Patent Publication Number: US-9906238-B2

Title: Encoding device, encoding method and search method

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2016-145779, filed on Jul. 25, 2016, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiments discussed herein are related to an encoding device, an encoding method and a search method. 
     BACKGROUND 
       FIG. 1  illustrates relationships between various types of text analyses that are performed on documents. Text analyses include for example a morphological analysis (part-of-speech analysis), a syntax analysis (modification analysis), and a semantic analysis. A morphological analysis is a process in which a sentence is segmented into morphemes so as to give part-of-speech information to each of the morphemes. Morphemes obtained in a morphological analysis may be treated as words in some cases. In a morphological analysis, a lexical analysis may be performed. A lexical analysis is a process in which a sentence is segmented into words in a document on the basis of notation. 
     A syntax analysis is a process in which phrases containing independent words are synthesized on the basis of part-of-speech information of words so as to obtain a modification relationship (qualification relationship) between phrases on the basis of independent words included in the phrases. Also, a semantic analysis is a process in which meaning relationships between words contained in sentences are analyzed on the basis of for example a modification relationship. A semantic analysis result can be used for a process of obtaining meaning of a synonymous expression and a polysemous expression or a process of extracting a word having a similar meaning from among a plurality of words. While a semantic analysis that does not aim at very high accuracy can be performed on the basis of words alone or on the basis of words and pieces of part-of-speech information, using modification relationships increases the accuracy of a semantic analysis. In a semantic analysis, part of the processes of a syntax analysis may be performed. 
     A semantic analysis uses a result of a morphological analysis of a natural sentence so as to obtain the semantic structure of that natural sentence. Using a semantic structure makes it possible to express, as data treated by computers, what a natural sentence means. 
     A semantic structure includes for example a plurality of nodes respectively representing the concepts of a plurality of words included in a morphological analysis result and directed arcs connected to the nodes. When an arc is connected to only one node, that arc represents the attribute of the node to which it is connected. Also, when an arc is connected to two nodes, that arc represents the relationship between the two nodes to which it is connected. In some cases, one node is connected to a plurality of arcs. A semantic structure is expressed by a graph structure (directed graph) that is created from for example nodes and arcs.  FIG. 2  exemplifies a graph structure corresponding to a sentence of “WATASHI WA GAKKOU DE HATARAITE IMASU” (“I work for a school”). 
     A semantic analysis defines structures on the basis of for example rules so as to perform an analysis while combining a plurality of structures as needed. An example of rules used by semantic analyses is case grammar, which is proposed by Fillmore. According to case grammar, a sentence for example is considered to consist of one verb and a plurality of case categories. For example, by repeatedly applying a rule as described, a graph structure, as illustrated in  FIG. 2 , that corresponds to one sentence can be generated eventually. 
     Also,  FIG. 3  illustrates an example of a utilization process in which a text analysis result is utilized. A document  311  is compressed by using a compression dictionary  301 , and is stored as a compressed document  312 . Then, the compressed document  312  is decompressed for utilization so that the document  311  is restored, and a morphological analysis and a semantic analysis are performed on the document  311  by using an analysis dictionary  302  so as to generate a semantic analysis result  313 . The semantic analysis result  313  is utilized by an application program etc. 
     Regarding this, a technique is known in which for example a document is rewritten so that the semantic contents will not be changed and document compression is performed by converting the document into bit strings while referring to a compression table after the rewriting (see Patent Document 1 for example). Also, a technique of obtaining a method of accessing and searching for information via a data communication system is known (see Patent Document 2 for example). A technique is also known that makes it possible to analyze document contents without preparing a dictionary for a natural language (see Patent Document 3 for example). 
     Patent Document 1: Japanese Laid-open Patent Publication No. 7-160684 
     Patent Document 2: Japanese Laid-open Patent Publication No. 2008-135023 
     Patent Document 3: Japanese Laid-open Patent Publication No. 7-129588 
     SUMMARY 
     According to an aspect of an embodiment, a recording medium is a non-transitory computer-readable recording medium having stored therein an encoding program for causing a computer to execute a process, the process including generating a plurality of word codes by assigning a compression code to each of a plurality of words contained in a sentence in a compression target document, generating a plurality of pieces of semantic structure information respectively corresponding to the plurality of words by performing a semantic analysis of the sentence, and generating a semantic structure code by assigning a compression code to each of the plurality of pieces of semantic structure information, and arranging the plurality of word codes and the plurality of semantic structure codes in a prescribed order so as to output the word codes and the semantic structure codes. 
     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  exemplifies relationships between various types of text analyses; 
         FIG. 2  exemplifies a graph structure; 
         FIG. 3  illustrates an example of a utilization process in which a text analysis result is utilized; 
         FIG. 4  illustrates an example of a compression dictionary used in LZ77 encoding; 
         FIG. 5  illustrates an example of a compression dictionary used in LZ78 encoding; 
         FIG. 6  illustrates a functional configuration example of an encoding device according to an embodiment; 
         FIG. 7  is a flowchart explaining an example of an encoding process; 
         FIG. 8  exemplifies an encoding device according to a first embodiment; 
         FIG. 9  is a flowchart of an encoding process according to the first embodiment; 
         FIG. 10  illustrates an example of a word dictionary; 
         FIG. 11  exemplifies a tree structure that represents a semantic analysis result; 
         FIG. 12  illustrates examples of pieces of concept information of words and examples of arcs; 
         FIG. 13  exemplifies conversion of a semantic structure into a binary tree; 
         FIG. 14  exemplifies a basic structure of a binary tree; 
         FIG. 15  exemplifies a semantic structure binary tree in which four subtrees are connected; 
         FIG. 16  illustrates an example of a code table; 
         FIG. 17  exemplifies assignment of compression codes to semantic structure information and nested-structure information; 
         FIG. 18  exemplifies assignment of compression codes to semantic structure information and nested-structure information of a semantic structure binary tree; 
         FIG. 19  illustrates an example of a compression code string arranged in a first order; 
         FIG. 20  illustrates an example of a compression code string arranged in a second order; 
         FIG. 21  illustrates an encoding device according to a second embodiment; 
         FIG. 22  is a flowchart of an encoding process according to the second embodiment; 
         FIG. 23  illustrates an example of an intermediate code table; 
         FIG. 24  illustrates examples of a plurality of compression target documents; 
         FIG. 25  illustrates an example of aggregation information for a plurality of compression target documents; 
         FIG. 26  illustrates an example of a code table of compression codes; 
         FIG. 27  illustrates a functional configuration example of an information processing apparatus that performs a utilization process; 
         FIG. 28  exemplifies a flowchart in a case when a compression code string is utilized for extracting a synonym; 
         FIG. 29  illustrates an example of a synonym search; 
         FIG. 30  exemplifies a flowchart in a case when a compression code string is utilized for knowledge extraction; 
         FIG. 31  exemplifies a flowchart in a case when a compression code string is utilized for sentence revision; 
         FIG. 32  exemplifies a flowchart for a variation embodiment in a case when a compression code string is utilized for synonym extraction; and 
         FIG. 33  exemplifies a hardware configuration of an information processing apparatus that performs an encoding process or a utilization process according to embodiments; 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, some embodiments of the present invention will be explained in detail by referring to the drawings. Note that elements corresponding to a plurality of drawings will be denoted by similar numerals. 
     In the utilization process illustrated in  FIG. 3 , a text analysis such as a morphological analysis, a semantic analysis, etc. is performed after decompressing a compressed document. Decompression of a compressed document to be utilized leads to large process loads. Also, a semantic analysis has individual applications executed independently for their purposes, which further increases process loads. An increase in process loads as described above more greatly affects an information processing apparatus that has a small amount of computation resources such as, in particular, a mobile terminal. 
     In view of this, it may be possible to employ an arrangement in which when a document is to be compressed in order to reduce loads of a utilization process of a semantic analysis result, a morphological analysis and a semantic analysis are performed in advance so as to obtain analysis results so as to store the analysis results in a compressed state. In such a case, a semantic analysis does not have to be performed for the utilization. However, a process of decompressing a compressed semantic analysis result is to be added. That is, the utilization of a semantic analysis result is possible after decompressing a compressed document and a compressed semantic analysis result and associating the decompressed document and the decompressed semantic analysis result. This means that the loads of a decompression process and an association process will not be reduced. 
     A compressed document and a compressed semantic analysis result are decompressed because a compression dictionary and analysis dictionary have no commonalities. While a compression dictionary stores character strings, such as a maximum matching character string, for encoding that is not word sensitive, an analysis dictionary stores information containing words, parts of speech of words, subclassification of parts of speech, etc. 
       FIG. 4  illustrates an example of a compression dictionary used in LZ77 encoding, and  FIG. 5  illustrates an example of a compression dictionary used in LZ78 encoding. As illustrated in  FIG. 4  and  FIG. 5 , character strings in a compression dictionary are often segmented in the middle of a word and information of words is not held, making it difficult to associate character strings in a compression dictionary with a semantic analysis result. 
     In view of this situation, an arrangement may be possible in which words of natural languages are used for character strings of compression dictionaries so that a compress process and a morphological analysis share dictionaries. Performing a morphological analysis and compression of words on the basis of one dictionary makes it possible to associate each word with the semantic analysis result of that word in their compressed state. 
       FIG. 6  illustrates a functional configuration example of an encoding device according to an embodiment. An encoding device  600  illustrated in  FIG. 6  includes a storage unit  611 , a code generation unit  612  and an output unit  614 . 
     The storage unit  611  may store for example a compression target document. The code generation unit  612  performs a compression process on a compression target document and performs a semantic analysis of the compression target document so as to perform a compression process on a semantic analysis result. The output unit  614  arranges the compression results and outputs them. 
       FIG. 7  is a flowchart explaining an example of an encoding process performed by the encoding device  600  illustrated in  FIG. 6 . In S 701 , the code generation unit  612  assigns compression codes to a plurality of words contained in a sentence in a compression target document, and performs a semantic analysis of the sentence so as to generate semantic structure information for each of the plurality of words. Semantic structure information may contain information representing for example a node in a graph structure and an arc from a higher-order node treating that node as the end point. Then, the code generation unit  612  assigns a compression code to each of the pieces of semantic structure information corresponding to the words. 
     In S 702 , the output unit  614  arranges in prescribed order and outputs the compression codes that were respectively assigned to the plurality of words and the pieces of semantic structure information. The encoding device  600  as described above can reduce process loads accompanied by utilizing a semantic analysis result of a document. Note that a semantic analysis may be performed by using a plurality of words contained in a sentence in a compression target document or may be performed by using a compression code assigned to each of a plurality of words contained in a sentence. 
     First Embodiment 
       FIG. 8  exemplifies an encoding device  800  according to the first embodiment. The encoding device  800  includes the storage unit  611 , the code generation unit  612 , the output unit  614  and a morphological analysis unit  801 . When for example an encoding process starts, the storage unit  611  has stored a compression target document  811 , a word dictionary  813 , and a code table  814 . 
       FIG. 9  is a flowchart of an encoding process according to the first embodiment. The encoding process illustrated in  FIG. 9  may be performed by for example the encoding device  800  illustrated in  FIG. 8 . In S 901 , the morphological analysis unit  801  uses the word dictionary  813  so as to perform a morphological analysis of the compression target document  811  so that a morpheme contained in each sentence in the compression target document  811  is extracted. Note that a morpheme obtained through a morphological analysis may be treated as a word. 
       FIG. 10  illustrates an example of the word dictionary  813 . Each entry in the word dictionary  813  in  FIG. 10  includes a word ID for identifying a word, the word and additional information. Additional information is information representing the attribute of the word, and may include for example information such as the part of speech, subclassification of the part of speech, utilization, etc. A plurality of pieces of additional information may be registered for one entry of the word dictionary  813 . Note that subclassification of a part of speech is for example information resulting from further categorizing the part of speech. When for example a part of speech is nouns, the subclassification may include common nouns, proper nouns, numerals, etc. Further, additional information may include subclassification of a plurality of parts of speech for one word. For example, a proper noun may further be classified into more detailed levels such as a person&#39;s name, an organization&#39;s name, or a place name. The morphological analysis unit  801  compares the character strings of each sentence with the character string of each word registered in the word dictionary  813 , and thereby can extract a corresponding word. 
     In S 902 , the code generation unit  612  performs a semantic analysis of each sentence by using a morphological analysis result so as to generate a semantic analysis result  812  to store it in the storage unit  611 . Note that the semantic analysis result  812  may be in for example the graph structure illustrated in  FIG. 2 . 
       FIG. 11  exemplifies a tree structure obtained by converting the graph structure (as illustrated in  FIG. 2  for example) that represents the semantic analysis result  812 . A graph structure obtained from a semantic analysis result has a center node and does not have loops so that the structure can be converted into a tree structure as illustrated in  FIG. 11 . Note that a center node may be for example a predicate of a sentence. Also, in  FIG. 11 , an arc that is connected to just one node in the graph structure is assigned a void node (NIL) at an end of that arc on the side that is not connected to a node. Thereby, for example concept information, which represents the concept of a word, and an arc from a higher-order node treating that node as a terminal point, are associated with a node. Alternatively, when a node is a void node (NIL), an arc representing the attribute of a higher-order node for the void node is associated. Concept information may be for example information contained in additional information of the word dictionary  813  and may contain the subclassification of the part of speech of a word.  FIG. 12  illustrates examples of pieces of concept information of words and examples of arcs. For example, in concept information of words, ADJ represents an adjective, ADV represents an adverb, and ADVP represents an adverbial phrase. Also, in arcs, “ST” for example is an arc that represents the origin (center node) of a graph structure. A word denoted by “ST” may be for example a predicate in a sentence. “AGENT” is for example an arc representing an agent. 
     Also, when the number of branches that a node has is not two in a tree structure obtained by converting a graph structure, a binary tree can be obtained by adding a dummy node. When for example one node has three or four branches, the structure can be converted into a binary tree by adding dummy nodes of one layer, and when one node has five through eight branches, the structure can be converted into a binary tree by adding dummy nodes of two layers. 
       FIG. 13  exemplifies conversion of the semantic structure tree illustrated in  FIG. 11  into a binary tree. Nodes  1301  and  1302 , which have three or more branches in  FIG. 11 , have in  FIG. 13  dummy nodes added to them so that the structure has been converted into a binary tree with each of the nodes having a reduced number of branches. As described above, the graph structure representing the semantic structure exemplified in  FIG. 2  can be converted into a binary tree as illustrated in  FIG. 13 . Note that a binary tree obtained by converting a graph structure representing a semantic structure may also be referred to as a semantic structure binary tree hereinbelow. 
     Also, converting a graph structure representing a semantic structure into a binary tree makes it possible to represent a semantic structure binary tree by using a basic structure of a binary tree.  FIG. 14  exemplifies the basic structure of a binary tree. The binary tree illustrated in  FIG. 14  is a four-layer binary tree including 15 nodes of node  0  through node  14 , the numbers of the nodes representing the positions of the nodes in the tree structure of the binary tree. By connecting, in a nested structure, a plurality of subtrees based on this basic structure of the binary tree, a binary tree having a deeper hierarchical structure can be generated. When a graph structure representing a semantic structure is converted into a binary tree, only a part of the resultant binary tree tends to be deep, and connecting another subtree to a leaf node of a subtree of a basic structure can reduce the ratio of unnecessary portions. 
       FIG. 15  illustrates an example of a semantic structure binary tree in which four subtrees are connected. A subtree  1202  and a subtree  1203  are child subtrees having a subtree  1201  as a parent, and a subtree  1204  is a child subtree having the subtree  1202  as a parent. 
     Root node  0  of the subtree  1202  is also functioning as leaf node  7  of the parent subtree  1201  and root node  0  of the subtree  1203  is also functioning as a leaf node  13  of the parent subtree  1201 . Also, root node  0  of the subtree  1204  is also functioning as leaf node  11  of the parent subtree  1202 . 
     Using these four subtrees makes it possible to describe for example a nine-layer binary tree including 19 nodes as below. 
     Subtree  1201 : nodes  0  through  3 , nodes  5  through  7  and node  13   
     Subtree  1202 : nodes  1  through  5  and node  11   
     Subtree  1203 : node  1  and node  2   
     Subtree  1204 : node  1 , node  3  and node  4   
     As described above, using a plurality of subtrees to express the tree structure of a semantic structure binary tree makes it possible to efficiently store, in the storage unit  611 , a semantic structure binary tree in which only a portion has a deep hierarchical structure. In such a case, the semantic analysis result  812  includes nested-structure information, which represents a connection relationship between a parent subtree and a child subtree, together with semantic structure information corresponding to each branch of the semantic structure binary tree. 
     In S 903 , the code generation unit  612  refers to the word dictionary  813  and the code table  814  so as to assign a compression code to each word included in each sentence in the compression target document  811 . Also, the code generation unit  612  assigns compression codes to semantic structure information and nested-structure information included in the semantic analysis result  812  in accordance with for example a prescribed rule. Then, the code generation unit  612  stores, in the storage unit  611 , compression codes assigned to a word, semantic structure information and nested-structure information as a word code  815 , a semantic structure code  816  and a nested-structure code  817 , respectively. 
     The code table  814  registers association relationships between words and compression codes. A fixed-length code of 1 through 5 bytes may be used for a compression code. Examples of such compression codes are described below by using hexadecimal numbers. 
     Alphanumeric characters: 00h through 7Fh (1 byte) 
     CJK compatibility ideograph characters: A00000h through AFFFFFh (3 bytes) 
     English words: B00000h through B7FFFFh (3 bytes) 
     English collocations: B8000000h through BFFFFFFFh (4 bytes) 
     Japanese words: C00000h through C7FFFFh (3 bytes) 
     Japanese collocations: C8000000h through CFFFFFFFh (4 bytes) 
     Words of a third language: D00000h through D7FFFFh (3 bytes) 
     Collocations of a third language: D8000000h through DFFFFFFFh (4 bytes) 
     4-digit value: E00000h through E3FFFFh (3 bytes) 
     6-digit value: E4000000h through E4FFFFFFh (4 bytes) 
     9-digit value: E500000000h through E8FFFFFFFFh (5 bytes) 
     Semantic structure information and nested-structure information: F000000000h or greater (5 bytes) 
     Compression codes assigned to 4-digit or 6-digit values may have a code for distinguishing options for numerical expressions such as whether or not “,” is added for every three digits of a decimal value, whether the value is a positive number or a negative number, and other factors. 
     A 3-byte through 5-byte compression code assigned to a word, semantic structure information, and nested-structure information has its 4 highest-order bits used for identifying the code type. For example, “C” represents a Japanese word and “F” represents semantic structure information and nested-structure information. The other bits are used for identifying an individual word, semantic structure information or nested-structure information. 
     The above compression code is just an example and a different method may be used for assigning a compression code to a word. A compression code may be a fixed-length code of a different size or may be a variable-length code. 
       FIG. 16  illustrates an example of the code table  814 . Each entry in the code table  814  illustrated in  FIG. 16  includes for example an ID for identifying a word and a compression code. A word ID illustrated in  FIG. 10  is used for an ID of a word. For example, the compression code of the word “SAKURA (cherry)” corresponding to word ID “1” is “C01234h”. 
     The code generation unit  612  replaces a word with a compression code that corresponds to the word on the code table  814 , and thereby can generate the word code  815 . It is also possible to manage the information in the word dictionary  813  and the information in the code table  814  collectively. 
     Also, the code generation unit  612  assigns compression codes to semantic structure information and nested-structure information in accordance with for example a prescribed rule, and thereby can generate the semantic structure code  816  and the nested-structure code  817 . Semantic structure information and nested-structure information may be encoded in such a manner that they will include for example the information below. 
     In an embodiment, a 5-byte compression code assigned to semantic structure information has its 4 highest-order bits used for identifying the code type. The other bits, i.e., the lower 36 bits, are used as described below. 
     4 bits: numbers of nodes in a binary tree of a basic structure 
     8 bits: ID of a binary tree including nodes 
     12 bits: concept information of a word represented by a node 
     12 bits: arc (connection information) representing a connection relationship with a higher-order node. 
     Also, a 5-byte compression code assigned to nested-structure information has its 4 highest-order bits used for identifying the code type. The other bits, i.e., the lower 36 bits, are used as below. 
     4 bits: numbers of nodes in a binary tree of a basic structure 
     8 bits: ID of a binary tree including nodes 
     12 bits: ID of child binary tree 
     12 bits: code representing connection between trees 
       FIG. 17  exemplifies compression codes assigned to the 12 highest-order or lowest-order bits among the 24 lowest-order bits of a compression code for semantic structure information and nested-structure information. In the example illustrated in  FIG. 17 , 0xAAA” and “0x085” are respectively assigned to “WORK=HATARAKU” and “I”, which are concept information of words. Also, 0x001” and “0x0BC” are respectively assigned to “ST” and “AGENT” representing arcs. Note that “ST” is for example an arc representing the origin of a graph structure. “AGENT” is an arc representing for example an agent. A code equal to or greater than “0xF01” is assigned to the ID of a child binary tree of nested-structure information. 
       FIG. 18  exemplifies compression codes assigned to semantic structure information and nested-structure information of the semantic structure binary tree illustrated in  FIG. 13 . The semantic structure binary tree illustrated in 
       FIG. 18  has root nodes  0  of child binary trees connected to the respective positions of leaf nodes  8  and  10  of parent binary trees. “0x00” is assigned to the ID of a parent binary tree at 8 bits, which represents a binary tree including nodes of semantic structure information. “0x01” is assigned, at 8 bits, which represents the ID of a binary tree including nodes in semantic structure information and nested-structure information, to the ID of the child binary tree that is connected to leaf node  8 . Also, “0xF01” is assigned at 12 bits, which represents a child node of nested-structure information. Similarly, “0x02” is assigned, at 8 bits, which represents the ID of a binary tree including nodes in semantic structure information and nested-structure information, to the ID of the child binary tree that is connected to leaf node  10 . At 12 bits, which represents the ID of a child binary tree of nested-structure information, “0xF02” is assigned to the ID of a child binary tree that is connected to leaf node  10 . 
     Also, for example, a semantic structure code of “0xF000AAA001” is assigned to the semantic structure information of root node  0  of a parent binary tree. The semantic structure code of “0xF000AAA001” has the first “F” (4 bits) representing semantic structure information, has the next “0” (4 bits) representing the number of node  0  in the binary tree and has the next “00” (8 bits) representing the ID of the binary tree including the node. Further, the next “AAA” (12 bits) represents the concept information of the word “WORK=HATARAKU”. Also, the “001” (12 bits) at the tail represents arc:ST. 
     A semantic structure code of “0xF100000000” is assigned to the semantic structure information of a dummy node of node  1 . The semantic structure code of “0xF100000000” has its first “F” (4 bits) representing that it is semantic structure information, has the next “1” (4 bits) representing the number of node  1 , and has the next “00” (8 bits) representing the ID of the binary tree including the node. The next “000” (12 bits) represents that it is a NIL node not including the concept information of a word, and the “000” (12 bits) at the tail represents that it is a dummy node not including an arc. 
     In leaf node  8  of a parent binary tree, semantic structure information and nested-structure information exist. Among them, a nested-structure code of “0xF800F01002” is assigned to the nested-structure information, and a semantic structure code of “0xF001001013” is assigned to the semantic structure information. 
     The nested-structure code of “0xF800F01002” of leaf node  8  of the parent binary tree has its first “F” representing that it is nested-structure information, has the next “8” representing the number of node  8 , has the next “00” representing the ID of the binary tree including the node, and has the next “F01” representing the ID of the child binary tree. Also, “002” as a code representing nested-structure information, which represents connections between trees, is assigned to 12 bits at the tail. 
     Also, the semantic structure code of “0xF001001013” of leaf node  8  of the parent binary tree has the first “F” representing semantic structure information, has the next “0” representing the number of node  0  in the binary tree including the node, and has the next “01” representing the ID of the binary tree including the node. The next “001” is a code representing a node that corresponds to a word, that however does not include the concept information of the word, and that is associated with the information of the notation. Also, the “013” at the tail represents arc: “SCOPE”. 
     Similarly, in leaf node  10  of a parent binary tree, semantic structure information and nested-structure information exist. Among them, a nested-structure code of “0xFA00F02002” is assigned to the nested-structure information, and a semantic structure code of “0xF0020850BC” is assigned to the semantic structure information. 
     The nested-structure code of “0xFA00F02002” of leaf node  10  of the parent binary tree has the first “F” representing nested-structure information and has the next “A” representing the number of node  10 . Also, the next “00” represents the ID of the binary tree including node, and the next “F02” represents the ID of a child binary tree. Also, “002” as a code representing nested-structure information, which represents connections between trees, is assigned to 12 bits at the tail. 
     Also, the semantic structure code of “0xF0020850BC” of leaf node  10  of the parent binary tree has the first “F” representing semantic structure information, has the next “0” representing the number of node  0  in the binary tree including the node, and has the next “02” representing the ID of the binary tree including the node. The next “085” is a code assigned to concept information of a word: “I”. Also, the “0BC” at the tail is a code assigned to arc: AGENT. 
     As described above, the semantic structure code  816  and the nested-structure code  817  may be assigned to a node that connects two subtrees, and the semantic structure code  816  may be assigned to the other nodes. 
     In S 904 , the output unit  614  arranges the word code  815 , the semantic structure code  816  and the nested-structure code  817  in a prescribed order for each sentence so as to generate a compression code string, and outputs the generated compression code string to for example an information processing apparatus that performs a utilization process. An example of a prescribed order may be the one below. 
     (1) First Order 
     The word code  815  assigned to each word and the semantic structure code  816  assigned to the semantic structure information corresponding to that word are located for each sentence in such a manner that they are adjacent to each other. Note that the semantic structure code  816  for a NIL node or a dummy node that is not associated with a word may be arranged following for example a group resulting from arranging the word code  815  and the semantic structure code  816  assigned to the semantic structure information corresponding to that word.  FIG. 19  illustrates an example of a compression code string arranged in the first order. Arranging a compression code in the first order makes it easy to associate each word with its semantic analysis result in a utilization process for utilizing a semantic analysis result. 
     (2) Second Order 
     The plurality of word codes  815  assigned to a plurality of words are located for each sentence in such a manner that they are adjacent to each other.  FIG. 20  illustrates an example of a compression code string arranged in the second order. This example collectively locates the word codes  815  first, and next locates the semantic structure codes  816  collectively, for each sentence. Arranging compression codes in the second order makes it possible to efficiently refer to word codes in a utilization process that only uses words. Note that the example illustrated in  FIG. 20  arranges the semantic structure codes  816  corresponding to words in order of appearance of the words, and arranges semantic structure codes  816  corresponding to a NIL node or a dummy node that is not associated with a word following the above arranged semantic structure codes  816 . 
     The encoding process according to the first embodiment described above performs a morphological analysis and a semantic analysis upon compression. The utilization does not require a morphological analysis or a semantic analysis or does not require the decompression of a compressed document, which results in reduced calculation costs compared with a case where a morphological analysis and a semantic analysis are performed after the decompression of a compressed document. Also, large effects are expected when for example an information processing apparatus having a small amount of computation resources such as a mobile terminal is to be used for utilizing compression data containing a semantic analysis result that is obtained by performing a morphological analysis, a semantic analysis, and data compression in a cloud environment having a large amount of computation resources. 
     Also, in recent years, the computation speeds of processors have drastically increased compared with the speeds of reading and writing of data from/to storage devices such as hard disks etc. This has increased cases where compression is performed in order to suppress the data amounts upon for example reading and writing of data from/to storage devices. Also, when for example a compression process and a semantic analysis process are performed separately, writing of data to the storage device occurs separately in each of the processes. By contrast, the above embodiment performs a series of processes including a morphological analysis, a semantic analysis, etc. when data is read from a storage device to be compressed. This only needs a data writing process to be performed once, improving a process speed in terms of a whole process including compression and a semantic analysis. 
     Note that the operation flow illustrated in  FIG. 9  describes an example in which the code generation unit  612  performs a semantic analysis by using a word contained in a morphological analysis result and thereafter assigns a code to the word and semantic structure information and nested-structure information. This makes it possible for a semantic analysis of an encoding process according to an embodiment to use for example an application for performing an existing semantic analysis. However, embodiments are not limited to this example. For example, a different embodiment may employ a configuration in which the code generation unit  612  performs a morphological analysis in S 901  and assigns a compression code to a word contained in the morphological analysis result so as to generate the word code  815 . Thereafter, the code generation unit  612  may perform a semantic analysis by using the word code  815  in S 902 . In such a case, in  903 , the code generation unit  612  may assign a code to semantic structure information and nested-structure information contained in the semantic analysis result  812 . 
     Second Embodiment 
       FIG. 21  illustrates an encoding device  2100  according to the second embodiment. The encoding device  2100  illustrated in  FIG. 21  includes, similarly to the encoding device  600  illustrated in  FIG. 6 , the morphological analysis unit  801 , the storage unit  611 , the code generation unit  612  and the output unit  614 . The code generation unit  612  includes a first conversion unit  2101 , an aggregation unit  2102 , a generation unit  2103 , and a second conversion unit  2104 . When for example an encoding process starts, the storage unit  611  has stored the compression target document  811 , the word dictionary  813 , and an intermediate code table  2112  that registers intermediate codes. 
       FIG. 22  is a flowchart of an encoding process according to the second embodiment. The encoding process illustrated in  FIG. 22  is performed by the encoding device  2100  illustrated in  FIG. 21 . In S 2201  and S 2202  in  FIG. 22 , the morphological analysis unit  801  and the code generation unit  612  in the encoding device  2100  may perform for example processes similar to those in S 901  and S 902  in  FIG. 9 . 
     Next, in S 2203 , the first conversion unit  2101  of the code generation unit  612  refers to the word dictionary  813  and the intermediate code table  2112  so as to assign an intermediate code to each word contained in each sentence in the compression target document  811 . 
       FIG. 23  illustrates an example of the intermediate code table  2112 . Each entry in the intermediate code table  2112  illustrated in  FIG. 23  includes an ID for identifying a word and an intermediate code. A code similar to a compression code illustrated in  FIG. 16  for example may be used as an intermediate code. 
     Further, the first conversion unit  2101  in S 2203  assigns compression codes to semantic structure information and nested-structure information contained in the semantic analysis result  812  in S 2202  in accordance with for example a prescribed rule, and thereby generates an intermediate code. Then, the first conversion unit  2101  associates the generated intermediate code with the ID so as to register them in the intermediate code table  2112 , and also stores them in the storage unit  611 . An ID that is not used as a word ID for example is used as an ID assigned to an intermediate code for semantic structure information and nested-structure information. The process in S 2203  adds information that associates an intermediate code for semantic structure information and nested-structure information with the ID to the intermediate code table  2112  in addition to for example information that associates the ID of a word in the word dictionary  813  with an intermediate code. 
     In S 2204 , the aggregation unit  2102  counts the number of times that each intermediate code appears for an intermediate code assigned to each word contained in each sentence in the compression target document  811  and for an intermediate code assigned to semantic structure information and nested-structure information contained in the semantic analysis result  812 . Then, the aggregation unit  2102  stores the counting result of intermediate codes in the storage unit  611  as aggregation information  2114 . When a plurality of the compression target documents  811  are to be encoded, the number of times that an intermediate code appears may be counted for each document. Note that a configuration may be employed in which the aggregation unit  2102 , upon counting, arranges an intermediate code assigned to each word and an intermediate code assigned to semantic structure information and nested-structure information contained in the semantic analysis result  812  in a prescribed order so as to generate an intermediate code string. Also, the aggregation unit  2102  may count the number of intermediate codes assigned to words contained in the intermediate code string and intermediate codes assigned to semantic structure information and nested-structure information. 
       FIG. 24  illustrates examples of a plurality of compression target documents  811 , and  FIG. 25  illustrates an example of the aggregation information  2114  for the plurality of compression target documents  811  of  FIG. 24 . Each entry in the aggregation information  2114  illustrated in  FIG. 25  includes the document ID of the compression target document  811  and the number of times that each intermediate code assigned to the compression target document  811  appears. While  FIG. 25  expresses intermediate codes by words, in actuality, for example IDs in the intermediate code table  2112  may be used for identifying intermediate codes of words, semantic structure information and nested-structure information. 
     For example, the compression target document  811  corresponding to document ID “1” contains one each of the words “SAKURA (cherry)”, “GAKKOU (school)” and “NO (Japanese word like “of”)”, and does not contain the word “KAEDE (maple)”. Also, the compression target document  811  corresponding to document ID “2” contains one each of the words “KAEDE”, “GAKKOU” and “NO”, and does not contain the word “SAKURA”. 
     In S 2205 , the generation unit  2103  generates, on the basis of the aggregation information  2114 , a code table  2113  in which a shorter compression code is assigned to information with a higher appearance frequency and a longer compression code is assigned to information with a lower appearance frequency. Then, the generation unit  2103  can obtain the number of times of appearance for each block of a prescribed size from the number of times of appearance for each document that is recorded in the aggregation information  2114  so as to generate the appropriate code table  2113  on the basis of the number of times of appearance of for each block. 
       FIG. 26  illustrates an example of the code table  2113  of compression codes. Each entry in the code table  2113  illustrated in  FIG. 26  includes an ID for identifying a word, semantic structure information and nested-structure information, an intermediate code of the intermediate code table  2112 , and a compression code assigned in S 2205 . Note that the information of the word dictionary  813  and the information of the code table  2113  may be managed in a collective manner. 
     In S 2206 , the second conversion unit  2104  refers to the word dictionary  813  and the code table  2113  so as to assign compression codes to each word contained in each sentence in the compression target document  811  and to semantic structure information and nested-structure information contained in the semantic analysis result  812 . Then, the second conversion unit  2104  stores the compression codes assigned to the word, the semantic structure information and the nested-structure information in the storage unit  611  respectively as the word code  815 , the semantic structure code  816  and the nested-structure code  817 . Note that the compression codes in the code table  2113  assigned to the word, the semantic structure information, and the nested-structure information are herein referred to respectively as the word code  815 , the semantic structure code  816 , and the nested-structure code  817 . However, embodiments are not limited to this example. For example, intermediate codes that are associated with a word, semantic structure information and nested-structure information in the code table  2113  may also be used as the word code  815 , the semantic structure code  816  and the nested-structure code  817 . 
     In S 2207 , the output unit  614  arranges the word code  815 , the semantic structure code  816  and the nested-structure code  817  in a prescribed order so as to generate a compression code string, and outputs the generated compression code string, the code table  2113  and the aggregation information  2114  to for example the storage unit  611 . Alternatively in a different embodiment, the output unit  614  may output a compression code string, the code table  2113  and the aggregation information  2114  to for example an information processing apparatus that performs a utilization process. Examples of a prescribed order may include the first order or the second order that is described above. 
     The encoding process illustrated in  FIG. 22  reduces the loads of utilization processes similarly to the encoding process illustrated in  FIG. 9 . Further, a compression code string of the compression target document  811 , the code table  2113  and the aggregation information  2114  are output in an associated manner, making it possible to manage these pieces of information in a unified manner. Using the semantic analysis result  812  and the aggregation information  2114  together increases the accuracy and the speeds of the utilization process. 
     The second embodiment describes an example in which the code generation unit  612  performs a semantic analysis by using a word contained in a morphological analysis result and thereafter assigns codes to the word and semantic structure information and nested-structure information. This makes it possible for a semantic analysis of an encoding process according to an embodiment to use for example an application for performing an existing semantic analysis. However, embodiments are not limited to this example. For example, a different embodiment may employ a configuration in which the code generation unit  612  assigns a code to a word contained in a morphological analysis result so as to perform a semantic analysis by using the encoded word. 
     &lt;Utilization Process&gt; 
     Next, a utilization process of a compression code string generated by the above process will be exemplified. 
     First Utilization Example 
     In a first utilization example, a case will be exemplified in which a compression code string is utilized for extracting a synonym. A synonym may be for example a word that has a different word form but is used to have a similar meaning. For example, examples of synonyms may include, among others, a combination of “HON (book)” and “SHOMOTSU (volume)”, a combination of “BYOUKI (illness)” and “YAMAI (disease)”, a combination of “TATERU (set)” in a case of “BITTO WO TATERU (set a bit)” and “ONN SURU (turn on)” in a case of BITTO WO ONN SURU (turn on a bit). Then, a semantic analysis result contained in a compression code string can be utilized for extracting such a synonym from writing. 
       FIG. 27  illustrates a functional configuration example of an information processing apparatus  2700  that performs a utilization process. The information processing apparatus  2700  may include for example a control unit  2701  and a storage unit  2710 . The control unit  2701  may be implemented by for example a processor executing a program. Also, the storage unit  2710  of the information processing apparatus  2700  may be for example a memory. The storage unit  2710  in the information processing apparatus  2700  stores for example the word dictionary  813 , the code table  2113 , the aggregation information  2114  and a compression code string  2711 . The compression code string  2711  may be for example information in which the word code  815 , the semantic structure code  816 , and the nested-structure code  817  that are output by the output unit  614  of the encoding device  2100  are arranged in a prescribed order. Also, the information processing apparatus  2700  may be for example the encoding device  2100  that generated the compression code string  2711 . 
       FIG. 28  exemplifies an operation flow in a case when the compression code string  2711  is utilized for extracting a synonym. In S 2801 , the control unit  2701  sets, as a search target, the aggregation information  2114  that has been aggregated in units of documents. In S 2802 , the control unit  2701  receives, from the user, for example input of an expression that serves as a key for the synonym extraction. Note that an expression that is input as a key may be in a form of for example a word, concept information of a word, a sentence containing an arc, or alternatively information related to a word, concept information of a word and an arc may be received as such an expression through user manipulation. Note that an expression that is input as a key is in a form of a sentence, and the control unit  2701  performs a semantic analysis of the input sentence, and thereby can obtain information such as a word, concept information of a word, an arc, etc. Also, an expression that is input as a key in this example may be an expression having a tendency to have its synonyms appearing often. An expression having a tendency to have its synonyms appearing often may be obtained by for example extracting an expression in writing in which a synonym registered in an existing thesaurus is utilized. 
     Next, in S 2803 , the control unit  2701  encodes an expression that is input as a key. For example, the control unit  2701  converts, into intermediate codes, a word, concept information of a word, and an arc contained in the expression that is input as a key. In other words, for example a word is converted into a word code, and concept information of a word and an arc are converted into a semantic structure code and a nested-structure code. Note for example that words may be interconverted with intermediate codes by the code table  2113  and that concept information of words and arcs may be interconverted with intermediate codes in accordance with a prescribed rule. 
     Next, in S 2804 , the control unit  2701  determines a document that is to be a search target, on the basis of the aggregation information  2114  and intermediate codes. For example, the control unit  2701  may refer to the aggregation information  2114  so as to determine, as a search target, a document containing a word code and a semantic structure code obtained by encoding an expression input as a key. 
     In S 2805 , the control unit  2701  uses an expression input as a key in S 2802  so as to search the compression code string  2711  of a document determined to be a search target in order to extract a compression code string of a sentence containing the expression as a key. For example, the control unit  2701  may use the code table  2113  so as to convert the compression code of the compression code string  2711  into intermediate codes and generate an intermediate code string. Also, the control unit  2701  may search the generated intermediate code string in order to extract an intermediate code string corresponding to a sentence containing a word, concept information of a word, and an arc by using intermediate codes corresponding to a word, concept information of a word, and an arc contained in an expression that is input as a key. 
     In S 2806 , the control unit  2701  outputs, as a synonym candidate, a word having a possibility of being a synonym from an intermediate code string corresponding to an extracted sentence. For example, in an intermediate code string corresponding to an extracted sentence, the control unit  2701  identifies an intermediate code of a word connected to a word input as a key by an arc input as a key, on the basis of a semantic structure code and a nested-structure code that are encoded into the intermediate code string corresponding to the extracted sentence. Then, the control unit  2701  converts the identified intermediate code of a word into the word by using the word dictionary  813  and the code table  2113 , and outputs the word as a synonym candidate. Note that in a different embodiment, the control unit  2701  may output a word that is still in a form of an intermediate code or may convert a word into a compression code to output it. 
       FIG. 29  illustrates an example in which a search is performed for “set”, which has the concept information of the word “UP” after the appearance of bit. Note that the example illustrated in  FIG. 29  has replaced a compression code in the compression code string with an intermediate code by using the code table  2113 . Also, instead of a search for the intermediate code of “0xC02651” (bit) of a word, a search has been performed for 48 bits of “0x04201902651” that is based on the last 24 bits of “0x042019” of an intermediate code of semantic structure information and the intermediate code of “0xC02651” (bit) of the word. By arranging the semantic structure code  816  and the word code  815  in series as described in  FIG. 19 , the control unit  2701  can perform a search in a compressed state on the basis of a combination between concept information and a word adjacent to that concept information. When for example a series of concept information, an arc, and a bit input as a key is detected as a result of searching, the control unit  2701  may output, as a synonym candidate, a word that is connected to a word:bit by an arc input as a key. Note that the control unit  2701  can increase the accuracy of synonym extraction by further confirming for example whether or not the concept information of an extracted word is concept information of “UP”. 
     As described in  FIG. 28  and  FIG. 29 , locating the semantic structure code  816  and the word code  815  in such a manner that they are adjacent to each other realizes high-speed detection of a word that corresponds to a particular semantic structure in a compression code string. Also, when a synonym is extracted on the basis of for example a juncture in a scope specified by using a scheme such as n-gram, one word sometimes has a plurality of qualifiers, resulting in a remote qualification relationship. However, because for example a semantic analysis locates nodes in qualification relationships adjacent to each other, a search can be performed for a word that is directly connected to a given word by an arc by using a semantic analysis result, resulting in high accuracy in the extraction of a synonym. 
     Second Utilization Example 
     The second utilization example exemplifies a case where the compression code string  2711  is utilized for knowledge extraction. It is possible to consider a case where knowledge is extracted for classifying questions from an article containing questions posted to a Q and A (Question and Answer) site and answers to them. 
     It is assumed for example that there is an answer of “system file of operating system or information necessary for activating hard disk may be damaged”. In such a case, three pieces of knowledge that the article is about for example “OPERATINGU SISUTEMU NO SISUTEMU FAIRU (system file of operating system)”, “HARDO DISUKU NO KIDOU (activation of hard disk)” and “JOUHOU NO HASON” (damage to information)” may be extracted from this sentence. Also, a semantic analysis result contained in the compression code string  2711  may be used for the extraction of for example these pieces of knowledge. 
       FIG. 30  exemplifies an operation flow in a case when a compression code string is utilized for knowledge extraction. In S 3001 , the control unit  2701  sets, as a search target, the aggregation information  2114  aggregated in units of documents. In S 3002 , the control unit  2701  receives the input of for example a search key for knowledge extraction from the user. Note that a search key may be received in a form of for example a sentence containing an arc often included in an expression that can be used as knowledge, or information related to an arc may be received as a search key through user manipulation. Also, a search key effective for the extraction of knowledge can be obtained by identifying an arc often included in knowledge on the basis of for example knowledge that has already been extracted or other information. 
     In S 3003 , the control unit  2701  converts an arc obtained from an input search key into an intermediate code in accordance with a prescribed rule. 
     Next, in S 3004 , the control unit  2701  determines a document that is to be a search target on the basis of the aggregation information  2114  and the intermediate code. For example, the control unit  2701  may determine, to be a search target, a document containing the intermediate code of an obtained arc, on the basis of the aggregation information  2114 . 
     In S 3005 , the control unit  2701  searches, by an arc obtained from an input search key, the compression code string  2711  of a document determined to be a search target, and outputs, as a knowledge candidate, two words connected by the arc for which the search has been performed. For example, the control unit  2701  may use the code table  2113  to convert the compression code string  2711  of a search target document into an intermediate code string and identify an intermediate code string of a sentence containing an intermediate code corresponding to an input arc in the obtained intermediate code string. Further, the control unit  2701  identifies the intermediate codes of the two words connected by the arc in for example the identified intermediate code of a sentence on the basis of a semantic structure code and a nested-structure code that have been encoded into the identified intermediate code string corresponding to a sentence. Also, the control unit  2701  may convert the identified intermediate codes of two words into the words by using the code table  2113  and the word dictionary  813  so as to output it. Note that in a different embodiment, the control unit  2701  may output a word that is still in a form of an intermediate code or may convert a word into a compression code to output it. Also, for example the arc obtained from the search key and the two words that were output may be utilized as knowledge for classifying articles of Q and A. 
     Third Utilization Example 
     In the third utilization example, an example in which the compression code string  2711  is utilized for document revision is exemplified. For example, the information processing apparatus  2700  may utilize a semantic analysis result in order to prompt correction of a sentence in a case when writing contains a sentence that allows a plurality of interpretations. 
     For example, when there is a sentence “MEMORI A NI HYOUJISARERU MESSEIJI WO KAKUNOU SURU” (“A message that is displayed is stored in a memory”), it is not clear whether a message is displayed in memory A or a message is stored in memory A. In order to extract a sentence that tends to be ambiguous, a semantic analysis result contained in the compression code string  2711  can be utilized. 
       FIG. 31  exemplifies an operation flow in a case where the compression code string  2711  is utilized for sentence revision. In S 3101 , the control unit  2701  sets, to be a search target, aggregation information  2114  aggregated in units of documents. In S 3102 , the control unit  2701  receives, from the user, the input of a search key for extracting for example a sentence that is desirably to be revised. For example, an expression of a search key may be a series of a plurality of arcs. Note that an expression serving as a search key may be received in a form of for example a sentence containing a plurality of arcs of a prescribed series, or information related to a series of a plurality of prescribed arcs may be received through user manipulation. Also, a search key that is effective for extracting a sentence that is desirably to be revised can be obtained by identifying a series etc. of arcs that tend to give ambiguity to a sentence obtained from a plurality of existing sentences that are desirably to be revised. 
     In S 3103 , the control unit  2701  converts for example a plurality of arcs obtained from an input search key into intermediate codes in accordance with a prescribed rule. 
     Next, in S 3104 , the control unit  2701  determines a document that is to be a search target on the basis of the aggregation information  2114  and the intermediate codes. For example, the control unit  2701  may refer to the aggregation information  2114  so as to determine, to be a search target, a document containing intermediate codes corresponding to a plurality of arcs obtained from a search key. 
     In S 3105 , the control unit  2701  uses the code table  2113  to convert a compression code string of the document determined to be a search target into an intermediate code string. Then, the control unit  2701  searches the obtained intermediate code string of the document by using intermediate codes corresponding to a plurality of arcs that are arranged in a prescribed order and that were obtained from the expression serving as the search keys input in S 3102 . Then, the control unit  2701  identifies an intermediate code string of a sentence containing a plurality of arcs arranged in the prescribed order on the basis of the semantic structure code and the nested-structure code that were encoded into the intermediate code string of the document, and outputs the intermediate code string of the document. The output sentence may be for example a sentence that is highly likely to be a sentence that is desired to be revised and may be used for prompting the user to perform correction, etc. Note that the output sentence may be output in a form of an intermediate code or a compression code or may be decoded into the original word. The control unit  2701  may use the code table  2113  so as to perform conversion between words, intermediate codes and compression codes. 
     As exemplified in the first through third utilization examples above, the information processing apparatus  2700  can utilize a semantic analysis result in various processes by using for example the compression code string  2711  output from the encoding device  2100 . Thereby, a semantic analysis does not have to be performed for utilization, leading to the reduction in the process loads of utilizing a semantic analysis result. Note that while the above utilization examples have exemplified a case where the scope of documents as search targets are narrowed effectively by using the aggregation information  2114 , the utilization examples of the embodiments are not limited to them. For example, a different utilization example does not have to use the aggregation information  2114 . 
     Variation Embodiment of First Utilization Example 
     The variation embodiment of the first utilization example exemplifies the utilization in the synonym extraction of the compression code string  2711  in a case when the aggregation information is not used. Note that in the variation embodiment, the storage unit  2710  in the information processing apparatus  2700  stores the code table  814  instead of the code table  2113 , and does not have to store the aggregation information  2114 . Also, the compression code string  2711  may be a compression code string that is output from the encoding device  800  according to the first embodiment. 
       FIG. 32  exemplifies an operation flow of the variation embodiment of the first utilization process that uses the compression code string  2711  for synonym extraction. In S 3201 , the control unit  2701  receives the specifying of a document that is a search target by the user. In S 3202 , the control unit  2701  receives from the user the input of for example an expression serving as a key for the synonym extraction. Note that an input expression serving as a key may be in a form of for example a sentence containing a word, concept information of a word and an arc, or information related to a word, and concept information of a word and an arc may be received as such an expression through user manipulation. Also, an expression that is input as a key may be an expression for which its synonyms appear often. 
     Next, in S 3203 , the control unit  2701  converts for example a word, concept information of a word, and an arc contained in an expression input as a key into corresponding compression codes by referring to the word dictionary  813  and the code table  814  or in accordance with a prescribed rule. 
     In S 3204 , the control unit  2701  searches the compression code string  2711  that is a search target specified in S 3201  by using a compression code of an expression as a key converted in S 3203 , and extracts a compression code string of a sentence containing the expression as the key. 
     In S 3205 , the control unit  2701  outputs, as a synonym candidate, a word having a possibility of being a synonym from a compression code string of an extracted sentence. For example, the control unit  2701  identifies a word code of a word connected to a word input as a key, by an arc input as the key and on the basis of a semantic structure code and a nested-structure code encoded into a compression code string corresponding to the extracted sentence. Then, the control unit  2701  converts the identified word code into a word by using the word dictionary  813  and the code table  814  so as to output the resultant word as a synonym candidate. Note that in a different embodiment, the control unit  2701  may output a word that is still in a form of a word code. 
     As described above, in the variation embodiment of the first utilization example, the information processing apparatus  2700  can use for example the compression code string  2711  output from the encoding device  800  so as to use a semantic analysis result in various processes without decompression. Thereby, decompression or semantic analysis does not have to be performed for utilization, leading to the reduction in the process loads of utilizing a semantic analysis result. 
     Note that while the above examples explain cases of Japanese, the embodiments are not limited to them, and the embodiments may be applied to any other language including for example English, Chinese, etc. 
     The encoding devices  600 ,  800  and  2100  illustrated in  FIG. 6 ,  FIG. 8  and  FIG. 21  and the information processing apparatus  2700  of  FIG. 27  for performing a utilization process can be implemented by for example an information processing apparatus (computer)  3300  illustrated in  FIG. 33 . 
     The information processing apparatus  3300  illustrated in  FIG. 33  includes a processor  3301 , a memory  3302 , an input device  3303 , an output device  3304 , an auxiliary storage device  3305 , a medium driving device  3306  and a network connection device  3307 . These constituents are connected to each other via a bus  3308 . 
     The memory  3302  is for example a semiconductor memory such as a Read Only Memory (ROM), a Random Access Memory (RAM), a flash memory, etc. The memory  3302  stores a program and data for an encoding process or a utilization process. The memory  3302  may be used as for example the storage unit  611  illustrated in  FIG. 6 ,  FIG. 8  and  FIG. 21  or as the storage unit  2710  illustrated in  FIG. 27 . 
     The processor  3301  executes a program by using for example the memory  3302  so as to operate as the code generation unit  612 , the output unit  614  and the morphological analysis unit  801  illustrated in  FIG. 6 ,  FIG. 8  and  FIG. 21  so as to perform an encoding process. The processor  3301  also operates as the first conversion unit  2101 , the aggregation unit  2102 , the generation unit  2103  and the second conversion unit  2104  illustrated in  FIG. 21 . Alternatively, the processor  3301  operates as the control unit  2701  illustrated in  FIG. 27  by for example using the memory  3302  to execute a program so as to perform a utilization process. 
     The input device  3303  is for example a keyboard, a pointing device, etc., and is used for inputting instructions or information from the user or the operator. The output device  3304  is for example a display device, a printer, a speaker, etc., and is used for outputting inquiries or process results to the user or the operator. The process result may be a result of a utilization process. 
     The auxiliary storage device  3305  is for example a magnetic disk device, an optical disk device, a magnetooptical disk device, a tape device, etc. The auxiliary storage device  3305  may be a hard disk drive or a flash memory. The information processing apparatus  3300  may store a program and data in the auxiliary storage device  3305  so as to load them onto the memory  3302  to use them. The auxiliary storage device  3305  can be used as the storage unit  611  illustrated in  FIG. 6 ,  FIG. 8  and  FIG. 21  or as the storage unit  2710  illustrated in  FIG. 27 . 
     The medium driving device  3306  drives a portable recording medium  3309  so as to access information recorded in it. The portable recording medium  3309  is for example a memory device, a flexible disk, an optical disk, a magnetooptical disk, etc. The portable recording medium  3309  may be a Compact Disk Read Only Memory (CD-ROM), a Digital Versatile Disk (DVD), a Universal Serial Bus (USB) memory, etc. The user or operator can store a program and data in the portable recording medium  3309  so as to load them onto the memory  3302  to use them. 
     As described above, a computer-readable recording medium having stored therein a program and data is a physical (non-transitory) recording medium such as the memory  3302 , the auxiliary storage device  3305  and the portable recording medium  3309 . 
     The network connection device  3307  is a communication interface that is connected to a communication network such as a Local Area Network (LAN), the Internet, etc. so as to perform data conversion for communications. The information processing apparatus may receive a program and data from an external device via the network connection device  3307  so as to load them onto the memory  3302  to use them. The network connection device  3307  enables for example the encoding devices  600 ,  800  and  2100  or the information processing apparatus  2700  for performing a utilization process to transmit and receive the code table  2113 , the aggregation information  2114 , a compression code string, etc. 
     Note that the information processing apparatus  3300  does not have to include all the constituents illustrated in  FIG. 33 , and some of them can be omitted in accordance with the purposes or conditions. For example, the input device  3303  can be omitted when the user or the operator does not input instructions or information, and the output device  3304  can be omitted when inquiries or process results are not output to the user or the operator. The medium driving device  3306  or the network connection device  3307  can be omitted when the information processing apparatus  3300  does not access the portable recording medium  3309  or a communication network. 
     As described above, the embodiments can reduce the process loads imposed when a semantic analysis result of a document is utilized. 
     While the embodiments of the disclosure and their advantages have been explained in detail, those skilled in the art will be allowed to make various changes, additions and omissions without departing from the scope of the present invention, which is clearly described in 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.