JPH09204418A - Document processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To convert a document for sufficiently obtaining effect that can help the understanding of a user. SOLUTION: A knowledge state management means 1 manages the understanding of the user on relation information for the respective relation types of plural words by a known record 1a. A relation information management means 2 manages relation information 2a required for helping the understanding of the user on the words. A word extraction means 4 extract the word 4a from an inputted original document 3. An information extraction means 5 acquires the understanding of the user on the word 4a which the word extraction means 4 extracts from the knowledge state management means 1 and extracts relation information from the relation information management means 2 in accordance with the understanding. A document generation means 6 adds relation information which the information extraction means 5 extracts to the original document 3 and generates the individual document 3a for supplying it to the user.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a document processing apparatus for modifying an existing document so that it can be easily understood, and more particularly to a document processing apparatus for modifying a document according to the degree of understanding of a user.

[0002]

2. Description of the Related Art Documents play a large role in human information transmission. It is the most popular means to preserve the results of human intellectual activity and to keep it in the form of documents for later use by other humans. Also, in daily communication, electronic documents such as electronic mail are often used these days. One of the biggest problems of information transmission by documents is the miscommunication caused by the difference between the knowledge of the creator of the document and the knowledge of the user of the document, and the burden on the creators of the documents. There is an increase in

When a user of a document reads the document, it often happens that he / she does not fully understand the contents of the document without knowing the definition of technical terms and related information in the document. As a result, the value of the content of a document cannot be accurately determined, and important information is often overlooked. In addition, the content of the document may be misunderstood, and the subsequent actions may be disturbed. In order to prevent such miscommunication, the user should make various inquiries to the document creator or another person who has the same knowledge as the document creator, if possible. It will be a heavy load for both parties.

In order to prevent miscommunication and an unnecessary load on the document user, the document creator selects a term or selects a term based on the knowledge level of the document user. Add definitions and related information to the document. Therefore, the less the knowledge shared between the creator of the document and the user, the more information has to be put in the document, which is a great burden on the document creator.

Further, when there are many users of the document, the knowledge possessed by the users is various, and the document creator has to create a document that the user can understand for each user, or if there are many users. Assuming the level of knowledge you think, you will create a single document for that level.

In the former case, however, the creator is burdened with creating a plurality of documents to convey the same information. In the latter case, the document users who share a lot of knowledge with the document creator have much unnecessary information,
It will take more time than necessary to read the document, and will not be fully understood by the document user who does not have the level of knowledge assumed by the document creator.

In order to solve such a problem, in the invention disclosed in Japanese Patent Laid-Open No. 7-93334, a computer simulates a known or unknown state of a word held by a user, and Among them, a step of automatically identifying a word that the user does not know, a step of automatically supplementing the unknown word with a synonym known by the user, and automatically displaying a document supplementing the synonym And a document processing method including the steps of:

Further, the above-mentioned publication discloses a means for simulating a known or unknown state of a word possessed by a user and automatically discriminating a word which is not known by the user among the words in the document, A document processing apparatus is also disclosed, which comprises means for automatically supplementing unknown words with synonyms known to the user, and means for automatically displaying a document supplementing the synonyms.

With such a document processing method and document processing apparatus, a synonym known to the user of the document is automatically added to a term unknown to the user of the document for each document user. Helps users understand and reduces the burden on the creator of the document.

[0010]

However, Japanese Patent Laid-Open No. 7-9 / 1995
In the disclosed invention (hereinafter, this invention is referred to as a conventional example), since only the synonyms of terms in the document are added to the document, the effect of helping the user to understand is insufficient. is there.

Certainly, for a user who does not know a technical term, paraphrasing it with a term known to the user has an effect of helping the user to understand, but because the meaning of the term is understood by it. However, the content of the document cannot be understood immediately. This is because there are various kinds of things that the user wants to know about a particular concept or term. For example, "definition of a concept or term", "how the concept or term appeared", "concept or term derived from a concept or term and related information", "concept or term related to a concept or term and its Related information ",
There are many other than this.

As described above, in the process of understanding the document by the user, the kind of information required varies depending on the state of the user's knowledge for each concept or term in the document. Therefore,
In the conventional example, the user still has to make a lot of troubles in order to obtain the above information about the concept or term in the document written by the creator. As a result, the creator of the document takes into consideration such a pain of the receiver and prepares a plurality of documents, and the hard work of including a lot of information in the document continues.

Further, in the conventional example, since the computer autonomously simulates the state of knowledge of the user, it is difficult to continuously reflect the state of true knowledge of the user. that is,
For the following reasons.

The first reason is that the state of change of the knowledge of the user is different for each user, and it is not possible to simulate the knowledge state effective for all users with certain rules and programs implemented on the computer. It is impossible.

The second reason is that the change of the user's knowledge occurs in various situations other than the work through the document processing apparatus according to the conventional example, and the change of the knowledge state there cannot be reflected. Is.

Therefore, as time goes by, the gap between the state of knowledge of the user and the state of knowledge simulated on the computer becomes severe, and synonyms are synonymous with words known to the user. Frequently, a synonym cannot be attached to an unknown word, or a synonym attached to an unknown word is unknown. That is, in the conventional example, there is no means for inquiring a synonym of an unknown word or a known synonym instead of the unknown synonym when such a deviation occurs. In addition, there is no way to correct the state of knowledge on a computer that deviates from the actual situation.

In order to solve this, it is conceivable to directly edit the state of knowledge managed on the computer with an editor or the like, but this is not realistic for the following reason.
The state of knowledge being simulated is enormous (it is expected that information on tens to hundreds of thousands of terms will exist on the computer even if the terms are known or unknown), and the modification is very manual. It is not possible by editing. In addition, since the result of correcting the state of knowledge is not necessarily reflected in the next document, the result of the correction does not lead to the direct benefit of the user. For this reason, the user recognizes that the burden of modification is unnecessary.

The present invention has been made in view of the above points, and reflects the true state of knowledge of the user.
An object of the present invention is to provide a document processing device for converting a document that can sufficiently obtain an effect of helping a user to understand.

[0019]

FIG. 1 is a block diagram showing the principle of the present invention. In the present invention, in order to solve the above-mentioned problems, a knowledge state management means 1 for managing a user's understanding of terms
A related information management unit 2 that manages a plurality of related information for a term in association with a related type; a term extraction unit 4 that extracts a predetermined term from an input original document 3; An information extraction unit 5 that acquires the degree of understanding of the extracted term of the user from the knowledge state management unit 1 and extracts related information of a type of association corresponding to the degree of understanding from the related information management unit 2. There is provided a document processing device characterized by the above.

According to this structure, when the original document 3 is input, the term extracting means 4 extracts a predetermined term from the original document 3. Next, the information extraction means 5 extracts relevant information for helping the user to understand the term according to the degree of understanding of the term by the user. Thereby, the related information according to the degree of understanding of the user is extracted, and the user can fully understand the content of the original document 3 by referring to the extracted related information.

[0021]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing the principle configuration of the present invention. The knowledge state management unit 1 manages a known record 1a in which a known degree of a user for a term, a related species of the term, and related information specified by the term and the related species is set. Here, the related type represents a type of related information that supplements the term, such as definition information, detailed information, or additional information. The known degree may be changed to a large value when the related information is referred to, and the value may be decreased in a constant cycle. In this case, the larger the value of the degree of knownness is, the more the related information is known to the user. The knowledge state management means 1
With this known record 1a, the degree of understanding of the user regarding the related information for each related species of a plurality of terms is managed. The related information managing means 2 manages related information 2a necessary for helping the user to understand the terms in three groups of terms, related species, and text.

The term extracting means 4 extracts terms 4a from the input original document 3. The information extraction unit 5 acquires the user's understanding level of the term 4a extracted by the term extraction unit 4 from the knowledge state management unit 1 and extracts the related information 2a from the related information management unit 2 according to the understanding level. To do. The document creating unit 6 adds the related information 2a extracted by the information extracting unit 5 to the original document 3 to create an individual document 3a to be provided to the user.

With such a configuration, for example, "C" in which related information should be added to the input original document 3 as necessary.
When the term "PU" is included, the term extraction means 4
First, the term 4a "CPU" is extracted from the original document 3. Then, the information extraction means 5 is replaced by the knowledge state management means 1
From the user, the degree of understanding of the related information of each related species of the “CPU” is acquired. In this example, the definition information has a known degree of “20”, and it is assumed that the related information needs to be added at this degree of understanding. Therefore, the information extraction means 5
Extracts the related information 2 a, which is the definition information of “CPU”, from the related information management means 2. Related information 2a extracted
Is added to the original document 3 by the document creating means 6 to generate the individual document 3a.

In this way, necessary related information can be added according to the user's knowledge state, and the effect of helping each user's understanding can be sufficiently obtained. FIG. 2 is a block diagram of a first example of a document processing apparatus for implementing the present invention. In this example, when a user inputs a first document to be understood, it is automatically converted into a second document that the user can understand.

In FIG. 2, the related information management section 11 is a section for accumulating / managing / searching information (hereinafter referred to as related information) necessary for the user to understand the contents of the document.
Registration of related information managed by the related information management unit 11 /
For deletion / update, the related information creator 17 inputs / outputs related information i
/ F15. The knowledge state management unit 12 is a unit that accumulates / manages information representing the state of knowledge held by the user 18.

The additional information extraction unit 13 is a technical term extraction unit 1
3a and the condition narrowing portion 13b are included. The technical term extraction unit 13a analyzes a document and extracts a technical term from the document. Then, a technical term list is created based on the extracted technical terms. The condition narrowing unit 13b determines, for the term extracted by the technical term extracting unit, information related to the term and a related species of information necessary for the user. In the additional information extraction unit 13 having such a configuration, the related information required for the user to understand the first document is extracted from the related information management unit 11, and the first document and the list of the extracted related information are individually separated. It is sent to the document creation unit 14. The first document is input from the user 18 via the user input / output i / f 16.

The individual document creating section 14 is provided with the additional information extracting section 1
A second document is created from the document and related information sent from No. 3. The user 18 can read the second document through the input / output i / f.

The details of each of the above components will be described below. First, the related information management unit in the example of FIG. 2 will be described. In the related information management unit 11, the related information stores body data indicating the information body, a data type attribute indicating what type of body data the data is, and a character string indicating what the information is about. Key attributes,
It is managed as data consisting of a related species attribute indicating what kind of relationship is related to the target specified by the key part. Data of a data type such as a character string or various figures is stored in the main body data. The data types that can be used in this example are string type, table type, geometric type, mathematical type, image type, and composite type thereof. The type to which the body data belongs is indicated by the data type attribute.

The related species attribute includes "definition information" indicating that the related information is the definition of the object indicated by the key attribute, and "definition information" indicating that the related information is the details of the object indicated by the key attribute. Any one of three values of “detailed information” and “additional information” indicating that the related information is associated with the object indicated by the key attribute is stored.

Then, the related information management unit 11 has the specified key attribute value and related seed attribute value when the extraction of the related information is specified together with the value of the key attribute and the value of the related seed attribute. It has a search function that searches information and retrieves the related information if it exists.

FIG. 3 is a diagram showing an example of related information managed by the related information management unit. Related information is key attribute 11
1, the related species attribute 112, the data type attribute 113, and the body data 114 are managed. In this figure, 6
The type-related information 11a-11f is shown. Note that all the related information shown in this example is a "character string type" data type attribute.

There are two types of related information whose key attribute is "related information", and the related type attribute of the first related information 11a is "definition information" and the related type attribute of the second related information 11b is "detailed information". Is. There are three types of related information whose key attribute is “knowledge state”, the related species attribute of the first related information 11c is “definition information”, the related species attribute of the second related information 11d is “detailed information”, the third The related species attribute of the related information 11e is “incidental information”. There is one type of related information whose key attribute is "additional information", and the related species attribute of the related information 11f is "definition information".

Next, the knowledge state management unit 12 shown in the example of FIG.
Will be described. The knowledge state management unit 12 manages known records that represent information that the user knows.
Each known record consists of a category of information that the user knows, and a degree of knownness (an integer value of 0 to 30) that indicates the degree to which the user knows the information of the category of the information.

The category of information in this example is a key portion holding a character string indicating what the information relates to, and a related seed portion indicating what kind of information the object designated by the key portion has. And consists of. In the related seed part, "definition information" indicating that the information is the definition of the target indicated by the key attribute, and "detailed information" indicating that the information is the details of the target indicated by the key attribute, Any one of three types of values of “incidental information” indicating that the information is associated with the object indicated by the key attribute is stored.

When the instruction to refer to the knowledge state is input together with the character string, the knowledge state management unit 12 outputs a known record having the character string in the key portion. Since there may be a plurality of known records having the same character string in the key part for each related species, if there are a plurality of known records corresponding to the designated character string, all of them are output.

The knowledge state management unit 12 also uses a character string representing a key, a value of a related species, and an update mode (“known”, “unknown”).
If either of the two) is specified, the known record managed is registered / updated.
If the update mode is known, a character string representing the key,
If there is a known record having the value of the related species, the known degree is set to 30. If the corresponding known record does not exist, a known record having the character string representing the key and the value of the related species is created, and the known degree thereof is set to 30.

When the update mode is unknown, if there is a known record having the character string representing the key and the value of the related species, the known degree is set to 0. If the corresponding known record does not exist, a known record having the character string representing the key and the value of the related species is created, and the known degree thereof is set to 0.

The knowledge state management unit 12 also includes a function of decreasing the knownness value of all known records managed by -1 every 24 hours. This is to simulate human characteristics that are forgotten over time. However, the degree of knownness of 0 does not decrease any further.

FIG. 4 is a diagram showing an example of known records managed by the knowledge status management unit 12. Known records are
It is managed by the items of the key part 121, the related seed part 122, and the known degree 123. In this figure, five types of known records 12a to 12e are shown.

There are two types of known records whose key part is "related information", the related seed part of the first known record 12a is "definition information", the degree of knownness is "20", and the second known record 12b. The related species part of is "detailed information", and the degree of knownness is "10". There are three types of known records whose key part is "knowledge state", the related species part of the first known record 12c is "definition information", the degree of known is "25", and the related species of the second known record 12d. The part is "detailed information", the degree of knownness is "20", the related species attribute of the third known record 12e is "incidental information", and the degree of knownness is "0".

Next, the additional information extraction unit 13 will be described. FIG. 5 is a flowchart showing a procedure for extracting related information and updating the knowledge state when the first document is input to the additional information extraction unit.

First, the technical term extracting unit 13a analyzes the first document sent from the user input / output i / f16, and has a noun having a technical meaning in the document (hereinafter referred to as technical term).
To make a list (step S1). Therefore, the technical term extraction unit 13a has a morphological analysis unit that divides sentences in a document and a noun dictionary of general terms. The technical term extraction unit 13a examines the noun extracted as a result of the morphological analysis in the noun dictionary of general terms, and determines that the term that is not there is a technical term.

Next, it is checked whether the technical term list is empty (step S2). If it is empty, the list of the first document and the related information is sent to the document creating unit (step S3), and the process ends. If the technical term list is not empty, one technical term is extracted from the list (step S4). At this time, the extracted technical term is deleted from the list.

Next, with respect to the acquired terms, the condition narrowing unit determines the related species of information necessary for the user to understand the first document (step S5). As a result of the determination of the related species, it is determined whether or not the user has the necessary related information, and if there is the necessary related information, the related species is determined (step S6). As a result of the determination of the related species, if it is determined that the user does not need information of any related species, the process returns to the check of the technical term list (step S2).

When it is determined that the user needs the definition information, the processing for the case where the definition information is necessary, which will be described later, is performed (step S7), and the process returns to the check of the technical term list (step S2). When it is determined that the user needs the detailed information, a process for the detailed information described later is performed (step S8), and the process returns to the check of the technical term list (step S2). When it is determined that the user needs the supplementary information, the processing for the case where the supplementary information is required, which will be described later, is performed (step S9), and the technical term list is checked again (step S2).

FIG. 6 is a flowchart showing the procedure for determining the related species (step S5) performed by the condition narrowing unit. In the related species determination process, first, an instruction to refer to the knowledge state is sent to the knowledge state management unit together with the extracted technical term, and a known record having the technical term in the key section is extracted (step S11). It is determined whether or not there is a known record (step S12), and if there is no known record in the knowledge state management unit, all information regarding the technical term is known to the user, and information on any related species is required. It is determined that there is not (step S19), and the process ends.

When the known record can be extracted, the known degree of the record of which the value of the related seed part is "definition information" is checked (step S13). Here, when the known degree is 15 or less, it is determined that the user needs the definition information (step S14), and the process ends.

When the known degree is higher than 15, it is determined that the definition information of the technical term is known to the user, and the known value of the record of which the value of the related species part in the extracted known records is "detailed information". Check the degree (step S1
5). Here, when the known degree is 15 or less, it is determined that the user needs detailed information (step S1).
6) and end.

When the degree of knownness is larger than 15, it is determined that the detailed information of the technical term is known to the user, and the known value of the record of which the value of the related seed part in the extracted known records is “incidental information” is known. Check the degree (step S1
7). If the known degree is 15 or less, it is determined that the user needs the supplementary information (step S1).
8), end.

If the knownness is greater than 15, all information about the term is known to the user,
Decide that information on any related species is not needed (step S1
9), end.

FIG. 7 is a flow chart showing the procedure of the processing (step S7) when definition information is required. First, using the technical term as a key attribute value, the related information management unit is instructed to take out the related information whose related seed attribute value is "definition information" (step S21). Here, it is determined whether or not the definition information exists (step S22). When the retrieval of the related information fails, the user remains unknown about the technical term, and the technical term is used as a key, and the knowledge state is updated with the related species as “definition information” and the update mode as “unknown”. Is issued to the knowledge state management unit.
As a result, the degree of knownness of the definition information is updated to "0" (step S23).

When the extraction of the related information has succeeded, the related information is added to the related information list (step S24), and then the related term is defined as "definition information" and the update mode is set as "known" with the technical term as a key. The knowledge state management unit is instructed to update the knowledge state. That is, since the definition information is presented to the user, it becomes “known”. As a result, the degree of knownness of the definition information is updated to "30" (step S25).

The processing when the detailed information is required and the incidental information is required is the same procedure as the flowchart of FIG. However, the "definition information" portion becomes "detailed information" and "incidental information", respectively.

Next, the individual document creating section 14 will be described. The individual document creation unit 14 receives the first document and the related information list described later from the additional information extraction unit, and adds the related information to an appropriate position of the first document to generate the second document. .

FIG. 8 is a flow chart showing the procedure for creating the second document in the individual document creating section. First, it is checked whether the related information list is empty (step S3).
1). If the related information list is empty, the second document is complete and the process ends. If the related information list is not empty, the related information is retrieved from the list (step S3).
2). At this time, the retrieved related information is deleted from the list.

Next, in the first document, the position where the same character string as the value of the key attribute of the related information appears (more precisely, the position of the tail end of the matched character string) is checked (step S3).
3). When there are multiple corresponding character strings, the position that appears first is adopted.

Next, the data type attribute of the related information is checked (step S34). If the value of the data type attribute is not the character string type, the body data of the related information is used as the annotation of the position of the character string (step S38).

If the value of the data type attribute is a character string, the value of the related species attribute of the related information is checked (step S3).
6). When the value of the related species attribute is "definition information", the contents of the main body data are put in parentheses, placed at the position detected in step S33 (step S37), and the process returns to the related information list check (step S31).

If the value of the related species attribute is "detailed information", the length of the body data is checked (step S3).
6). If the length of the body data is 20 characters or less,
The contents of the body data are put in parentheses, placed at the position detected in step S33 (step S37), and the process returns to the check of the related information list (step S31). When the length of the body data is more than 20 characters, the body data is used as the annotation of the position (step S38), and the process returns to the check of the related information list.

When the value of the related species attribute is "incidental information", the body data is used as the annotation of the position detected in step S33 (step S38), and the process returns to the related information list check (step S31).

The annotation process of this example (step S3)
In 8), the note symbol is placed at the position where the annotation is placed, placed at the tail of the document together with the note symbol, and the process returns to the check of the related information list.

With the document processing apparatus as described above, for example, when the document 20 in FIG. 9 is the first document, the technical term list 21 as shown in FIG. 10 is created by the technical term list creation processing. . Since the technical term list 21 is not empty, the first technical term “related information” is retrieved. Here, a case will be described as an example where the related information management unit manages the related information shown in FIG. 3 and the knowledge state management unit manages the knowledge state shown in FIG.

Next, a known record having "related information" in the key part is taken out. In this case, known records 12a and 12b having "related information" in the key part are extracted from the known records shown in FIG. FIG. 11 is a diagram showing the two known records 12a and 12b that have been taken out. Of the retrieved known records, the known degree of the known record 12a having the related seed portion value of “definition information” is greater than 15, and the known record 1 having the related seed portion value of “detailed information”.
Since the known degree of 2b is 15 or less, it is determined that the user needs "detailed information" of the term "related information".

Then, the related information management unit is instructed to take out the related information whose key attribute value is "related information" and related seed attribute value is "detailed information". Related information whose key attribute value is "related information" and related species attribute value is "detailed information" is extracted from the related information shown in FIG. FIG. 12 is a diagram showing the extracted related information 11b.

After adding the related information shown in FIG. 12 to the related information list, an instruction to update the knowledge state in which the "related information" is used as a key, the related species is "detailed information", and the update mode is "known" Is performed to the knowledge state management unit. Then, the process returns to the check of the technical term list.

Such processing is performed for all the technical terms in the technical term list. As a result, the related information list is completed. FIG. 13 shows the created related information list 22.
FIG. In FIG. 13, the related information 11b whose key attribute is "related information" and related species attribute is "detailed information"
And the related information 11e whose key attribute is "knowledge state" and related species attribute is "incidental information" are registered. At this time, the knowledge status managed by the knowledge status management unit is updated. FIG. 14 is a diagram showing the updated knowledge state. FIG. 14 is updated from the knowledge state of FIG. What has been updated is a known record 12f in which the key portion is "related information" and the related seed portion is "detailed information", and a known record 12 in which the key portion is "knowledge state" and the related seed portion is "incidental information".
g. The known degree of the known record 12f is changed from “10” to “30”. The known record 12g is
The known degree is changed from “0” to “30”.

The first document 20 is shown in FIG.
When the related information list is as shown in FIG. 13, the second document 23 as shown in FIG. 15 is created by the document creating section (the underlined part is the part added to the first document). Is).

In the document creating section, the second document can be created as hypertext by connecting the first document and the related information extracted by the additional information extracting section with a reference link.

FIG. 16 is a flowchart showing the procedure for creating the second document as hypertext. First, it is determined whether or not the related information list is empty (step S41), and if it is empty, the process is terminated. If it is determined that it is not empty, the related information in the related information list is selected and the selected related information is deleted from the list (step S42). Next, a character string that matches the value of the key attribute is searched (step S43). A reference link is provided from the matched character string to related information (step S43),
The process returns to step S41.

For example, in the reference link, an identifier is added to the extracted related information, and the position of the character string in the first document (for example, starting from the 21st byte from the beginning of the first document to the 28th byte) End, etc.)
And may be expressed as a pair of the identifiers.

When the first document is as shown in FIG. 9 and the related information is as shown in FIG. 13, the hypertext as shown in FIG. 17 is created by the document creating section. In this example, the document 24 in which the character strings 24a and 24b linked to the related information in the first document are underlined is created. This document 24 is displayed on the screen. When the user specifies one of the character strings 24a and 24b using a pointing device or the like, the related information 25 and 26 linked to the specified character string is displayed on the screen.

As described above, the knowledge state of the user is simulated by the knowledge state management section, and the terms necessary for the user to understand the document based on the contents of the knowledge state management section. , Related information is provided to the user according to the understanding level of the user. Therefore, the user can obtain, together with the document, an appropriate type of information that assists in understanding the document, according to the state of knowledge of the user. This alleviates the trouble for the user of the document to obtain the information necessary to understand the document. On the other hand, the document creator is relieved from the trouble of preparing a plurality of documents and incorporating a lot of information into the documents.

Next, in addition to the above document processing apparatus, an invention of a document processing apparatus capable of converting a document while continuously reflecting the true knowledge state of the user will be described.
FIG. 18 is a block diagram of the second example of the document processing apparatus for carrying out the present invention. This is an apparatus that, when a user inputs a first document to be understood, it is automatically converted into a second document that the user can understand.

In FIG. 18, the related information management section 31
Information necessary for the user to understand the contents of the document (hereinafter,
This is a part for accumulating / managing / searching (referred to as related information). Registration / deletion / update of the related information managed by the related information management unit 31 can be performed by the related information creator 38 through the related information input / output i / f 36. The knowledge state management unit 32 is a unit that accumulates / manages information representing the state of knowledge possessed by the user 39.

The additional information extraction unit 33 is the technical term extraction unit 3
3a and the 1st condition determination part 33b are included. The technical term extraction unit 33a analyzes a document and extracts a technical term from the document. Then, a technical term list is created based on the extracted technical terms. The first condition determining unit 33b determines, for the term extracted by the technical term extracting unit, information related to the term and a related species of information necessary for the user. In the additional information extraction unit 33 having such a configuration, the related information required for the user to understand the first document is extracted from the related information management unit 31, and the first document and the list of the extracted related information are individually separated. It is sent to the document creation unit 35. The first document is input from the user 39 through the user input / output i / f 37. The second condition deciding unit 33c, for a term input as an unknown term from the user 39, is information about the term,
Determine the relevant species of information needed by the user.

The individual document creating section 35 is the additional information extracting section 3
A second document is created from the document and related information sent from No. 3. The user 39 can read the second document through the user input / output i / f 37. Processing content storage unit 3
4 stores the first document and what related information is extracted for the document.

The details of each of the above components will be described below. The related information management unit 31, the knowledge status management unit 32, and the individual document creation unit 35 have the same functions as the related information management unit 11, the knowledge status management unit 12, and the individual document creation unit 14 shown in FIG. 2, respectively. Therefore, the description is omitted here.

The additional information extraction unit 33 will be described. FIG. 19 is a flowchart showing a procedure of extracting related information and updating the knowledge state when the first document is input to the additional information extraction unit.

First, the technical term extracting section 33a analyzes the first document sent from the user input / output i / f 37, and has a noun having a technical meaning in the document (hereinafter referred to as technical term).
Is created (step S51). Therefore, the technical term extraction unit 33a has a morphological analysis unit that divides sentences in a document and a noun dictionary of general terms. The technical term extraction unit 33a examines the noun extracted as a result of the morphological analysis in the noun dictionary of general terms, and determines that the term that is not there is a technical term.

Next, it is checked whether the technical term list is empty (step S52). If it is empty, the first document and the related information list are stored in the processing content storage unit (step S53), the list of the first document and the related information is sent to the individual document creation unit (step S54), and the process ends. .

If the technical term list is not empty, one technical term is extracted from the list (step S55). At this time, the extracted technical term is deleted from the list. Next, with respect to the term, the first condition determining unit 33b determines the related species of information necessary for the user to understand the first document (step S56). As a result of the determination of the related species, it is determined whether or not the user has the necessary related information, and if there is the necessary related information, the related species is determined (step S57).
As a result of the determination of the related species, if it is determined that the user does not need information of any related species, the process returns to the check of the technical term list (step S52).

When it is determined that the user needs the definition information, the processing for the case where the definition information is necessary, which will be described later, is performed (step S58), and the process returns to the check of the technical term list (step S52). When it is determined that the user needs the detailed information, the process for the detailed information described below is performed (step S59), and the process returns to the check of the technical term list (step S52). When it is determined that the user needs the supplementary information, the processing for the case where the supplementary information is necessary is performed (step S6).
0) Return to checking the technical term list (step S)
52).

The function of the first condition determining section is the same as that of the condition narrowing section 13b of the example shown in FIG. Therefore,
The procedure of determining the related species performed by the first condition determining unit is shown in FIG.
Is the same as that shown in. The processing procedure when definition information is required is also the same as the flowchart shown in FIG. 7. Further, the processing when the detailed information is necessary and the incidental information is necessary is the same procedure as the flowchart of FIG. 7. However, the "definition information" portion becomes "detailed information" and "incidental information", respectively.

Here, it is assumed that the document of FIG. 9 is the first document and the document of FIG. 15 is the second document, for example. At this time, the user can input the evaluation (evaluation information) for the second document through the user input / output i / f after reading the second document through the user input / output i / f. . Based on the evaluation information, necessary related information is extracted again, and the knowledge state is updated to reflect the evaluation information. The evaluation information is input to the additional information extraction unit in the form of a list of terms that the user could not understand in the second document (called an unknown term list).

FIG. 20 is a flow chart showing the procedure for extracting the related information and updating the knowledge state when the unknown term list is input. When the unknown term list is input to the additional information extraction unit 33, it is first checked whether or not the unknown term list is empty (step S61). If it is empty, the first document and the related information list are extracted from the processing content storage unit (step S62), and the extracted related information list and the newly extracted related information list are merged to create a new related information list. Yes (step S63). After that, the first document and the new related information list are stored in the processing content storage unit (step S64), the first document and the new related information list are sent to the individual document creating unit, and the process is completed ( Step S65).

If the unknown term list is not empty, one unknown term is taken out from the list (step S66). The retrieved term is removed from the list. Then, for the term, the second condition determining unit determines the related species of information required by the user (step S67). As a result of the determination of the related species, the necessary related species are determined (step S68), and if it is determined that the user does not need information of any related species, the process returns to the unknown term list check (step S61).

When it is determined that the user needs the definition information, the process shown in the flowchart of FIG. 7 for the case where the definition information is necessary is performed (step S69), and the unknown term list is checked ( Return to step S61). When it is determined that the user needs the detailed information, the same process as the process shown in the flowchart of FIG. 7 is performed when the user needs the detailed information (step S70), and the unknown term list is checked. Return to (step S61). When it is determined that the user needs the additional information, the same processing as the processing shown in the flowchart of FIG. 7 is performed when the user needs the additional information (step S71), and the unknown term list is checked. Return to (step S61).

FIG. 21 is a flow chart showing the procedure for determining the related species performed by the second condition determining section.
In the related species determination process, first, an instruction to refer to the knowledge state is sent to the knowledge state management unit together with the retrieved term, and a known record having the term in the key portion is retrieved (step S
81). It is determined whether or not a known record exists in the knowledge state management unit (step S82), and if the known record does not exist, it is determined that the user needs the definition information (step S83), and the process ends.

When the known record can be extracted, the known degree of the record in which the value of the related seed part is "definition information" is checked (step S84). Here, when the known degree is 15 or less, it is determined that the user needs the definition information (step S83), and the process ends.

When the known degree is higher than 15, it is determined that the definition information of the term is known to the user, and the known degree of the record in which the value of the related seed portion of the retrieved known records is “detailed information”. Is checked (step S85). Here, when the known degree is 15 or less, it is determined that the user needs detailed information (step S86), and the process ends.

When the known degree is higher than 15, it is judged that the detailed information of the term is known to the user, and the known degree of the record of which the value of the related seed part of the extracted known records is “incidental information”. Is checked (step S87). Here, when the known degree is 15 or less, it is determined that the user needs the supplementary information (step S88), and the process ends.

If the known degree is higher than 15, definition information,
Of the detailed information and the incidental information, the related species of the known record having the smallest known degree is checked (step S89). If the definition information has the lowest known degree, it is determined that the user needs the definition information (step S83), and the process ends. If the smallest known degree is the detailed information, it is determined that the user needs the detailed information (step S86), and the process ends. If the incidental information has the smallest known degree, it is determined that the incidental information is necessary for the user (step S88), and the process ends.

Here, the operation of the evaluation information reflecting section will be described by taking as an example the case where “knowledge state” and “additional information” are input as unknown terms. FIG. 22 is a diagram showing the input unknown term list 40. "Knowledge state" as an unknown term
And "additional information" are listed. At this time, the knowledge state managed by the knowledge state management unit is shown in FIG. 14, the first document stored in the processing content storage unit is shown in FIG. 9, and the related information list is shown in FIG. 13 is shown.

First, "knowledge state" is selected from the unknown term list. Next, known record 1 about "knowledge state"
2c, 12d, 12g (shown in FIG. 14) are retrieved from the knowledge state management unit. Known record 12 retrieved
Since c, 12d, and 12g all have a known degree greater than 15, it is determined that the user needs detailed information with the smallest known degree. Then, "knowledge status" is used as a key attribute value, related information whose related seed attribute value is "detailed information" is extracted and added to the related information list, and further, "knowledge status" is used as a key The detailed state ”and the instruction to update the knowledge state with the update mode set to“ known ”are issued to the knowledge state management unit.

Next, "additional information" is selected from the unknown term list. Although the known record regarding the "additional information" is retrieved from the knowledge state management unit, there is no known record regarding the "additional information", so it is determined that the definition information is necessary for the user. Then, "additional information" is used as the key attribute value,
The related information whose related seed attribute value is “definition information” is extracted and added to the related information list. FIG. 23 is a diagram showing the created related information list 41. In FIG. 23, the related information 11d in which the key attribute is “knowledge state” and the related species attribute is “detailed information” and the related information 11f in which the key attribute is “additional information” and the related species attribute is “definition information” are shown. It is registered.

Further, with the "additional information" as a key, the knowledge state management section is instructed to update the knowledge state in which the related species is "definition information" and the update mode is "known". FIG. 24 is a diagram showing the updated knowledge state. 24 is updated from the knowledge state of FIG. In this example, a known record 12h having a key part of "additional information" and a related seed part of "definition information" is newly added to FIG. 14 in a state of known degree "30".

Since the unknown term list has become empty, the first document 20 (shown in FIG. 9) and the previously extracted old related information list 22 (shown in FIG. 13) are retrieved from the processing content storage section, The new related information list 41 and the old related information list 22 are merged to create another related information list. FIG. 25 is a diagram showing the created related information list. In the related information list 42, the two related information 11b and 11e registered in the old related information list 22 are registered.
After that, the two related information 11d and 11f registered in the related information list 41 are registered.

Next, the first document 20 and the related information list 42 shown in FIG. 25 are stored in the processing content storage unit and further sent to the document creation unit. The document creation unit creates a document from the first document 20 and the related information list 42. FIG. 26 is a diagram showing the created second document 43. The underlined portion in FIG. 26 is the extracted related information.

In this way, the extracted related information and the state of the user's understanding of the document are input as unknown terms, and by using the unknown terms, what kind of information is further used for the user to understand the document. Is determined, and relevant information that matches the determined condition is extracted. At the same time, the knowledge status in the knowledge status management unit regarding the information corresponding to the determined condition is changed.

Therefore, since it is possible to receive the input of the state of understanding from the user and extract the related information reflecting it, the relation due to the difference between the simulated knowledge state and the actual knowledge state on the computer can be extracted. The lack of information can be supplemented. Further, at this time, since the state of knowledge simulated on the computer is changed, the state of knowledge on the computer is always kept close to the state of actual knowledge of the user. Moreover, since it occurs in association with the action taken by the user to extract the relevant information necessary to understand the document, the user can change the simulated state of knowledge into the actual state of knowledge. No extra load to keep things close.

[0101]

As described above, in the present invention, the user's understanding of terms is managed by the knowledge status management means, and when an original document is input, based on the contents of the knowledge status management means, Since the related information for a given term in the original document is extracted, the user must obtain the original document and appropriate related information to assist understanding of the original document according to the state of knowledge of the user. You can As a result,
The user of the document is relieved of the trouble of obtaining the information necessary for understanding the document, and the document creator is freed from the trouble of preparing a plurality of documents and including a lot of information in the document.

Further, since the individual document is created for the original document by supplementing the related document according to the user's degree of comprehension regarding terms appearing in the original document, the user can assist the understanding of the document. It is possible to obtain a new document supplemented with the appropriate type of information. As a result, the user of the document is relieved of the trouble of obtaining the information necessary to understand the document, and the document creator is free from the trouble of preparing multiple documents and including a lot of information in the document. To be done.

Further, since the knowledge status management means changes the understanding level of the term when the related information of the term is extracted from the related information management means, the knowledge level of the user is always appropriate. It can be kept at the value.

When an unknown term is input, the related information is extracted according to the degree of understanding of the user with respect to the unknown term. Therefore, the related information reflecting the degree of understanding of the user can be extracted. It is possible to compensate for the lack of related information due to the deviation between the knowledge state managed by the knowledge state management means and the actual knowledge state. At this time,
By changing the knowledge status of the knowledge status management means,
The state of knowledge on the computer is always kept close to the actual state of knowledge of the user. Moreover, since it occurs in association with the action taken by the user to extract the relevant information needed to understand the document, the user is given the simulated state of knowledge as the actual state of knowledge. No extra load to keep things close to.

When an original document is input, the original document and the related information extracted by the information extracting unit at that time are stored in the storage unit, and when the unknown term is input, Since the individual document is created by adding the related information for the unknown term and the related information extracted by the information extraction means to the original document, the input of the understanding state from the user is accepted and the document reflecting it is created. Therefore, the user can obtain the document in which the contents of the level suitable for the state of his / her knowledge are described.

[Brief description of drawings]

FIG. 1 is a principle configuration diagram of the present invention.

FIG. 2 is a configuration diagram of a first example of a document processing apparatus for implementing the present invention.

FIG. 3 is a diagram showing an example of related information managed by a related information management unit.

FIG. 4 is a diagram showing an example of known records managed by a knowledge state management unit.

FIG. 5 is a flowchart showing a procedure of extracting related information and updating a knowledge state when the first document is input to the additional information extracting unit.

FIG. 6 is a flowchart showing a procedure of determining a related species (step S5) performed by a condition narrowing unit.

FIG. 7 is a flowchart showing a procedure of processing (step 7) when definition information is required.

FIG. 8 is a flowchart showing a procedure of creating a second document in the individual document creating unit.

FIG. 9 is a diagram showing an example of an input document.

FIG. 10 is a diagram showing a created technical term list.

FIG. 11 is a diagram showing two known records extracted.

FIG. 12 is a diagram showing extracted related information.

FIG. 13 is a diagram showing a created related information list.

FIG. 14 is a diagram showing an updated knowledge state.

FIG. 15 is a diagram showing a created second document.

FIG. 16 is a flowchart showing a procedure for creating a second document as hypertext.

FIG. 17 is a diagram showing a second document associated by hypertext.

FIG. 18 is a second document processing apparatus for implementing the present invention.
FIG. 3 is a configuration diagram of an example of FIG.

FIG. 19 is a flowchart showing a procedure of extracting related information and updating a knowledge state when the first document is input to the additional information extracting unit.

FIG. 20 is a flowchart showing a procedure of extracting related information and updating a knowledge state when an unknown term list is input.

FIG. 21 is a flowchart showing a procedure of determining a related species performed by a second condition determining unit.

FIG. 22 is a diagram showing an input unknown term list.

FIG. 23 is a diagram showing a created related information list.

FIG. 24 is a diagram showing an updated knowledge state.

FIG. 25 is a diagram showing a created related information list.

FIG. 26 is a diagram showing a created second document.

[Explanation of symbols]

 1 Knowledge State Management Means 1a Known Records 2 Related Information Management Means 2a Related Information 3 Original Documents 3a Individual Documents 4 Term Extraction Means 4a Terms 5 Information Extraction Means 6 Document Creation Means 11 Related Information Management Department 12 Knowledge State Management Department 13 Additional Information Extraction Part 13a Technical term extraction part 13b Condition narrowing part 14 Individual document creation part 15 Related information input / output i / f 16 User input / output i / f

Claims (5)

[Claims] 1. In a document processing apparatus for converting an original document and providing it to a user, a related information managing means for managing a plurality of related information for a term in association with a related type, and an understanding of the user for the term. From the knowledge state management unit, the knowledge state management unit that manages the degree of understanding, the term extraction unit that extracts a predetermined term from the input original document, and the degree of understanding of the user with respect to the term extracted by the term extraction unit. A document processing apparatus, comprising: an information extraction unit that acquires and extracts, from the related information management unit, related information of a related type according to an understanding level. 2. The document creating means for creating an individual document to be provided to a user from the original document and the related information extracted by the information extracting means is further included. Document processing device. 3. The knowledge status management unit, when the related information is extracted from the related information management unit, changes the understanding level of the user with respect to the extracted related information to thereby obtain the understanding level of the user. The document processing apparatus according to claim 1, wherein the document processing apparatus manages the document. 4. When an unknown term is input, the degree of comprehension of the user with respect to the unknown term is acquired from the knowledge status management means, and relevant information corresponding to the degree of comprehension of the unknown term is extracted from the relevant information management means. The document processing apparatus according to claim 1, further comprising unknown term information extraction means for performing the unknown term information extraction. 5. A storage unit for storing the original document and the related information extracted by the information extracting unit, and a relation extracted by the original document and the information extracting unit from the storage unit when an unknown term is input. Take out information and
The unknown information added document creating means for creating the individual document to be provided to the user by adding the related information extracted by the unknown term information extracting means to the extracted information, further comprising: Document processing device described.