JPH1153397A - Device and method for document processing and storage medium storing document processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To decide the similarity with high accuracy between an acquired object document and another document by using one of discriminated text and non-text data for decision of the similarity. SOLUTION: A CPU 111 performs a morphological analysis to extract the independent words of a retrieval object document A, or the like, or a document B of interest, or the like. Then the CPU 111 decides the importance of every candidate word (phrase) based on the frequency of occurrence, the evaluation function, etc., set in every document for the candidate words (phrases) which are extracted from the document A or B and then decides every document vector based on the importance with the importance of every keyword of the document A or B used as an element. Furthermore, the CPU 111 decides the similarity between both documents A and B based on the cosine that is dependent on the angle set between the vectors of both documents. Then it is decided that the document A is similar to the document B when the similarity value is larger than its threshold.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0002]

The present invention relates to a document processing apparatus,
The present invention relates to a storage medium storing a document processing program and a document processing method, and more particularly to improving the accuracy of similarity determination of acquired documents.

[0003]

2. Description of the Related Art Conventionally, various kinds of documents such as books, papers, reports, etc., such as an automatic search process by a vector space method or other methods, a search process of similar documents, a process of associating with other documents, and the like. The processing is performed using a computer.

[0004]

However, when a conventional document processing apparatus searches for a document similar to a target document, for example, the target document or a document in a database contains a program language other than text data. In addition, when non-text data such as a source program, table data, and a grab schema is included, it is not possible to obtain very high accuracy even if the similarity is determined. This is because, for example, if similarity to a file created by spreadsheet software is determined, the file will be dominated by numeric data, and even if keywords and important words for determining similarity are extracted, numeric data For the majority.

SUMMARY OF THE INVENTION The present invention has been made to solve such a conventional problem, and provides a document processing apparatus capable of determining the similarity between an acquired target document and another document with high accuracy. The primary purpose is to provide. A second object of the present invention is to provide a storage medium storing a computer-readable document processing program capable of determining the similarity between an acquired target document and another document with high accuracy. I do. A third object of the present invention is to provide a document processing method capable of determining the similarity between an acquired target document and another document with high accuracy.

[0006]

According to the first aspect of the present invention, a target document acquiring unit for acquiring a target document to be processed and a target document acquired by the target document acquiring unit other than text data. Determining means for determining whether or not the target document has non-text data; and distinguishing between text data and non-text data from the target document when the determining means determines that the document has a non-text document. Means and a similarity determining means for determining similarity with another document using at least one of the text data and the non-text data distinguished by the distinguishing means. Achieve the first objective. According to a second aspect of the present invention, in the document processing apparatus according to the first aspect, the non-text data includes table data, schema information of a graph, a programming language, a source program, and the like. According to a third aspect of the present invention, in the document processing apparatus according to the first aspect, when the non-text data distinguished by the distinguishing means is a source program, the converting means for converting the source program into an intermediate language is provided. The similarity determination unit determines the similarity between the intermediate language programs converted by the conversion unit. According to a fourth aspect of the present invention, in the document processing apparatus according to the first, second, or third aspect, the similarity determination unit includes a calculation unit that calculates a similarity by a vector space method. The similarity is determined from the similarity calculated by the calculation means. According to the fifth aspect of the present invention, a target document acquisition function for acquiring a target document to be processed, and whether the target document acquired by the target document acquisition function has non-text data other than text data A judgment function for judging whether or not the target document has a non-text document, and a discrimination function for discriminating text data and non-text data from the target document; A computer-readable document processing program for causing a computer to implement a similarity determination function of determining similarity with another document using at least one of text data and non-text data is stored in a storage medium. Thus, the second object is achieved. In the invention described in claim 6, in the storage medium according to claim 5, the non-text data includes table data, graph schema information,
Program language, source program, etc. Claim 7
The storage medium according to claim 5, further comprising, when the non-text data distinguished by the distinguishing function is a source program, a conversion function of converting the source program into an intermediate language. The gender determination function determines the similarity between the intermediate language programs converted by the conversion function. According to the invention described in claim 8, in the storage medium described in claim 5, 6, or 7, the similarity determination function has a calculation function of calculating a similarity by a vector space method, The similarity is determined from the similarity calculated by the calculation function. According to the ninth aspect of the present invention, text data and non-text data are distinguished from a target document to be processed, and at least one of the distinguished text data and non-text data is used. The third object is achieved by judging the similarity with the document.

[0007]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of a document processing apparatus and a storage medium storing a document processing program according to the present invention will be described below with reference to FIGS. (1) Outline of Embodiment In this embodiment, by interpreting the meaning of table data and graph schemas, a particular document (target document) can be obtained based on accurate clues, especially on numerical expressions. Related numerical data and graphs can be ordered. Specifically, for each individual table data file, a given,
Alternatively, a real value with a unit is calculated based on schema (× 100 yen, Kg / m, etc.) information attached to another table data having the same structure and similar numerical contents. The result is clarified and subjected to similarity search. At this time, the axis name of the graph, the unit display thereof, and the title of the graph are also subjected to similarity determination by increasing the weight. Similarly, by applying the normalization of the numerical expression in the text, it is possible to improve the performance of the similarity search based on the numerical data. In addition, by differentiating local semantics by a reserved word conversion table or a syntax interpretation routine that gives the semantics of a programming language and a result of conversion into an intermediate language, heterogeneous source programs can be subjected to similarity search. A similar language conversion process is performed on the source program, and furthermore, the comment area in the natural language is clarified for the original English or Japanese, and a two-layer transposed file is prepared for the same file. As a result, the document can be hit with similarity to a text-centered document, and can be hit with similarity to another source program.

(2) Details of Embodiment FIG. 1 is a block diagram showing the configuration of a document processing apparatus.
The document processing apparatus according to the present embodiment can be configured in a computer system including a personal computer, a word processor, and the like. As shown in FIG. 1, the document processing apparatus includes a control unit 11 for controlling the entire apparatus. A keyboard 12 and a mouse 13 as input devices, a display device 14, and a printing device 15 are transmitted to the control unit 11 via a bus line 21 such as an address bus and a data bus.
, Storage device 16, storage medium drive device 17, communication control device 18, input / output interface (I / F) 19
And the character recognition device 20 are connected.

The control unit 11 includes a central processing unit (CPU) 1
11, a ROM 112, and a RAM 113.
The ROM 112 is a read-only memory in which various programs and data for the CPU 111 to perform control and calculation are stored in advance. The RAM 113 is a random access memory used as a working memory by the CPU 111. In the RAM 113, various areas for performing the processing according to the present embodiment are secured.

The keyboard 12 has various keys such as kana keys and numeric keys for inputting kana characters, numbers, and symbols, function keys for executing various functions, and cursor keys. The mouse 13 is a pointing device, and is an input device for designating a corresponding function by left-clicking a key, an icon, or the like displayed on the display device 14. As the display device 14, for example, a CRT or a liquid crystal display is used. This display device displays input results from the keyboard 12 and the mouse 13,
The processing result of the PU 111 is displayed. The printing device 15 prints a document displayed on the display device 14, a document stored in the document database 164 of the storage device 16, a processing result by the CPU 111, and the like. Various printing apparatuses such as a laser printer, a dot printer, an ink jet printer, a page printer, a thermal printer, and a thermal transfer printer are used as the printing apparatus.

The storage device 16 comprises a readable and writable storage medium and a drive device for reading and writing various information such as programs and data on the storage medium.
As a storage medium used for the storage device 16, a hard disk is mainly used, but a readable / writable storage medium of various storage media used in a storage medium drive device 17 described later is used. Is also good. The storage device 16 includes a kana-kanji conversion dictionary 161, a program storage unit 162, a data storage unit 163, a document database 16
4, a document vector database 166, and other storage units (not shown) (for example, storage units for backing up programs, data, and the like stored in the storage device 16). In the program storage unit 162,
The document vector program and other programs according to the present embodiment are stored. Data storage unit 163
Stores various data.

The document database 164 stores typical documents and ordinary documents. As typical documents, for example, when determining a workflow of a target document, a typical document that is used to assist in determining a workflow such as searching for a similar document and referring to the workflow of the similar document. Documents with typical contents are stored. When classifying target documents, documents having typical contents representing the characteristics of each classification are stored as typical documents. The format of each document stored in the document database 164 is not particularly limited, and includes various types of document data such as text format documents, HTML (Hyper Text Markup Language) format documents, and JIS format documents. Various data, programs, source programs,
Documents and data in which these are mixed are also stored.

The document vector data 166 stores a document vector corresponding to each document, data, program or source program stored in the document database 164.

FIG. 2 shows a document vector database 166.
Are conceptually represented. As shown in FIG. 2, the importance f (x) obtained for a document, data including words and schema information, or a keyword x automatically extracted from a program or the like is a key element value f ( x) is stored as an element value of a document vector for each document, each data, each program or each source program. This document vector represents each document (Aa,
Ba, Ca,...), Each data (Ab, Bb, Cb,
..), Each program (Ac, Bc, Cc,...) Or each source program (Ad, Bd, Cd,...), And each document, each data, each program or each program stored in the document database 164. Corresponds to the source program. The dimension of each document vector is the number of keywords x (keywords) to be adopted. When the similarity between two documents is obtained from both document vectors, the number of unions of keywords of both documents is equal to both document vectors. Of dimension. In this case, the element value of the other document vector for the keyword included only in one document vector is “
0 "is defined.

For example, in FIG. 2, the keywords of the document Aa are “important, important words, importance,...”, The keywords of the document Ca are “important,..., Politics,. It is as follows. Document vector of document Aa = (1,18,19, ...) Document vector of document Ca = (18, ..., 21, ...) On the other hand, when calculating the similarity between document Aa and document Ca, The keywords of both documents are "important, important words, importance, ..., politics, ...", and the document vectors of both documents are defined as follows. Document vector of document Aa = (1, 18, 19,...,
0,...) Document vector of document Ca = (18, 0, 0,.
1,…)

The storage medium drive 17 (FIG. 1) has a CPU
Reference numeral 111 denotes a driving device for reading data including computer programs and documents from an external storage medium.
The computer programs and the like stored in the storage medium include programs for various processes executed by the document processing apparatus of the present embodiment, and dictionaries and data used therein. Here, the storage medium refers to a storage medium in which a computer program, data, and the like are stored, and specifically, a magnetic storage medium such as a floppy disk, a hard disk, a magnetic tape, a memory chip or an IC.
Semiconductor storage media such as cards, CD-ROM, MO, PD
(A phase change rewritable optical disk) or other storage medium from which information can be read optically, a storage medium using paper (such as a paper card or paper tape) (and a medium having a function equivalent to paper), and a computer using various other methods. It includes a storage medium in which programs and the like are stored. The storage medium used in the document processing apparatus of the present embodiment is mainly a CD.
-A storage medium such as a ROM or a floppy disk is used. The storage medium drive 17 reads a computer program from these various storage media, and also writes an RA to a writable storage medium such as a floppy disk.
It is possible to write data or the like stored in the M113 or the storage device 16.

In the document processing apparatus of the present embodiment, the CPU 111 of the control unit 11 reads a computer program from an external storage medium set in the storage medium drive 17 and stores it in each unit of the storage device 16 (installation).
I do. When executing various processes according to the present embodiment, the corresponding program is stored in the RAM 113 from the storage device 16.
To read and execute. However, it is also possible to read the data directly from the external storage medium into the RAM 113 by the storage medium driving device 17 instead of the storage device 16 and execute it. Also, depending on the document processing device,
The program and the like of the present embodiment may be stored in the ROM 112 in advance, and the CPU 111 may execute the program. Further, various programs and data such as the processing program of the present embodiment may be downloaded from another storage medium via the communication control device 18 and executed.

The communication control device 18 is capable of transmitting and receiving various types of documents such as text format and HTML format and various data such as bitmap data to and from other personal computers and word processors. I have. The input I / F 19 is an interface for connecting various devices such as a speaker that outputs voice, music, and the like. The character recognition device 20 is a device for recognizing characters written on paper or the like in various formats such as a text format or HTML, and is configured by an image scanner, a character recognition program, and the like.

The similarity judging operation for judging the similarity of a document or the like by the document processing apparatus of the present embodiment configured as described above will be described with reference to FIGS. FIG.
FIG. 4 to FIG. 9 conceptually illustrate a process of determining the degree of similarity in the main operation of the process of determining whether or not documents and the like are similar. (A) to (H) on the right side of the flowchart in FIG. 3 correspond to (A) to (H) in FIGS. 4 to 8.

The CPU 111 acquires the target document A (FIG. 4A) to be processed and stores it in the RAM 113 (step 11). The target document etc. A is stored in the RAM 113 (in the case where the document is created in the own device), the document database 164 of the storage device 16, the storage medium driving device 17 (the document created in the own device or another device) in accordance with a user's instruction. Is obtained from the communication control device 18 (in the case of communication by personal computer communication, the Internet, etc.). The target document A may be a text document, an HTML document, a JIS document, a document in another format, various data, a program, a source program, or a document or data in which these are mixed.

Next, as shown in FIG. 4B, the CPU 111 determines whether the similarity with the target document A
(Other documents) are read from the document database 164 and stored in the RAM 113 (step 12). If a document vector has already been created and stored in the document vector database 166 for this document of interest B, it is not necessary to use the created document vector. In addition, the noticed document B is also a text document, HT
ML documents, JIS documents, documents in other formats, various data,
There may be a program, a source program, and a document or data in which these are mixed.

Next, the CPU 111 acquires an input value when a necessary parameter (for example, a threshold value for similarity determination) is input from the keyboard 12 or the like by the user, and acquires the input value when there is no input by the user. Storage unit 16
3 to obtain the default value of the parameter stored in
It is stored in M113 (step 13). Next, CPU1
11 determines whether the target document A is a document, data, a program, or the like (step 14). here,
When the CPU 111 determines that the document is a document (step 14; document), the CPU 111
A, etc. stored in the target document A (see reference numeral 50 in FIG. 5C)
(Step 15, see reference numeral 60 in FIG. 5C). Note that the document vector V is
In general, it is composed of the number of keywords (for example, n dimensions), but in FIGS. 5 to 8, it is represented by a two-dimensional vector because of the display relationship (the same applies hereinafter).

When the CPU 111 determines that the data is data (step 14; data), the CPU 111
Reference numeral 111 denotes a target document or the like A stored in the RAM 113,
The CPU 11 retrieves words such as titles and notes, schema information such as four arithmetic operations, 100 yen, and 10 kg / m (step 16, see reference numeral 51 in FIG. 5D).
1 acquires a document vector V for a target document A or the like stored in the RAM 113 (step 17, see reference numeral 61 in FIG. 5D).

When the CPU 111 determines that the program is a program or the like (step 14; program or the like), it is further determined whether the program is a program or a source program (step 18). Here, if the program is CP
When determined by U111 (step 18;
Y), the CPU 111 executes the corresponding program of the target document A stored in the RAM 113 (see reference numeral 52 in FIG. 6E).
Then, the program language is given a meaning by using a reserved conversion table or a syntax interpretation routine (see reference numeral 521 in FIG. 6E) that gives semantics to make it into a language (step 19, see reference numeral 522 in FIG. 6E). , RAM 11
3 in a predetermined area (step 20).
Reference numeral 11 denotes the conversion language (reference numeral 5) stored in a predetermined area.
22) (step 2)
1, see reference numeral 62 in FIG. 6 (E)).

On the other hand, if the source program is
11 (step 18; N),
The CPU 111 stores the target document A stored in the RAM 113
Corresponding source program (see reference numeral 53 in FIG. 6 (F))
(Step 22, reference numeral 53 in FIG. 6F)
0), when it is determined that the program area is a natural language or a program area (step 22; N),
The programming language is given a meaning by using a reserved conversion table or a syntax interpretation routine (see reference numeral 531 in FIG. 6 (F)) which gives semantics to make the RAM 113
(Step 24, see reference numeral 532 in FIG. 6F), and store it in a predetermined area of the RAM 113 (step 25), and the CPU 111 stores the conversion language (reference numeral 532) stored in the predetermined area. ) Is obtained (step 25, reference numeral 63 in FIG. 6 (F)).
a). Also, the CPU 111 executes the corresponding source program of the target document A stored in the RAM 113 (FIG. 6F).
(See reference numeral 53) (step 2).
2; Y, see reference numeral 530 in FIG. 6 (F)), a natural language comment description area (see reference numeral 533 in FIG. 6 (F))
, The document vector V is obtained as it is (step 27, see reference numeral 63b in FIG. 6F).

Next, it is determined whether or not all documents have been completed (step 28). In this case, since all of the target documents etc. A are not completed, and the target document B etc. are not completed (step 28; N), the process returns to step 14 again, and the processing of the remaining documents of the target document etc. A is performed. Execute, or calculate the document vector of the document of interest B or the like. This operation is exactly the same as the above-described operation, and a description thereof will not be repeated. Note that as described above, the noted document B
If the document vector is calculated in advance for
There is no need to do this.

FIG. 9 is a flowchart showing the operation of the document vector creation processing.
This is for explaining the details of the processing contents of 7, 21, 25 and 27. The CPU 111 extracts the independent words of the search target document A or the like and the noted document B or the like by performing a morphological analysis (step 151). Further, the CPU 111 extracts a candidate word (phrase) including a noun phrase, a compound noun phrase and the like from the target document etc. A and stores it in a predetermined work area of the RAM 113 (step 152). Then, the degree of importance f (x) of each candidate word (phrase) is determined from the appearance frequency of the extracted candidate word (phrase) in the target document A and the evaluation function (step 153).
Here, as the evaluation function, for example, when a predetermined important word is specified in advance, weighting for the important word, weighting according to the type of a candidate word (phrase) such as a word, a noun phrase, a compound noun phrase, and the like are used. used.

Further, the CPU 111 determines the determined importance f
The keywords a, b,... Of the target document A etc. are determined from the value of (x) (step 154). Importance f of each keyword
The document vector V = (f (a), f
(B),... Are stored in the document vector storage area of the RAM 113 (step 155), and the process returns to the processing routine of FIG. When the document vector V is obtained, the CPU 11
1 is a similarity S between the target document A etc. and the noted document B etc.
Is obtained by the cosine depending on the angle (q) between the document vector Av of the document A or the like and the document vector Bv of the document of interest B or the like (step 29). That is, assuming that the two document vectors are Av and Bv, and the absolute values of the two document vectors are | Av | and | Bv |, respectively, the similarity S between the two document vectors is obtained by the following equation (1).

[0029]

(1) Similarity S = cos (q) = (Av · Bv) / (| Av | × | Bv |)

The value of the similarity S takes a value up to −1 ≦ S ≦ 1, and as the value is closer to 1, the two document vectors are closer to each other in parallel, and the target document A and the target document B are similar. Can be considered. For example, in the case of a normal document, data or program, the document vector Av of the target document A etc. (reference numeral 70 in FIG. 7G) and the document vector Bv of the target document B etc. (reference numeral 70 in FIG. 7 (G)) 80), the similarity S is calculated using Expression 1 (reference numeral 9 in FIG. 7G).
0). This is represented by reference numeral 900 in FIG.
It can be calculated by taking the cosine of the angle (q) between the two document vectors Av and Bv.

In the case of a source program, the document vector Av of the target document A etc. (reference numeral 71 in FIG. 8H)
And the document vector Bv (reference numeral 81 in FIG. 8H) of the document of interest B, etc., are classified into a program system and a natural language system, and each angle of two document vectors Ava, Bva and document vectors Avb, Bvb Equation (1) for (qa, qb)
Is used to calculate the cosine, and the similarity S is calculated for each (reference numeral 91 in FIG. 8H). As shown by reference numerals 910a and 910b in FIG. 8H, the similarity Sa is obtained by taking the cosine of each of the angles (qa, qb) of the two document vectors Av and Bv of the program system and the natural language system. , Sb can be calculated.

When the CPU 111 determines that the value of the similarity S is larger than a predetermined threshold (for example, 0.8) (step 30; Y), the target document A etc. are similar to the target document B etc. And the result is stored in the RAM 113.
(Step 31). When the CPU 111 determines that the value of the similarity S is smaller than a predetermined threshold (for example, 0.8) (Step 30; N), the target document A is dissimilar to the target document B. And stores the result in a predetermined storage area of the RAM 113 (step 32).

When the above processing is completed, the CPU 111
Performs storage processing of each data stored in a predetermined storage area of the RAM 113 in accordance with a user's instruction. That is, the document vector V obtained in the document vector creation process (step 13 in FIG. 3, FIG. 7) is read from the RAM 113, and the target document A or the like stored in the document database 164,
The document is stored in the document vector database 166 of the storage device 16 in association with the document of interest B or the like. Also, the determination result is stored in the document vector database 166 or the like of the storage device 16 in association with the target document A or the like and the target document B or the like.

As described above, according to this embodiment, a file created by spreadsheet software is also searched for in natural language processing, and numerical data is included in information (schema) such as units and axis names of graphs. By making an inverted file, it is possible to improve the accuracy of numerical data search. In addition, by adding information such as units to the inverted file (a type of normalization), a file of spreadsheet software can be subjected to similarity search. In addition, by using the axis information, a graph or the like can be subjected to the similarity search. As described above, according to the present embodiment, it is possible to determine the similarity between the target document and other documents without making a meaningless determination, and to determine the validity data. The accuracy of the judgment can be improved. In particular, the retrieval accuracy for numerical data is improved, and the reusability of existing spreadsheet data files is dramatically improved.

Although the configuration and operation of the present embodiment have been described above, the present invention is not limited to these embodiments, and various modifications can be made within the scope of the invention described in the claims. It is possible to

According to the present invention, text data and non-text data are distinguished from a target document to be processed, and at least one of the distinguished text data and non-text data is used. Since the similarity with another document is determined, the similarity between the acquired target document and another document can be determined with high accuracy.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration of a document processing apparatus according to an embodiment of the present invention.

FIG. 2 is an explanatory diagram conceptually showing the contents of a document vector database in the embodiment.

FIG. 3 is a flowchart illustrating a main operation of a search process according to the embodiment.

FIG. 4 is a part of an explanatory diagram conceptually showing processing corresponding to each step of the search processing shown in FIG. 3 in the embodiment.

FIG. 5 is a part of an explanatory diagram conceptually showing processing corresponding to each step of the search processing shown in FIG. 3 in the embodiment.

FIG. 6 is a part of an explanatory diagram conceptually showing processing corresponding to each step of the search processing shown in FIG. 3 in the embodiment.

FIG. 7 is a part of an explanatory diagram conceptually showing processing corresponding to each step of the search processing shown in FIG. 3 in the embodiment.

FIG. 8 is a part of an explanatory diagram conceptually showing processing corresponding to each step of the search processing shown in FIG. 3 in the embodiment.

FIG. 9 is a flowchart illustrating an operation of a document vector creation process according to the embodiment.

[Explanation of symbols]

 Reference Signs List 11 control unit 111 CPU 112 ROM 113 RAM 12 keyboard 13 mouse 14 display device 15 printing device 161 kana-kanji conversion dictionary 162 program storage unit 163 data storage unit 164 document database 165 document vector database 17 storage medium drive device 18 communication control device 19 input Output I / F 20 Character recognition device

Claims (9)

[Claims] A target document acquisition unit for acquiring a target document to be processed; and a target document acquired by the target document acquisition unit.
Judgment means for judging whether or not to have non-text data other than text data, When it is judged by the judgment means to have a non-text document, text data and non-text data from the target document And a similarity determining unit that determines the similarity to another document by using at least one of the text data and the non-text data distinguished by the distinguishing unit. Characteristic document processing device. 2. The document processing apparatus according to claim 1, wherein the non-text data is table data, graph schema information, a programming language, a source program, or the like. 3. When the non-text data distinguished by the distinguishing means is a source program, the non-text data is provided with a converting means for converting the source program into an intermediate language, and the similarity determining means is converted by the converting means. 2. The document processing apparatus according to 1, wherein the similarity between the intermediate language programs is determined. 4. The apparatus according to claim 1, wherein the similarity determination unit includes a calculation unit that calculates a similarity by a vector space method, and determines the similarity from the similarity calculated by the calculation unit. The document processing apparatus according to claim 2. 5. A target document acquisition function for acquiring a target document to be processed, and a target document acquired by the target document acquisition function,
A determination function of determining whether or not non-text data other than text data is included, and when it is determined by the determination function that a non-text document is included, text data and non-text data are converted from the target document. A computer realizes a distinguishing function of distinguishing, and a similarity determining function of determining similarity with another document by using at least one of text data and non-text data distinguished by the distinguishing function. A storage medium storing the computer-readable document processing program of the present invention. 6. The storage medium according to claim 5, wherein the non-text data is table data, graph schema information, a programming language, a source program, or the like. 7. When the non-text data distinguished by the distinguishing function is a source program, the non-text data is provided with a converting function of converting the source program into an intermediate language. The similarity determining function is converted by the converting function. A storage medium storing the document processing program according to claim 5, wherein the similarity between the intermediate language programs is determined. 8. The similarity determination function has a calculation function of calculating a similarity by a vector space method, and determines similarity from the similarity calculated by the calculation function. A storage medium storing the document processing program according to claim 6. 9. A method according to claim 1, wherein the text data and the non-text data are distinguished from the target document to be processed, and at least one of the distinguished text data and the non-text data is used to determine a similarity to another document. A document processing method characterized by determining gender. [0001]