JP5348786B2 - Sentence search program, server and method using non-search keyword dictionary for search keyword dictionary - Google Patents

Description

  The present invention relates to a text search program, server, and method using a search keyword dictionary.

  With the spread of the Internet, various texts are described on blogs, bulletin boards, or Web sites that publish reviews. A “blog” is a site that is generally run by an individual and can be updated in a diary to express their opinions on current news and specialized topics. A “bulletin board” is a site for sequentially exchanging discussions with other people on various themes. Furthermore, “review comments” refers to a site where comments about things such as people's rumors can be described. With the spread of these sites, general users can freely send information on the Internet.

  Along with this, it is a social problem that these websites contain text that slanders and slanders individuals and organizations, or illegal and harmful text information such as crime notices, crime assistants, or adult sites. It has become. Therefore, it is necessary to detect sentences and individuals that describe these illegal and harmful sentences while giving consideration to freedom of expression.

  Conventionally, there is a filtering technique for denying access from a terminal to a site describing illegal / harmful text (see, for example, Patent Document 1). According to this technique, a keyword or URL (Uniform Resource Locator) is registered in advance, and access from a terminal to a Web site including the keyword or URL is denied. However, it is necessary to constantly update keywords and URLs registered in advance, which requires time and cost.

  On the other hand, there is a technique for automatically extracting and filtering keywords and URLs (see, for example, Patent Document 2). According to this technology, keywords can be automatically extracted from illegal / harmful text information such as adult sites and crime notice sites. By registering these keywords in advance, illegality / harmfulness is determined depending on whether or not the text information to be analyzed contains those keywords.

JP 2007-128119 A JP 2009-37420 A

Nagaoka University of Technology, Natural Language Processing Laboratory, “CaboCha”, [online], [Search on December 28, 2009], Internet <URL: http://nlp.nagaokaut.ac.jp/CaboCha> Department of Intelligent Informatics, Graduate School of Informatics, Kyoto University, Kurohashi Laboratory, "Japanese Parsing System KNP", [online], [Search on December 28, 2009], Internet <URL: http: // nlp .kuee.kyoto-u.ac.jp / nl-resource / knp.html>

  However, when a general keyword is registered as belonging to an illegal / harmful category, there is a problem that a sentence that is not illegal / harmful is picked up as illegal / harmful text. For example, if you simply use keyword search technology, if a general word such as “kill” is registered as an illegal / harmful keyword, the expression “kill your breath” or “no kill” Will be detected as illegal and harmful text information. As a result, the classification accuracy into the illegal / harmful category is lowered, and the illegal / hazardous is determined by visual inspection.

  Therefore, the present invention, for example, when determining whether or not a pre-registered keyword belongs to an illegal / harmful category, as long as it is possible to classify text information that is not illegal / harmful into an illegal / harmful category. It is an object to provide a sentence classification program, a server, and a method that can be reduced.

According to the present invention, there is provided a text search program having search keyword dictionary means for storing search keywords, and causing a computer to function to search text information including the search keywords,
A learning sentence storage means for storing a plurality of legitimate learning sentence information not belonging to a specific category and a plurality of illegal learning sentence information belonging to a specific category;
Learning sentence keyword search means for searching learning sentence information including a search keyword using learning sentence storage means;
A learning dependency analysis means for extracting dependency keywords for the search keyword for learning sentence information including the search keyword;
Validity ratio calculating means for counting the number of legitimate learning sentence information and the number of illegal learning sentence information for each dependency keyword, and calculating a legitimate ratio of the number of legitimate learning sentence information with respect to the number of all learning sentence information; ,
The computer functions as a non-search keyword dictionary means for registering a dependency keyword having a valid ratio equal to or greater than a predetermined threshold as a non-search keyword so that sentence information including the non-search keyword as a dependency keyword for the search keyword is not searched. It is characterized by.

According to another embodiment of the text search program of the present invention,
A target sentence input means for inputting target sentence information to be analyzed;
Target text keyword search means for determining whether the target text information includes a search keyword stored in the search keyword dictionary means;
A target dependency analysis means for extracting a dependency keyword for the search keyword for the target sentence information including the search keyword;
Non-search keyword determining means for determining whether or not the extracted dependency keyword matches the non-search dependency keyword registered in the non-search keyword dictionary means;
The target sentence information determined to be false by the target sentence keyword search means and the target sentence information determined to be true by the target sentence keyword search means and determined to be true by the non-search keyword determination means do not belong to a specific category. It is also preferable to cause the computer to function as text information classification means for classifying text information.

According to another embodiment of the text search program of the present invention,
The non-search keyword dictionary means registers the dependency ratio calculated by the validity ratio calculation means in association with each registered dependency keyword,
The non-search keyword determining means determines that the extracted dependency keyword matches the dependency keyword registered in the non-search keyword dictionary means, and the valid ratio in the dependency keyword is equal to or greater than a predetermined threshold. It is also preferable to determine that the dependency keyword is true.

According to another embodiment of the text search program of the present invention,
Search keywords are illegal and harmful keywords,
It is also preferable to make the computer function so that the specific category in the learning sentence storage means is a group of illegal and harmful sentence information.

According to another embodiment of the text search program of the present invention,
Search keywords are keywords specified by the user,
It is also preferable to make the computer function so that the specific category in the learning sentence storage means is a group of sentence information designated by the user.

  According to another embodiment of the text search program of the present invention, the target text information is text described by an unspecified number of users in a blog (Weblog), bulletin board, and / or word-of-mouth comment published via a network. Information is also preferred.

According to the present invention, there is provided a text analysis server having search keyword dictionary means for storing search keywords and searching text information including the search keyword from text information acquired from another public server via a network. ,
A learning sentence storage means for storing a plurality of legitimate learning sentence information not belonging to a specific category and a plurality of illegal learning sentence information belonging to a specific category;
Learning sentence keyword search means for searching learning sentence information including a search keyword using learning sentence storage means;
A learning dependency analysis means for extracting dependency keywords for the search keyword for learning sentence information including the search keyword;
Validity ratio calculating means for counting the number of legitimate learning sentence information and the number of illegal learning sentence information for each dependency keyword, and calculating a legitimate ratio of the number of legitimate learning sentence information with respect to the number of all learning sentence information; ,
The computer functions as a non-search keyword dictionary means for registering a dependency keyword having a valid ratio equal to or greater than a predetermined threshold as a non-search keyword so that sentence information including the non-search keyword as a dependency keyword for the search keyword is not searched. It is characterized by.

According to another embodiment of the text information analysis server of the present invention,
A target sentence input means for inputting target sentence information to be analyzed from another public server via a network;
Target text keyword search means for determining whether the target text information includes a search keyword stored in the search keyword dictionary means;
A target dependency analysis means for extracting a dependency keyword for the search keyword for the target sentence information including the search keyword;
Non-search keyword determining means for determining whether or not the extracted dependency keyword matches the non-search dependency keyword registered in the non-search keyword dictionary means;
The target sentence information determined to be false by the target sentence keyword search means and the target sentence information determined to be true by the target sentence keyword search means and determined to be true by the non-search keyword determination means do not belong to a specific category. It is also preferable to further include text information classification means for classifying the text information.

According to the present invention, there is provided a search keyword dictionary that stores search keywords, and a text search method for searching text information including the search keywords using a computer,
A plurality of legitimate learning sentence information that does not belong to a specific category and a plurality of illegal learning sentence information that belongs to a specific category are accumulated in the learning sentence accumulation unit,
A first step of searching for learning sentence information including a search keyword using the learning sentence storage unit;
A second step of extracting dependency keywords for the search keyword for learning sentence information including the search keyword;
A third step of counting the number of legitimate learning sentence information and the number of illegal learning sentence information for each dependency keyword, and calculating a legitimate ratio of the number of legitimate learning sentence information to the number of all learning sentence information; ,
And a fourth step of registering a dependency keyword having a valid ratio equal to or greater than a predetermined threshold as a non-search keyword, and text information including the non-search keyword as a dependency keyword for the search keyword is not searched. It is characterized by that.

  According to the sentence classification program, server, and method of the present invention, for example, when determining whether or not a sentence that is illegal or harmful belongs to a keyword registered in advance, the sentence information that is not illegal or harmful is classified as illegal or harmful. Can be reduced as much as possible.

It is a functional block diagram of the text classification program in this invention. It is explanatory drawing showing the production | generation of the non-search keyword in this invention. It is explanatory drawing showing the classification | category of the object text information in this invention. It is a system configuration | structure figure of the text analysis server in this invention. It is a sequence diagram of a system in the present invention.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a functional block diagram of a sentence classification program according to the present invention.

  According to FIG. 1, the sentence classification program is divided into a search keyword dictionary unit 10, a non-search dictionary generation function unit 11, and a target sentence classification function unit 12. The non-search dictionary generation function unit 11 includes a learning text storage unit 110, a learning text keyword search unit 111, a learning dependency analysis unit 112, a correct ratio calculation unit 113, and a non-search keyword dictionary unit 114. The target text classification function unit 12 includes a target text input unit 120, a target text keyword search unit 121, a target dependency analysis unit 122, a non-search keyword determination unit 123, and a text information classification unit 124. These functional units can be realized by executing a program that causes a computer installed in the apparatus to function. The processing flow of these functional components is also understood as a sentence classification method in the apparatus.

  The sentence classification program causes the computer to function so as to classify the target sentence information according to whether or not it belongs to a specific category. Specifically, it is searched whether or not the search text registered in the search keyword dictionary unit 10 is included in the target sentence information. For example, in the case of classification based on whether or not the text information is illegal / harmful, the search keyword stored in the search keyword dictionary unit 10 is an illegal / harmful keyword. For example, in the case of a crime notice, a keyword such as “kill” is stored in the search keyword dictionary unit 10. Of course, the search keyword stored in the search keyword dictionary unit 10 may be a keyword designated by the user. According to the present invention, even text information including a search keyword registered in the search keyword dictionary unit 10 can prevent text information that does not belong to a target specific category from being searched.

[Non-search dictionary generation function]
FIG. 2 is an explanatory diagram showing generation of a non-search keyword in the present invention. In the following, the content of FIG. 2 will be described together with the description of FIG.

The learning sentence accumulating unit 110 accumulates a large number of legitimate learning sentence information that does not belong to the specific category and a large number of inappropriate learning sentence information that belongs to the specific category. The validity / injustice is distinguished by a flag added to the learning sentence information.
For example, when the specific category is a group of illegal / harmful text information, the learning text storage unit 110 stores the following text information group in a mixed manner.
(1) Group of text information that does not belong to illegal or harmful categories
= "Valid" flag is added to each text information
= Group of legitimate learning text information (2) Group of text information belonging to illegal / harmful categories
= "Unfair" flag is added to each text information
= Illegal Learning Text Information Group Of course, when the specific category is a text information group designated by the user, the learning text accumulating unit 110 accumulates the following text information groups together.
(1) A group of text information that does not belong to a specific category specified by the user
= "Valid" flag is added to each text information
= Group of legitimate learning sentence information (2) Group of sentence information belonging to a specific category designated by the user
= "Unfair" flag is added to each text information
= Group of inappropriate learning text information

  The learning text keyword search unit 111 searches the learning text information including the search keyword stored in the search keyword dictionary unit 10 for a large number of learning text information stored in the learning text storage unit 110. According to FIG. 2, all learning text information including the search keyword “kill” is searched regardless of the flag “valid” / “unjust”. A large number of retrieved pieces of learning text information are output to the learning dependency analysis unit 112. Here, since the learning sentence information d does not include the search keyword “kill”, it is not a target of subsequent processing.

  The learning dependency analysis unit 112 extracts a dependency keyword for the search keyword from a large number of pieces of learning text information including the search keyword. The learning dependency analysis unit 112 also includes morphological analysis necessary as a pre-stage of dependency analysis. A “morpheme” refers to the smallest meaningful unit among the constituent elements of a sentence. The morphological analysis has a dictionary in which “part of speech” and “reading” are registered for each “word”. According to the present invention, only one sentence including a search keyword (which may be shorter than one sentence) is divided into morphemes, and grammatical rules of “part of speech” and “reading” are formed using a dictionary, , Analyze the dependency.

  As the dependency analyzer, for example, a conventional CaboCha (for example, see Non-Patent Document 1) or KNP (for example, see Non-Patent Document 2) may be used. “CaboCha” is a typical Japanese dependency analyzer, which uses a definitive analysis algorithm (Cascaded Chunking Model) that does not perform backtracking based on a support vector machine. “KNP” is a Japanese sentence syntactic / case analysis system for Japanese sentences. The morpheme sequence of the morpheme analysis system JUMAN is input, and the dependency relation and case relation between clauses and basic phrases are output. Technology.

According to FIG. 2, the following dependency keywords are extracted for the search keyword “kill”.
Learning text information a: “Unfair” flag added (belongs to illegal / harmful category)
"Kill"<-"People"
Learning text information b: "Legacy" flag added (does not belong to illegal or harmful category)
"Kill"<-"breath"
Learning text information c: "Legacy" flag added (does not belong to illegal or harmful category)
"Kill"<-"No"
As a result, the following keyword set is output to the valid ratio calculation unit 113.
"Kill <-People: Unfair"
"Kill <-Breath: Justified"
“Kill <-No: Justice”

  The correct ratio calculation unit 113 counts the number of correct learning sentence information and the number of illegal learning sentence information for each dependency keyword, and sets the correct ratio of the number of correct learning sentence information to the number of all learning sentence information. calculate. Based on a large amount of learned text information, a large number of valid / invalid dependency keywords for one search keyword are extracted. Then, a legitimate ratio is calculated for each dependency keyword with respect to the search keyword.

According to FIG. 2, the correct ratio is calculated for each dependency keyword as follows.
"Kill"<-"People": Number of legitimate learning text information = 40
Number of inappropriate learning text information = 1960
Legitimate ratio = 40/2000 = 2%
"Kill"<-"breath": Number of legitimate learning text information = 90
Number of illegal learning text information = 10
Legitimate ratio = 90/100 = 90%
"Kill"<-"Nouse": Number of legitimate learning text information = 120
Number of illegal learning text information = 80
Legitimate ratio = 120/200 = 60%
Then, the correct ratio calculation unit 113 outputs the correct ratio for each dependency keyword to the non-search keyword dictionary unit 114.
"Kill"<-"People": 2%
<-"Breath": 90%
<-"No use": 60%

  The non-search keyword dictionary unit 114 registers a dependency keyword whose legal ratio is equal to or greater than a predetermined threshold. According to FIG. 2, it is assumed that the predetermined threshold is set to 70% or more. In this case, for the search keyword “kill”, only the dependency keyword “breath” having a legitimate ratio of 70% or more is stored in the non-search keyword dictionary unit 114 as a non-search keyword that is a dependency on the search keyword “kill”. be registered.

The non-search keyword dictionary unit 114 may store not only the dependency keywords whose legal ratio is equal to or greater than a predetermined threshold value, but may also register the legal ratios in association with every dependency keyword. . Accordingly, the predetermined threshold as a level derived as a non-search keyword can be made variable by user designation.
Search keyword "kill"<-Non-search keyword "people": 2%
<-Non-search keyword "breath": 90%
<-Non-search keyword "No": 60%

[Target sentence classification function section]
FIG. 3 is an explanatory diagram showing classification of target sentence information in the present invention. In the following, the content of FIG. 3 will be described together with the description of FIG.

  The target sentence input unit 120 inputs target sentence information to be analyzed. The target sentence information may be input in response to a user operation, or may be received via a network. The target text information received via the network is, for example, text information described by an unspecified number of users in a publicly available blog, a bulletin board, and / or a review comment.

  The target text keyword search unit 121 determines whether the target text information includes the search keyword stored in the search keyword dictionary unit 10. According to FIG. 3, the search keyword “kill” is included in the sentence “... Therefore, the target sentence information is output to the target dependency analysis unit 122 with the search keyword.

The target dependency analysis unit 122 extracts a dependency keyword for the search keyword from the target sentence information including the search keyword. According to FIG. 3, the dependency keyword “breath” is extracted for the search keyword “kill”.
Target text information: "Kill"<-"Breath"
The extracted dependency keyword “breath” is output to the non-search keyword determination unit 123 together with the target sentence information.

  The non-search keyword determination unit 123 determines whether or not the extracted dependency keyword matches the non-search dependency keyword registered in the non-search keyword dictionary unit 114. According to FIG. 3, “kill” <− “breath” is registered in the non-search keyword dictionary unit 114. Therefore, the non-search keyword determination unit 123 determines that the extracted dependency keyword “breath” matches the non-search dependency keyword “breath”. This determination result is output to the text information classification unit 124.

  Note that the non-search keyword determination unit 123 matches the extracted dependency keyword with the dependency keyword registered in the non-search keyword dictionary unit 114, and the valid ratio of the dependency keyword is equal to or greater than a predetermined threshold. In this case, it may be determined that the dependency keyword is true. For example, with respect to the matching dependency keyword “breath”, since the legal ratio 90% registered in the non-search keyword dictionary unit 114 is equal to or greater than a predetermined threshold 70%, it is determined that it matches the non-search keyword “breath”. Also good. In this case, conversely, non-search keywords with a predetermined threshold value less than 70% are not adopted.

  The text information classification unit 124 includes target text information determined to be false by the target text keyword search unit 121, and target determined to be true by the target text keyword search unit 121 and determined to be true by the non-search keyword determination unit 123. Text information is classified as text information that does not belong to a specific category.

  FIG. 4 is a system configuration diagram of the sentence analysis server in the present invention.

  According to FIG. 4, the sentence analysis server 1 includes the search keyword dictionary unit 10, the non-search dictionary generation function unit 11, the target sentence classification function unit 12, and the communication interface unit 13 described above. The sentence analysis server 1 communicates with the Web server 2 via the Internet. In addition, the Web server 2 accumulates and discloses Web text information as target text information transmitted from the terminal 3. The target sentence classification function unit 12 of the sentence analysis server 1 receives target sentence information from a number of Web servers 2 via the communication interface unit 13. The target sentence classification function unit 12 classifies the target sentence information into a specific category by using the search keyword dictionary and the non-search keyword dictionary generated by the non-search dictionary generation function unit 11. The target sentence information received from the Web server 2 can be classified into, for example, a specific category that is illegal or harmful.

  FIG. 5 is a sequence diagram of the system according to the present invention.

(S501) The non-search dictionary generation function unit 11 stores, as learning text information, a large number of valid learning text information that does not belong to a specific category and a large number of inappropriate learning text information that belongs to a specific category. First, learning sentence information including a search keyword registered in a search keyword dictionary is searched from a large number of learning sentence information.
(S502) About the learning sentence information containing a search keyword, the dependency keyword with respect to a search keyword is extracted.
(S503) For each dependency keyword, the number of legitimate learning sentence information and the number of illegal learning sentence information are counted, and the legitimate ratio of the number of legitimate learning sentence information to the number of all learning sentence information is calculated.
(S504) Then, a dependency keyword whose legitimate ratio is equal to or greater than a predetermined threshold is registered as a non-search keyword.
(S505) The terminal 3 transmits the target text information to the Web server 2 in accordance with a user operation.
(S506) The sentence analysis server 1 receives target sentence information to be analyzed from the Web server 2. The target sentence information is output to the target sentence classification function unit 12.
(S507) It is determined whether the target sentence information includes the search keyword stored in the search keyword dictionary.
(S508) For the target sentence information including the search keyword, a dependency keyword for the search keyword is extracted.
(S509) It is determined whether or not the extracted dependency keyword matches the non-search dependency keyword registered in the non-search keyword dictionary.
(S510) Then, the target sentence information determined to be false by S507 and the target sentence information determined to be true by S507 and determined to be true by S509 are classified as sentence information not belonging to the specific category. Thus, when the dependency keyword for the search keyword is a non-search keyword registered in the non-search keyword dictionary, the sentence information is not searched as not belonging to the specific category.

  As described above in detail, according to the sentence classification program, server, and method of the present invention, it is not illegal / harmful when, for example, it is determined whether or not it belongs to an illegal / harmful category by a keyword registered in advance. It is possible to reduce as much as possible the sentence information being classified into illegal and harmful categories. According to the present invention, in addition to the search keyword dictionary, a non-search keyword dictionary in which non-search keywords having a dependency relationship with the search keyword are registered. As a result, even text information including a general search keyword is determined not to be illegal / harmful text information if the dependency relation keyword matches the non-search search keyword. According to the present invention, since the dependency relationship of search keywords is also analyzed, excessive determination is suppressed and classification accuracy is improved.

  Various changes, modifications, and omissions of the above-described various embodiments of the present invention can be easily made by those skilled in the art. The above description is merely an example, and is not intended to be restrictive. The invention is limited only as defined in the following claims and the equivalents thereto.

DESCRIPTION OF SYMBOLS 1 Text analysis server 10 Search keyword dictionary part 11 Non-search dictionary production | generation function part 110 Learning sentence storage part 111 Learning sentence keyword search part 112 Learning dependency analysis part 113 Validity ratio calculation part 114 Non-search keyword dictionary part 12 Target sentence classification function part DESCRIPTION OF SYMBOLS 120 Target text input part 121 Target text keyword search part 122 Target dependency analysis part 123 Non-search keyword determination part 124 Text information classification part 13 Communication interface part 2 Web server 3 Terminal

Claims (9)

A text search program having search keyword dictionary means for storing search keywords, and causing a computer to function to search text information including the search keywords,
A learning sentence accumulating means for accumulating a plurality of legitimate learning sentence information not belonging to a specific category and a plurality of illegal learning sentence information belonging to the specific category,
Learning sentence keyword search means for searching learning sentence information including the search keyword using the learning sentence storage means;
A learning dependency analysis unit that extracts a dependency keyword for the search keyword for learning sentence information including the search keyword;
For each dependency keyword, the number of legitimate learning sentence information and the number of illegal learning sentence information are counted, and a legitimate ratio of the number of legitimate learning sentence information to the number of all learning sentence information is calculated. A ratio calculation means;
The sentence information including a non-search keyword as a dependency keyword for the search keyword is made to function as a non-search keyword dictionary means for registering the dependency keyword having a valid ratio equal to or greater than a predetermined threshold as a non-search keyword. A text search program that prevents searching. A target sentence input means for inputting target sentence information to be analyzed;
Target text keyword search means for determining whether or not the target text information includes the search keyword stored in the search keyword dictionary means;
For the target sentence information including the search keyword, target dependency analysis means for extracting a dependency keyword for the search keyword;
Non-search keyword determination means for determining whether or not the extracted dependency keyword matches the non-search dependency keyword registered in the non-search keyword dictionary means;
The target sentence information determined to be false by the target sentence keyword search means, and the target sentence information determined to be true by the target sentence keyword search means and determined to be true by the non-search keyword determination means, The text search program according to claim 1, wherein a computer is caused to function as text information classification means for classifying text information that does not belong to the specific category. The non-search keyword dictionary means associates and registers the legitimate ratio calculated by the legitimate ratio calculator for each registered dependency keyword,
The non-search keyword determination unit matches the extracted dependency keyword with the dependency keyword registered in the non-search keyword dictionary unit, and the valid ratio of the dependency keyword is equal to or greater than a predetermined threshold. The sentence search program according to claim 2, wherein the determination is true for the dependency keyword. The search keyword is an illegal or harmful keyword,
The sentence search program according to any one of claims 1 to 3, wherein the computer is caused to function so that the specific category in the learning sentence storage unit is a group of illegal and harmful sentence information. The search keyword is a keyword specified by the user,
The sentence search program according to any one of claims 1 to 3, wherein the computer is caused to function so that the specific category in the learning sentence accumulating unit is a group of sentence information designated by a user.   6. The target sentence information is sentence information described by an unspecified number of users in a blog (Weblog), a bulletin board, and / or a word-of-mouth comment published via a network. The text search program according to any one of the above items. A sentence analysis server that has a search keyword dictionary unit that stores search keywords, and that searches sentence information including the search keyword from sentence information acquired from another public server via a network,
A learning sentence accumulating means for accumulating a plurality of legitimate learning sentence information not belonging to a specific category and a plurality of illegal learning sentence information belonging to the specific category,
Learning sentence keyword search means for searching learning sentence information including the search keyword using the learning sentence storage means;
A learning dependency analysis unit that extracts a dependency keyword for the search keyword for learning sentence information including the search keyword;
For each dependency keyword, the number of legitimate learning sentence information and the number of illegal learning sentence information are counted, and a legitimate ratio of the number of legitimate learning sentence information to the number of all learning sentence information is calculated. A ratio calculation means;
The sentence information including a non-search keyword as a dependency keyword for the search keyword is made to function as a non-search keyword dictionary means for registering the dependency keyword having a valid ratio equal to or greater than a predetermined threshold as a non-search keyword. A sentence analysis server characterized by not being searched. A target sentence input means for inputting target sentence information to be analyzed from another public server via a network;
Target text keyword search means for determining whether or not the target text information includes the search keyword stored in the search keyword dictionary means;
For the target sentence information including the search keyword, target dependency analysis means for extracting a dependency keyword for the search keyword;
Non-search keyword determination means for determining whether or not the extracted dependency keyword matches the non-search dependency keyword registered in the non-search keyword dictionary means;
The target sentence information determined to be false by the target sentence keyword search means, and the target sentence information determined to be true by the target sentence keyword search means and determined to be true by the non-search keyword determination means, The sentence analysis server according to claim 7, further comprising sentence information classification means for classifying the sentence information as not belonging to the specific category. A text search method having a search keyword dictionary storing search keywords and searching text information including the search keyword using a computer,
A plurality of legitimate learning sentence information that does not belong to a specific category and a plurality of illegal learning sentence information that belongs to the specific category are accumulated in the learning sentence accumulation unit,
A first step of searching for learning sentence information including the search keyword using the learning sentence storage unit;
A second step of extracting a dependency keyword for the search keyword for learning sentence information including the search keyword;
For each dependency keyword, the number of the legitimate learning sentence information and the number of the illegal learning sentence information are counted, and a legitimate ratio of the number of the legitimate learning sentence information to the number of all the learning sentence information is calculated. 3 steps,
And a fourth step of registering the dependency keyword with the legitimate ratio equal to or higher than a predetermined threshold as a non-search keyword, and text information including the non-search keyword as a dependency keyword for the search keyword is not searched. A text search method characterized by:

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