TW202314581A - Method and system of screening for text data relevance - Google Patents

Abstract

Abstract of Invention Method and system of screening for text data relevance A method and system of screening for text data relevance, which can be used to perform word segmentation processing, screening processing, relevance processing and integration processing on multiple text data comparison (such as theses, etc.) and forms a relevance index file based on the adjacent screening of word segmentation, so as to quickly sort out a brief information of the text data comparison, and further analyze the originality of the text data to be compared based on the relevance index file.

Description

Method and system for filtering and linking text data

The present invention relates to a method and system for screening and association of textual data; in particular, it uses a method and system based on the screening and segmentation of words adjacent to each other, which can quickly organize and analyze textual data, and can analyze the originality of the textual data to be compared against the textual data A method and system for screening and linking text data.

In recent years, the plagiarism incidents of papers have emerged one after another, and the public has begun to have doubts about the originality of papers. Although there are many detection systems for plagiarism comparison of papers and articles on the market, most of these systems are aimed at copyright holders who publish research papers. It is unfair to the copyright owner to carry out plagiarism comparison detection under the circumstance of adopting a skeptical attitude. In addition, some units even require the copyright owner to submit the plagiarism comparison results first, and require the degree of similarity to be below a certain percentage before the copyright owner of the paper can publish it directly. Therefore, the copyright owner needs to use this method to prove that his document has not plagiarized others. It is very inappropriate to take an attitude of distrust towards the copyright owner. The inventor believes that it is necessary to think backwards and provide a tool for detecting originality in the publication of copyright holders' papers, as a reference for the publication of their papers, and the publishing unit can formulate an originality ratio as a reference for the quality management of papers.

Regarding the plagiarism comparison system, in recent years, in academic research, the issue of plagiarism has become more and more serious. Plagiarism Detection is getting more and more attention. Plagiarism issues are mainly divided into the following categories: 1. Copy/paste/clone plagiarism without modification. 2. Paraphrasing plagiarism: by plagiarizing passages, switching vocabulary, or rewriting sentence structure or grammatical style. 3. Metaphor plagiarism (Metaphor plagiarism): Through clarity, better express the ideas of others. 4. Idea plagiarism: Ideas or solutions are borrowed from other sources as your own research paper. 5. Self-plagiarism (Self/recycled plagiarism): Use your own published articles and publish them again as new research results. 6. Citing plagiarism: Citing references from appropriate sources, but their descriptions are similar to the original content in terms of words, sentences, and even structural grammar.

Among these types of plagiarism, "unmodified copy and paste or fragment plagiarism" and "paragraph rewriting" are the most concerned. These two plagiarism methods can be clearly identified by comparing the paper with the plagiarized documents. Plagiarism can be seen, so the two are the most criticized.

In 1995, some scholars conducted research. The paper carried out copy detection on digital files. With the evolution of natural language processing and hardware equipment, many different methods have been introduced in recent years. In the field of plagiarism detection , mainly divided into several methods: 1. Character-Based Methods: This method is the most common method for plagiarism detection in papers. The papers to be compared are compared with the existing paper database, and by searching for matching strings , and then determine the proportion of plagiarism in the paper, and therefore can tell the system user, plagiarized paragraphs and sentences. Shrestha and Solorio published in 2013, by combining stop words, named entities, and all words with The method of n-grams detects plagiarism by considering whether there is an article with a high degree of n-gram matching between the detection paper and the text database article. Nguyen et al. proposed in 2016 to detect whether Vietnamese articles are plagiarized through plagiarism detection. This method uses the substring n-gram method. This type of method has the following three defects: 1. If there are words in the paper that are not in the paper database, similar sentences cannot be compared, so plagiarized papers cannot be detected; 2. Users can change the vocabulary or Swapping the order of words, thereby avoiding the detection method of this method, resulting in no similar words and sentences being detected; 3. Since this method is to compare strings, if the length of the input string is too long, it is easy to dilute the input paper, thereby reducing plagiarism similarity. 2. Vector-Based Methods: This method extracts lexical and grammatical features and classifies them as vectors rather than strings. This similarity is usually measured by methods such as Jaccard coefficient, Dice coefficient, Overlap coefficient or Cosine Similarity to measure the similarity between papers and papers degree. Mahdavi et al published a vector space model to detect plagiarism of Persian articles, and compared the similarity of articles by converting the articles to TF-IDF. Jiffriya et al. proposed in 2013 to convert articles into vectors and then use the K-means algorithm to group them. After grouping, the articles were used for plagiarism detection based on tri-grams. The disadvantage of this method is that the importance of a word in an article is measured by word frequency. Sometimes important words may not appear many times, which will lead to poor comparison results, and this calculation cannot reflect the location information and The importance of words in context. 3. Syntax-Based Methods (Syntax-Based Methods): This method uses syntactic features Signs are parts of speech, dependency trees of sentences and words in different statements to detect plagiarism, use parts of speech to represent word structure and calculate similarity. This method can find paragraphs with similar sentence structure, but cannot find the plagiarism of paragraph rewriting, word extraction and sentence structure conversion. There are several defects in the grammar-based method. First, Chinese grammar is much more complex than English grammar. If our Chinese plagiarism system detects plagiarism by grammar, the comparison results will be extremely poor; second, this method Detecting plagiarized content through syntactic features will lead to finding similar syntactic features, but no plagiarized text, only the same syntax, resulting in discrimination errors. 4. Semantic-Based Methods (Semantic-Based Methods): This method converts articles into vectors by letting the system understand the semantics of paragraphs. It can be used to detect order changes and active-passive changes. However, this method cannot find plagiarized paragraphs and sentences. . Torres proposed to help detect plagiarism by building a dictionary in 2009, and Resnik used external resources to help detect plagiarism by using semantics in 1999. Solving plagiarism detection through semantics will find papers with similar semantics, but it is impossible to know the plagiarized paragraphs and vocabulary, and there is no way to verify plagiarism.

In view of this, the inventor thinks hard, studies actively, adds years of experience in related product research, and through continuous testing and improvement, finally develops the present invention.

The purpose of the present invention is to provide a method for screening and associating text data that can quickly sort out the brief information of the text data.

The method for the present invention to achieve the above-mentioned purpose comprises the following steps: S11. Based on a word-segmentation vocabulary database, carry out word-segmentation processing to a text data To generate a word segmentation information; S12. Screening the word segmentation information to generate a screening word segmentation information; The screening word segmentation information has more than two screening word segmentation information; S13. Relevant to the screening word segmentation information Process to generate a plurality of relational sequence information; each of the relational sequence information is composed of more than two adjacent screening words.

Preferably, before performing the step S11, a step S110 can be performed; the step S110 is: collect the author-defined keywords in the text data to establish a professional keyword vocabulary database, and store the professional keyword vocabulary The word-segmentation vocabulary is imported into the word-segmentation vocabulary, so as to obtain correlation sequence information that is closer to the original meaning of the text data.

Preferably, in this step S12, after the screening process, the synonym word processing can be carried out first, and then the subsequent steps are carried out; the synonym word processing is: the text synonym check is carried out for the screen word segmentation after the screening process, and the Synonyms and synonyms are converted into standard text.

Another object of the present invention is to provide a text data screening and association system that can quickly sort out the brief information of the text data.

The structure of the present invention to achieve the above object includes: a storage module for storing a word segmentation vocabulary; a word segmentation processing module for performing word segmentation processing on a text data to generate a word segmentation information; a screening processing module A set is used for performing screening processing on the word segmentation information to generate a piece of screening word segmentation information; a correlation processing module is used for performing correlation processing on the screening word segmentation information to generate a plurality of correlation sequence information.

Another object of the present invention is to provide a method that can quickly sort out the brief information of multiple comparison text data, and

material; step S28 is: establish an intersection sequence, and arrange all the relative sequence information identical to the relevant sequence information to be compared; step S29 is: analyze each piece of sequence information that has the same correlation with the text data to be compared The originality of the textual materials to be compared is analyzed.

Preferably, in the step S23, after the screening process, the synonym word processing can be performed first, and then the subsequent steps can be performed, which can increase the correlation comparison effect.

Another object of the present invention is to provide a method that can quickly sort out the brief information of multiple comparison text materials, and integrate the brief information of each comparison text data, so as to facilitate the analysis of the originality of the text to be compared Data filtering and association system.

The structure of the present invention to achieve the above-mentioned purpose includes: a storage module for storing a word segmentation vocabulary and a comparison set information;

A word segmentation processing module, which is used to perform word segmentation processing on each comparison text data of the comparison set information to generate a pair of comparison and segmentation information respectively; a screening processing module, which is used to filter the comparison and segmentation information Processing to generate a pair of comparison and screening word segmentation information; a correlation processing module, used to perform correlation processing on the comparison screening and segmentation information to generate a plurality of comparison correlation sequence information; an integration module for Integrate all of the comparative correlation sequence information to create a correlation index file.

Preferably, the word segmentation processing module, screening processing module and correlation processing module perform word segmentation processing, screening processing and correlation processing on a text data to be compared to generate a plurality of correlation sequence information to be compared , and the screening association system of the text data further includes: a comparison module, based on the waiting Comparing the correlation sequence information with the correlation index file respectively, finding out each control text data having the same comparison correlation sequence information as the correlation sequence information waiting for comparison; an intersection module, combining all the correlation sequence information with The arrangement sequence of the reference sequence information with the same correlation sequence information to be compared is used to establish an intersection sequence; an analysis module is used to analyze each comparison text data having the same correlation sequence information with the text data to be compared.

In order to achieve the above and other objects of the present invention, the technical means, components and effects thereof adopted are illustrated below in a preferred embodiment.

100, 100a: Screening and association system for text data

1, 1a: storage module

2, 2a: Segmentation processing module

3, 3a: screening processing module

4, 4a: Relevance processing module

5a: Integrated modules

6a: Compare modules

7a: Intersection Module

8a: Analysis module

9, 9a: Segmentation system

[ Fig. 1 ] is a flow chart of the method for screening and associating text data according to the first embodiment of the present invention.

[ FIG. 2 ] is a block diagram of one of the specific embodiments of the present invention that can automatically execute the method of the first embodiment.

[ FIG. 3 ] is a flow chart of the method for screening and associating text data according to the second embodiment of the present invention.

[ Fig. 4 ] is a block diagram of one of the specific embodiments of the method of the present invention that can automatically execute the second embodiment.

1 to 2 are the first embodiment of the present invention. As shown in Figure 1, the screening association method of text data of the present invention comprises the following steps: S11. Based on a word segmentation vocabulary database, carry out word segmentation processing to a text data to generate a word segmentation information; S12. The word segmentation information Perform screening processing to generate a screening segmentation information; the screening segmentation information has more than two screening segmentations; S13. Perform correlation processing on the screening segmentation information to generate multiple correlation sequences The sequence information is composed of two or more adjacent screening words; by this method, the brief information of the text data can be sorted out quickly. It will be explained in detail below.

Step S11 is to perform word segmentation processing on a text data based on a word segmentation vocabulary database to generate word segmentation information.

Textual materials can be various published textual materials, such as doctoral and master's theses, academic papers, general articles or sentences, etc. In addition, for large text materials such as papers, the paper can be directly regarded as one text data, or multiple text materials can be formed after the paper is divided into sections. There are many methods of segmentation processing, and the examples are as follows. When performing segmentation processing, symbols such as newline symbols, continuous spaces, exclamation points (!), semicolons (:), tildes (~), question marks (?), commas (,), periods (.)... Based on this, a text data is divided into multiple text data with its length not less than the appropriate length as the cut-off point. When performing segment processing, it is also possible to divide a text data into multiple text data based on each chapter and section of the text data. When performing segmentation processing, it can also be used together with the word segmentation vocabulary, based on the method of screening and segmenting a predetermined number of words such as ten, twenty, etc., as the basis, and then divide a text data into multiple text data .

Segmentation processing is to convert text data into word segmentation information according to multiple words recorded in the word segmentation vocabulary. Multiple words in the word segmentation vocabulary can be classified according to part of speech, such as common nouns (Na), foreign words (FW), action verbs (VC), action intransitive verbs (VA), local words (Nc), Proper noun (Nb), verb of status (VHC), colon

Names of techniques, etc., put these author-defined key words into the word segmentation vocabulary and then perform word segmentation processing and subsequent steps, so as to obtain relevant sequence information that is closer to the original meaning of the text data.

Figure 2 shows one of the specific embodiments of the system for screening and correlating text data that can automatically execute the method for screening and correlating text data in the first embodiment. As shown in Figure 2, the present invention provides a kind of textual data screening association system 100, which includes: a storage module 1, used to store a hyphenated vocabulary; Segmentation processing to generate a word segmentation information; a screening processing module 3, used to filter and process the segmentation information to generate a screening segmentation information; a relevance processing module 4, used to filter the segmentation information Word information is subjected to association processing to generate a plurality of association sequence information. The storage module 1, word segmentation processing module 2, screening processing module 3 and correlation processing module 4 etc. can be established in one or more computers and/or cloud servers. When the system 100 for filtering and correlating text data is established in a cloud server, a corresponding web page can be set up, and the user can obtain a plurality of correlation sequence information (not shown in the figure) after inputting text data.

3 to 4 are the second embodiment of the present invention. As shown in Figures 3 to 4, the screening association method of the text data of the present invention comprises the following steps: S21. set up a contrast collection information with more than two contrast text data; S22. Segmentation processing is performed against the text data to generate a pair of comparison and segmentation information; S23. Screening is performed on the comparison and segmentation information to generate a comparison and screening segmentation information; the comparison and screening segmentation information has two or more The screening word; S24. Screening for such comparisons Selecting word information for correlation processing to generate a plurality of comparative correlation sequence information respectively; these comparative correlation sequence information are respectively composed of more than two adjacent screening and segmentation words; S25. Sequence information is integrated to create a correlation index file; by this method, the brief information of multiple comparative text data can be quickly sorted out, and the brief information of each comparative text data can be integrated together to facilitate the analysis of the text data to be compared originality.

Step S21 is to create a collation set information with more than two collation text data. The comparative collection information may contain various textual materials, for example, some or all of the papers in the knowledge value-added system for doctoral and master's thesis in Taiwan. In addition, when creating comparison collection information, you can create different photo collection information for different fields such as electronics, machinery, text data within 10 years... and so on. The contrasting text data described in the second embodiment and the text data to be compared are the same as the text data in the first embodiment, and can be various published text data, such as doctoral and master's thesis, academic papers, general articles or sentences etc., the difference is that in the second embodiment, it is necessary to compare and analyze the textual data to be compared with each of the control textual data one by one, so there are different names to facilitate the distinction.

Steps S22-S24 are to perform segmentation processing, screening processing and correlation processing on each comparison text data in the comparison collection information respectively, and respectively generate comparison segmentation information, comparison screening segmentation information and multiple comparison correlation sequences Information.

Step S25 is to integrate all the comparative correlation sequence information together to create a correlation index file. The associated index file created by integration can be It can be compared with the text data to be compared, and then it is convenient to analyze the originality of the text data to be compared.

As shown in Figure 3, step S220 can be performed before step S22; step S220 is: collect the author-defined keywords of some or all of the comparative text data and the text data to be compared to establish a professional key Word vocabulary, and importing the professional keyword vocabulary into the word segmentation vocabulary can be used to obtain a relevance index file that is closer to the original meaning of the text data. In addition, the work of removing duplication can be added to the finishing work of the professional keyword vocabulary database, so as to increase the processing efficiency.

In the second embodiment of the present invention, the brief information of the comparative text data can be quickly sorted out, and the brief information of each comparative text data can be further integrated, so as to facilitate the user to perform comparative analysis on the text data to be compared. For example, the originality of the text data to be compared is analyzed through the following steps S26-S29.

Step S26 is to perform word segmentation processing, screening processing and correlation processing on a text data to be compared to generate a plurality of related sequence information to be compared. The processing methods of steps S22-S24 and step S26 are the same as steps S11-S13, so the generated comparison related sequence information and the related sequence information to be compared have corresponding types, which can facilitate the comparison. In addition, in S12, S23 and/or S26, after the screening process, synonyms may be processed first, and then subsequent steps are performed. The synonymous word processing is: carry out the text synonym check on the screened words after screening, convert some or all synonyms and synonyms (except some special words that are not suitable for synonymous word processing) into standard text, which can increase the correlation comparison Effect. For example, change "air conditioner" and "air conditioner" to "

Step S22 performs word segmentation processing.

Step S23 performs the screening process, and each screening word can be numbered sequentially, for example, the first reserved filtering word of ID1 is recorded as ID1tp1.

Step S24 carries out association processing, which can sequentially number each comparison association sequence information, for example, record the first comparison association sequence information of ID1 as ID1S1.

Step S25 establishes the correlation index file, and each comparison and screening word segmentation information can be regarded as the index (namely Index or Key) of the correlation index file, and the serial number of the comparison screening and segmentation information can be used as the data of the correlation index file (Data). When creating a relational index file, any word segmentation information for comparison screening may be the same as another word segmentation information for comparison screening (for example, ID1S2, ID2S1). Therefore, an index can compare a plurality of different data, and the number of the data is large, and the total length of the stored data increases as more text data for comparison is added.

In step S26, word segmentation, etc. are performed on the text data to be compared, and the text data to be compared can be recorded as IDx.

Step S27: Use the related sequence information to be compared as an index To search, read all data with the same index in the associative index file.

Step S28 is to establish an intersection sequence, which can arrange all the reference related sequence information that is the same as the relative sequence information to be compared in the same order (ie sorting).

Step S29 is to analyze each control text data having the same correlation sequence information as the text data to be compared, so as to generate an originality analysis result of the text data to be compared relative to each control text data. There are many methods of comparison, such as using statistical analysis methods to analyze the reference ratio of the similarity of each text data in the comparison set information of the text data to be compared, and commonly used theories such as the Dice Coefficient rule can be used. In addition, it is also possible to perform simple analysis in an easy-to-understand and general method.

By means of the screening and correlation method of the text data mentioned above, the correlation between the text data to be compared and each control text data can be quickly analyzed, and the originality of the text data to be compared can be further analyzed.

FIG. 4 shows one of the specific embodiments of the system for screening and correlating text data that can automatically execute the method for screening and correlating text data in the second embodiment. As shown in Figure 4, the present invention provides a kind of textual data screening association system 100a, which includes: a storage module 1a, used to store a word segmentation vocabulary database and a comparison set information; a word segmentation processing module 2a, with Segmentation processing is performed on each of the comparison text data of the comparison collection information to generate a comparison and segmentation information respectively; a screening processing module 3a is used to perform screening processing on the comparison and segmentation information to generate a comparison screening Segmentation information; a correlation processing module 4a, which is used to perform correlation processing on the comparison and screening segmentation information to generate a plurality of comparison correlation sequence information respectively; an integration module 5a, which is used to correlate all comparisons The sexual sequence information is integrated to create a relational index file. In addition, the word segmentation processing module 2a, screening processing module 3a and association processing module 4a can further perform word segmentation processing, screening processing and association processing on a text data to be compared to generate multiple associations to be compared Sexual sequence information, and the screening and association system of the text data 100a further includes: a comparison module 6a, which uses the correlation sequence information to be compared with the correlation index file respectively to find out the comparison correlation sequence information having the same correlation sequence information as the correlation sequence information to be compared Each contrasting text data; an intersection module 7a, which arranges all the contrasting correlation sequence information identical to the correlation sequence information to be compared, so as to establish an intersection sequence; an analysis module 8a, analyzes each copy and the correlation sequence information to be compared A comparison text data that has the same associative sequence information as the text data.

The storage module 1a, word segmentation processing module 2a, screening processing module 3a, relevance processing module 4a, integration module 5a, comparison module 6a, intersection module 7a and analysis module 8a can be built in one or multiple computers and/or cloud servers. When the text data screening and association system 100a is established in a cloud server, a corresponding web page can be set up, and the user can obtain the originality analysis result (not shown in the figure) after inputting the text data to be compared.

In addition, the aforementioned parts related to word segmentation processing, such as steps S11, S22 and word segmentation vocabulary database, etc., can adopt known word segmentation systems such as CKIP developed by Taiwan Academia Sinica or stuttering of published computer codes9 , 9a, in order to save costs.

As mentioned above, written materials can be various published written materials, and for large-scale written materials such as papers, the paper can be directly regarded as a single written data, or the paper can be processed in sections to form Multiple text files. These multiple pieces of text data formed by segment processing can be correlated with each other in order to make an integrated analysis result of originality. For example, the number of a thesis is IDa1, and the numbers of the thesis after being segmented (for example, divided into chapters) are IDa2~IDan, that is, Not only the thesis is regarded as a text material, but each section (each chapter) of the thesis is also regarded as a text material. In this way, after analysis, not only the originality analysis results of the text data to be compared relative to the paper can be obtained, but also the text data to be compared relative to each segment (each chapter) of the paper can be obtained. Originality Analysis Results.

The above are the embodiments of the present invention, which are only for convenience of description, and should not limit the meaning of the present invention, that is, all the various transformation designs made according to the scope of the listed patent application should be included in the scope of the present invention. middle.

Claims (10)

A method for screening and associating text data, comprising the following steps: S11. Based on a word-segmentation vocabulary database, perform word-segmentation processing on a text data to generate word-segmentation information; S12. Filtering the segmentation information to generate screening segmentation information; the screening segmentation information has more than two screening segmentation information; S13. Relevant processing is performed on the screening and segmentation information to generate a plurality of correlation sequence information; each of the correlation sequence information is composed of more than two consecutive screening segmentation information. Such as the method for screening and associating textual data in claim item 1, wherein before performing the step S11, a step S110 can be performed first; the step S110 is: collecting the author-defined keywords in the textual data to establish a professional keyword vocabulary database, and import the professional keyword vocabulary database into the segmentation vocabulary database. Such as the screening and association method of text data in claim item 1, wherein in the step S12, after the screening process, the synonymous word processing can be performed first, and then the subsequent steps are performed; the synonymous word processing is: the screening process Screen word breaks for text synonym checks, and convert synonyms and synonyms into standard text. A screening and association system for text data, including: A storage module for storing a word segmentation vocabulary; A word-segmentation processing module, used to perform word-segmentation processing on a text data to generate Generating a broken word information; A screening processing module, used for and performing screening processing on the segmented word information to generate a screened segmented word information; A correlation processing module is used for performing correlation processing on the screened word segmentation information to generate a plurality of correlation sequence information. A method for screening and associating text data, comprising the following steps: S21. Create a comparison collection information with more than two comparison text data; S22. Based on a word-segmentation vocabulary database, perform word-segmentation processing on the contrasting text data to generate a pair of contrasting word-segmentation information; S23. Perform screening processing on the comparison and segmentation information to generate a pair of comparison and screening segmentation information; each of the comparison and screening segmentation information has more than two screening segmentation information; S24. Relevant processing is performed on the comparison and screening segmentation information to generate a plurality of comparison correlation sequence information respectively; the comparison correlation sequence information is respectively composed of two or more adjacent screening segmentation information; S25. Integrate all the reference correlation sequence information to create a correlation index file. Such as the method for screening and associating text data in claim item 5, wherein before performing the step S22, a step S220 is performed; the step S220 is: collecting part or all of the text data for comparison and the text data to be compared The author customizes keywords to build a professional keyword vocabulary, and imports the professional keyword vocabulary into the word segmentation vocabulary library. Such as the method for screening and associating text data of claim 5, wherein after step S25, steps S26 to S29 are performed; step S26 is: performing segmentation processing, screening processing and association processing on a text data to be compared to generate multiple a correlation sequence information to be compared; step S27 is: compare the correlation sequence information to be compared with the correlation index file respectively, and find out the control correlation with the same correlation sequence information as the correlation sequence information to be compared Each control text data of the sequence information; step S28 is: establish an intersection sequence, and arrange all the control correlation sequence information that is the same as the correlation sequence information to be compared; step S29 is: analyze each piece of text data with the correlation sequence information to be compared Collating text data with the same associative sequence information. For example, the method for screening and associating text data in claim item 5, wherein in the step S23, after the screening process, synonymous words can be processed first, and then subsequent steps can be performed. A screening and association system for text data, including: A storage module for storing a segmentation vocabulary and a comparison set information; A word segmentation processing module, which is used to perform word segmentation processing on each comparison text data of the comparison set information to generate a pair of comparison segmentation information respectively; A screening processing module, used for and performing screening processing on the comparison and segmentation information to generate a comparison and screening segmentation information; A correlation processing module, which is used to perform correlation processing on the comparison screening and segmentation information to generate a plurality of comparison correlation sequence information; An integration module is used to integrate all the comparative correlation sequence information to create a correlation index file. Such as the screening and correlation system of text data in claim item 9, wherein the word segmentation processing module, screening processing module and correlation processing module perform word segmentation processing, screening processing and correlation processing on a text data to be compared to generate A plurality of correlation sequence information to be compared, and the screening correlation system of the text data further includes: a comparison module, which compares the correlation sequence information to be compared with the correlation index file respectively, and finds out Each comparison text data of the comparison correlation sequence information which is the same as the correlation sequence information waiting to be compared; an intersection module arranges all the comparison correlation sequence information which is the same as the correlation sequence information to be compared in order to establish an intersection Sequence: an analysis module, which analyzes each comparison text data having the same correlation sequence information as the text data to be compared.

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