment. 
         FIG. 4  is a schematic diagram showing an example of a data transition in the synonym dictionary creation apparatus according to the first embodiment. 
         FIG. 5  is a schematic diagram showing an example of a data transition in the synonym dictionary creation apparatus according to the first embodiment. 
         FIG. 6  is a schematic diagram showing an example of a data transition in the synonym dictionary creation apparatus according to the first embodiment. 
         FIG. 7  is a schematic diagram illustrating an example of a screen displayed in the synonym dictionary creation apparatus according to the first embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENT 
       1  Hardware Configuration 
       FIG. 1  is a block diagram illustrating a hardware configuration of a synonym dictionary creation apparatus according to a first embodiment. 
     The synonym dictionary creation apparatus  1000  illustrated in  FIG. 1  is a personal computer (PC) in which a synonym dictionary creation program  1020  is installed, and includes a central processing unit (CPU)  1040 , a memory  1041 , a hard disk drive  1042 , and a display  1043 . The synonym dictionary creation apparatus  1000  may include a component other than the above components. 
     In the synonym dictionary creation apparatus  1000 , the synonym dictionary creation program  1020  is installed in the hard disk drive  1042 . For the installation of the synonym dictionary creation program  1020 , data read from an external storage medium  1060 , such as a compact disc (CD), a digital versatile disc (DVD), or a universal serial bus (USB) memory, may be written to the hard disk drive  1042 , or data received via a network  1080  may be written to the hard disk drive  1042 . The hard disk drive  1042  may be replaced with another type of auxiliary storage apparatus. For example, the hard disk drive  1042  may be replaced with a solid state drive or a random access memory (RAM) disk. The hard disk drive  1042 , the external storage medium  1060 , the solid state drive, the RAM disk, and the like are computer-readable recording media that record the synonym dictionary creation program  1020 . 
     In the synonym dictionary creation apparatus  1000 , the synonym dictionary creation program  1020  installed in the hard disk drive  1042  is loaded into the memory  1041 . The loaded synonym dictionary creation program  1020  is executed by the CPU  1040 , and thereby the PC executes the synonym dictionary creation program  1020  to function as the synonym dictionary creation apparatus  1000 . 
       2  Functional Configuration 
       FIG. 2  is a block diagram illustrating a functional configuration of the synonym dictionary creation apparatus according to the first embodiment. 
     As illustrated in  FIG. 2 , the synonym dictionary creation apparatus  1000  includes a removal unit  1100 , a morpheme analysis unit  1101 , an extraction unit  1102 , a multidimensional vectorization unit  1103 , a selection unit  1104 , a creation unit  1105 , and a storage unit  1106 . A synonym dictionary  1207  is automatically created from a text  1200  to be searched or analyzed. The synonym dictionary creation apparatus  1000  may include a component other than the above components. The storage unit  1106  stores a dictionary  1300  of forcibly extracted words, an exclusion word dictionary  1301 , an external dictionary  1302 , and an existing synonym dictionary  1303 . 
     The removal unit  1100 , the morpheme analysis unit  1101 , the extraction unit  1102 , the multidimensional vectorization unit  1103 , the selection unit  1104 , and the creation unit  1105  are configured through execution of the synonym dictionary creation program  1020  by the PC. The storage unit  1106  includes at least one of the memory  1041  and the hard disk drive  1042 . 
     All or part of the processing performed by the CPU  1040  may be performed by a processor other than the CPU  1040 . For example, all or part of the processing performed by the CPU  1040  may be performed by a graphics processing unit (GPU). All or part of the processing performed by the CPU  1040  may be performed by hardware that does not execute the program. 
     The removal unit  1100  removes a stop word from a pre-removal text  1200  from which the stop word has not been removed to obtain a post-removal text  1201  from which the stop word has been removed. When the removal of a stop word is unnecessary, for example, when the text  1200  to be searched or analyzed does not include a stop word, the removal unit  1100  may be omitted. 
     The morpheme analysis unit  1101  performs morpheme analysis on the post-removal text  1201  to segment the post-removal text  1201  into multiple words  1202  to obtain a morpheme-analyzed text  1203  including the multiple words  1202 . The morpheme analysis unit  1101  uses the dictionary  1300  of forcibly extracted words in the morpheme analysis for the post-removal text  1201 . The use of the dictionary  1300  of forcibly extracted words may be omitted. 
     The extraction unit  1102  performs a topic classification on the morpheme-analyzed text  1203 , selects at least one topic word belonging to each topic from the multiple words  1202  included in the morpheme-analyzed text  1203 , and extracts a feature word characterizing each topic from the at least one topic word belonging to each topic. The feature word that has been extracted becomes a reference word  1204  that is a reference for selecting a similar word. 
     The multidimensional vectorization unit  1103  multidimensionally vectorizes the multiple words  1202  to obtain multiple vectors  1205  respectively expressing the multiple words  1202 . 
     The selection unit  1104  selects at least one similar word similar to the reference word  1204  from the multiple words  1202  to create a similar word list  1206  including the at least one similar word that has been selected. The selection unit  1104  selects the at least one similar word, such that a similarity between a vector expressing the reference word  1204  and a vector expressing each similar word of the at least one similar word exceeds a set reference. 
     The creation unit  1105  creates the synonym dictionary  1207  from the similar word list  1206 , and saves the synonym dictionary  1207  that has been created. The creation unit  1105  organizes the at least one similar word included in the similar word list  1206  in creating the synonym dictionary  1207 . The creation unit  1105  also uses the exclusion word dictionary  1301 , the external dictionary  1302 , and the existing synonym dictionary  1303  in creating the synonym dictionary  1207 . Thus, at least a part of the at least one similar word included in the similar word list  1206  is registered in the synonym dictionary  1207 . Organizing the at least one similar word may be omitted. Use of at least a part of the exclusion word dictionary  1301 , the external dictionary  1302 , and the existing synonym dictionary  1303  may be omitted. When organizing of the at least one similar word is omitted and the use of all of the exclusion word dictionary  1301 , the external dictionary  1302 , and the existing synonym dictionary  1303  is omitted, all of the at least one similar word included in the similar word list  1206  is registered in the synonym dictionary  1207 . 
     In the synonym dictionary creation apparatus  1000 , the reference word  1204  that is predicted to be used in the search or analysis is automatically extracted from the text  1200  to be searched or analyzed. The at least one similar word similar to the reference word  1204  that has been extracted is automatically selected from the text  1200 . Then, at least a part of the at least one similar word that has been selected is automatically registered in the synonym dictionary  1207 . Thus, a synonym dictionary  1207  that registers the at least one similar word similar to the reference word  1204  predicted to be used in the search or analysis is automatically created from the text  1200 . 
     Further, in the synonym dictionary creation apparatus  1000 , synonym determination is performed based on the similarity between the vector expressing the reference word  1204  and the vector expressing each word of the multiple words  1202 . For this reason, a highly precise synonym determination is performed even without a history of word use in search, analysis, or the like in a document, or without word attributes, such as context, notation, pronunciation, and a part of speech. In particular, even when a technical term that is not registered in a general dictionary is included, the highly precise synonym determination is performed. 
     Further, in the synonym dictionary creation apparatus  1000 , by using the topic classification performed on the text  1200  to be searched or analyzed, a feature word that is the reference word  1204  is extracted from the text  1200 . Thus, a reference word group that covers words included in the text  1200  and predicted to be used in the search or analysis is extracted. Thus, the word predicted to be used in the search or analysis is unlikely to be dropped. On the contrary, in manual extraction of the reference word group by a human, the word predicted to be used in the search or analysis is likely to be dropped. 
     Further, in the synonym dictionary creation apparatus  1000 , the morpheme analysis is performed on the text  1200  to be searched or analyzed to segment the text  1200  into the multiple words  1202 . The at least one similar word is selected from the multiple words  1202  obtained from the segmentation. Then, at least a part of the at least one similar word that has been selected is automatically registered in the synonym dictionary  1207 . Therefore, a synonym group that covers the synonyms included in the text  1200  is registered in the synonym dictionary  1207 . 
       3  Examples of Processing and Data Transition 
       FIG. 3  is a flowchart showing processing performed by the synonym dictionary creation apparatus according to the first embodiment.  FIGS. 4, 5, and 6  are schematic diagrams each showing an example of a data transition in the synonym dictionary creation apparatus according to the first embodiment. 
     In step S 101  illustrated in  FIG. 3 , the removal unit  1100  removes the stop word from the text  1200  to be searched or analyzed to obtain the post-removal text  1201 . The stop word to be removed is a word that acts as noise unnecessary for a subsequent analysis. The word to be removed as the stop word is an identification code or the like that does not express a specific content of the text  1200 . Character strings that are commonly included in various URLs such as “http://” are also removed as the stop words. In the example shown in  FIG. 4 , a text element  1400  “R000003”, a text element  1401  “customization of development process”, a text element  1402  “master data (user, project, product, . . . )”, a text element  1403  “R000002”, a text element  1404  “of process ratio at the time of prediction formula registration . . .”, and a text element  1405  “for input of process ratio, input to the second decimal place is possible . . .” are included in the text  1200 . The text elements  1400  and  1403  are removed as stop words. 
     In step S 102  following step S 101 , illustrated in  FIG. 3 , the morpheme analysis unit  1101  performs the morpheme analysis on the post-removal text  1201 , and segments the post-removal text  1201  into the multiple words  1202 . Then, the morpheme-analyzed text  1203  including the multiple words  1202  is obtained. In the example illustrated in  FIG. 4 , the text element  1401  is segmented into multiple words  1411  “development process” and “customization”. The text element  1402  is segmented into the multiple words  1412 , such as “master data”, “user”, “project”, and “product”. The text element  1404  is segmented into multiple words  1414 , such as “prediction formula”, “registration”, “time”, “at”, “process”, “ratio”, and “of”. The text element  1405  is segmented into multiple words  1415 , such as “process”, “ratio”, “of”, “input”, “for”, “decimal”, “second place”, “to”, “input”, “possible”, and “is”. 
     Using the dictionary  1300  of forcibly extracted words that registers a technical term that is a compound word including two or more morphemes, the morpheme analysis unit  1101  forcibly extracts, from the post-removal text  1201 , the technical term registered in the dictionary  1300  of forcibly extracted words. Then, the morpheme analysis unit  1101  segments the post-removal text  1201  into the multiple words  1202 , such that the multiple words  1202  include the technical term that has been extracted. Thus, the technical term that is a compound word is normally extracted without being segmented. In the example shown in  FIG. 4 , a technical term  1420  “master data” and the technical term  1421  “prediction formula” are forcibly extracted. 
     In step S 103  following step S 102  illustrated in  FIG. 3 , the extraction unit  1102  performs the topic classification on the morpheme-analyzed text  1203  and selects the at least one topic word belonging to each topic from the multiple words  1202 . In the example illustrated in  FIG. 5 , multiple topic words  1430  “application”, “version”, “development”, and “specification” belonging to a topic to which topic No. 0 is assigned are selected. Multiple topic words  1431  “test”, “debug”, “single”, and “management” belonging to a topic to which topic No.  1  is assigned are selected. Multiple topic words  1432  “software”, “response”, “deadline”, and “confirmation” belonging to a topic to which topic No. 2 is assigned are selected. Multiple topic words  1433  “design”, “use case”, “button”, and “arrangement” belonging to a topic to which topic No. 3 is assigned are selected. Multiple topic words  1434  “release”, “action”, “notebook”, and “preparation” belonging to a topic to which topic No. 4 is assigned are selected. Multiple topic words  1435  “inquiry”, “receive”, “answer”, and “description” belonging to a topic to which topic No. 5 is assigned are selected. Multiple topic words  1436  “customer”, “hearing”, “main request”, and “sub-request” belonging to a topic to which topic No. 6 is assigned are selected. 
     In step S 103 , the extraction unit  1102  extracts the feature word characterizing each topic from the at least one topic word belonging to each topic. The feature word that has been extracted is the reference word  1204  that is the reference for selecting the at least one similar word. In the example illustrated in  FIG. 5 , a feature word  1440  “application” is extracted from the multiple topic words  1430 . A feature word  1441  “test” is extracted from the multiple topic words  1431 . A feature word  1442  “software” is extracted from the multiple topic words  1432 . A feature word  1443  “design” is extracted from the multiple topic words  1433 . A feature word  1444  “release” is extracted from the multiple topic words  1434 . A feature word  1445  “inquiry” is extracted from the multiple topic words  1435 . A feature word  1446  “customer” is extracted from the multiple topic words  1436 . 
     The extraction unit  1102  obtains a feature degree of each topic word that indicates a degree to which each topic word of the at least one topic word belonging  t o each topic characterizes each topic, and extracts the topic word having the highest feature degree as the feature word. The feature word that has been extracted is the reference word  1204 . The feature degree of each topic word is determined to increase as a probability of appearance of each topic word in the topic increases, the probability being determined in the topic classification, and to decrease as frequency of appearance of each topic word in the text  1200  to be searched or analyzed increases. Desirably, the feature degree of each topic word is obtained by dividing the probability of each topic word in the topic by the frequency of appearance of each topic word in the text, as shown in Equation (1). Dividing by the frequency of appearance of each topic word in the text suppresses extraction, as the feature word, of a word that belongs to various topics and has a weak property characterizing each topic. 
       Feature degree of each topic word=Probability of appearance of each topic word in the topic/Frequency of appearance of each topic word in the text   (1)
 
     The frequency of appearance of each topic word in the text is obtained by dividing the number of appearances of each topic word in the text by the number of words in the entire text, as shown in Equation (2). 
       Frequency of appearance of each topic word in the text=Number of appearances of each topic word in the text/Number of words in the entire text   (2)
 
     In step S 104  following step S 102 , illustrated in  FIG. 3 , the multidimensional vectorization unit  1103  multidimensionally vectorizes the multiple words  1202  to obtain the multiple vectors  1205  respectively expressing the multiple words  1202 . In the example illustrated in  FIG. 6 , vectors  1460 ,  1461 ,  1462 , and the like respectively expressing a word  1450  “air”, a word  1451  “defect”, a word  1452  “arrangement”, and the like are obtained. 
     The multidimensional vectorization unit  1103  multidimensionally vectorizes each word of the multiple words  1202  based on the probability of appearance of each word in the context. The multidimensional vectorization unit  1103  multidimensionally vectorizes a first word and a second word, such that a first vector and a second vector are directed to the same direction. The first vector and the second vector respectively express the first word and the second word that are included in the multiple words  1202 , are used in the same manner, and appear in similar contexts. For example, a word “personal computer” has such a probability model as having a high probability of appearing in a context including peripheral words “software” and “install”, and a low probability of appearing in a context including peripheral words “pot” and “boil”. The word “PC” has the same probability model as the probability model of the word “personal computer”. Thus, the vector expressing the word “PC” is directed in the same direction as the vector expressing the word “personal computer”. On the other hand, the word “personal computer” has such a probability model as having a high probability of appearing in a context including peripheral words “software” and “install”, and a low probability of appearing in a context including peripheral words “pot” and “boil”. The word “ramen” has such a probability model as having a low probability of appearing in a context including peripheral words “software” and “install”, and a high probability of appearing in a context including peripheral words “pot” and “boil”. The probability model of the word “ramen” is different from the probability model of the word “personal computer”. Therefore, the vector expressing the word “ramen” is directed in a different direction from the vector expressing the word “personal computer”. 
     In step S 105  following steps S 103  and S 104 , illustrated in  FIG. 3 , the selection unit  1104  selects the at least one similar word from the multiple words  1202 , and creates the similar word list  1206  including the at least one similar word that has been selected. The selection unit  1104  selects the at least one similar word, such that a similarity between a vector expressing the reference word  1204  and a vector expressing each similar word of the at least one similar word exceeds a set reference. In the example illustrated in  FIG. 6 , the similarity between the vector  1460  expressing the reference word  1450  “air” and the vector  1461  expressing the word  1451  “failure” exceeds the reference. The similarity between the vector  1460  expressing the reference word  1450  “air” and the vector  1462  expressing the word  1452  “arrangement” is lower than the reference. The similar word list  1206  that includes a similar word “defect” but does not include a dissimilar word “arrangement” is created. 
     Desirably, the selection unit  1104  selects the at least one similar word, such that a cos similarity between the vector expressing the reference word  1204  and the vector expressing each similar word exceeds a reference cos similarity, and is included in a set higher number of cos similarity of multiple cos similarities between the vector expressing the reference word  1204  and the multiple vectors  1205  respectively expressing the multiple words  1202 . Similarities other than the cos similarity may be used for selection. For example, an angle may be used for selection. 
     In step S 106  following step S 105 , illustrated in  FIG. 3 , using the exclusion word dictionary  1301  that registers the exclusion word that is unnecessary in the search or analysis, the creation unit  1105  deletes the exclusion word registered in the exclusion word dictionary  1301  from the similar word list  1206  to obtain a similar word list  1208  that has been updated. As a result, the creation unit  1105  creates the synonym dictionary  1207 , such that the exclusion word registered in the exclusion word dictionary  1301  is not registered in the synonym dictionary  1207 . 
     In step S 107  following step S 106 , illustrated in  FIG. 3 , the creation unit  1105  organizes the similar word list  1208  to obtain a similar word list  1209  that has been updated. 
     In organizing the similar word list  1208 , when the at least one similar word included in the similar word list  1208  has two or more overlapping similar words, the creation unit  1105  specifies a similar word to be deleted of the two or more overlapping similar words, and deletes this specified similar word to be deleted from a similarity list  1208 . Thus, the creation unit  1105  creates the synonym dictionary  1207 , such that the similar word specified to be deleted is not registered in the synonym dictionary  1207 . The creation unit  1105  specifies the similar word to be left, such that the cos similarity between the vector expressing the reference word  1204  and the vector expressing the similar word to be left is the highest cos similarity of the two or more cos similarities between the vector expressing the reference word  1204  and the two or more vectors expressing the two or more overlapping similar words. Then, the similar word other than the similar word that has been specified to be left is the similar word to be deleted. 
     In organizing the similar word list  1208 , when the at least one similar word included in the similar word list  1208  includes a similar word that overlaps the reference word  1204 , the creation unit  1105  specifies a new reference word for the at least one similar word and replaces the reference word  1204  with the new reference word. The creation unit  1105  specifies the new reference word, such that the cos similarity between the vector expressing the reference word  1204  and a vector expressing the new reference word is the highest cos similarity of at least one cos similarity between the vector expressing the reference word  1204  and at least one vector respectively expressing the at least one similar word. 
     In step S 108  following step S 107 , illustrated in  FIG. 3 , using the external dictionary  1302 , the creation unit  1105  performs additional learning by the external dictionary  1302  to obtain a similar word list  1210  that has been updated. The creation unit  1105  adds, to the similar word list  1209 , a similar word that is determined as being a synonym of the reference word  1204  in the external dictionary  1302  and is not included in the similar word list  1209 , and deletes, from the similar word list  1209 , a similar word that is determined as not being a synonym of the reference word  1204  in the external dictionary  1302  and is included in the similar word list  1209 . As a result, the creation unit  1105  creates the synonym dictionary  1207 , such that a similar word that is determined as being a synonym of the reference word  1204  in the external dictionary  1302  and is not included in the at least one similar word is registered in the synonym dictionary  1207 , and a similar word that is determined as not being a synonym of the reference word  1204  in the external dictionary  1302  and is included in the at least one similar word is not registered in the synonym dictionary  1207 . Words that are not registered in the external dictionary  1302 , such as technical terms, are left in the similar word list  1210  as they are. 
     Step S 109  following step S 108 , illustrated in  FIG. 3 , determines whether the existing synonym dictionary  1303  created from the text  1200  to be searched or analyzed or a text different from the text  1200  exists. When the synonym dictionary  1303  exists, the similar word list  1210  and the existing synonym dictionary  1303  are merged in step S 110 , and then the synonym dictionary  1207  is saved in step S 111 . When the existing synonym dictionary  1303  does not exist, the synonym dictionary  1207  is saved in step S 111 . 
     In step S 110 , the creation unit  1105  merges the similar word list  1209  and the existing synonym dictionary  1303 . Thus, the creation unit  1105  creates the synonym dictionary  1207 , such that the similar word registered in the existing synonym dictionary  1303  is registered in the synonym dictionary  1207 . 
     In step S 111 , the creation unit  1105  saves the synonym dictionary  1207 . 
     In additional processing for a similarity list  1406  performed in steps S 106  to S 110 , the synonym dictionary  1207  in which similar word groups covering similar words similar to the reference word  1204  are registered is created. At the same time, noise is removed from the synonym dictionary  1207 . 
       4  Example of Screen 
       FIG. 7  is a schematic diagram illustrating an example of a screen displayed in the synonym dictionary creation apparatus according to the first embodiment. 
     A screen  1500  illustrated in  FIG. 7  is displayed on the display  1043 . 
     The screen  1500  includes a drop-down list  1520  that specifies a folder in which the text  1200  to be analyzed is stored, a drop-down list  1521  that specifies a file name of the existing synonym dictionary  1303 , a drop-down list  1522  that specifies a file name of the dictionary  1300  of forcibly extracted words, a drop-down list  1523  that specifies a file name of the exclusion word dictionary  1301 , a drop-down list  1524  that specifies a file name of the external dictionary  1302 , a text box  1525  that specifies a file name of the synonym dictionary  1207 , a text box  1526  that specifies a file name of a log file, a button  1527  that receives a call instruction on an analysis option setting screen, a button  1528  that receives a request for creating the synonym dictionary  1207 , and a button  1529  that receives cancellation of creation of the synonym dictionary  1207 . All or a part of the drop-down lists  1520  to  1524 , the text boxes  1525  and  1526 , and the buttons  1527  to  1529  may be replaced with another type of graphical user interface (GUI) part, or may be omitted. 
     Although the present invention has been described in detail, the above description is illustrative in all aspects, and the present invention is not limited thereto. 
     It is understood that countless variations that are not illustrated can be envisaged without departing from the scope of the present invention. 
     EXPLANATION OF REFERENCE SIGNS 
       1000 : synonym dictionary creation apparatus 
       1020 : synonym dictionary creation program 
       1100 : removal unit 
       1101 : morpheme analysis unit 
       1102 : extraction unit 
       1103 : multidimensional vectorization unit 
       1104 : selection unit 
       1105 : creation unit 
       1106 : storage unit 
       1200 : pre-removal text (text to be searched or analyzed) 
       1201 : post-removal text 
       1202 : multiple words 
       1203 : morpheme-analyzed text 
       1204 : reference word 
       1205 : multiple vectors 
       1206 ,  1208 ,  1209 ,  1210 : similar word list 
       1207 : synonym dictionary 
       1300 : dictionary of forcibly extracted words 
       1301 : exclusion word dictionary 
       1302 : external dictionary 
       1303 : existing synonym dictionary 
       1400 ,  1401 ,  1402 ,  1403 ,  1404 ,  1405 : text element 
       1411 ,  1412 ,  1414 ,  1415 : multiple words 
       1420 ,  1421 : technical term 
       1430 ,  1431 ,  1432 ,  1433 ,  1444 ,  1435 ,  1436 : multiple topic words 
       1440 ,  1441 ,  1442 ,  1443 ,  1444 ,  1445 ,  1446 : feature word 
       1450 ,  1451 ,  1452 : word 
       1460 ,  1461 ,  1462 : vector