TW569112B - Selection method of combined classification feature - Google Patents

Abstract

A selection method of combined classification feature groups single keyword as multiple keywords for representation of certain classification feature, which can be more precise in determining document classification than single keyword.

Description

569112

The present invention relates to a combination of category feature selection methods different from "a type that can combine keywords to represent a certain type of: features that improve the accuracy of the keyword representation." In today's Internet, There are a variety of conveniences for searching, which often need to divide different types of files :: type of file, #in different keywords' this-: the basis for searching or classification. We are, however, nowadays In the technology, the selection of category features uses statistical methods to calculate the probability or frequency of the occurrence of a single keyword = TFIDF and Entropy. However, a single keyword may represent two identical categories, and it is impossible to accurately filter files. For example, a firewall may The defense category can also represent the information category. If a document contains the keywords of the defense brand, it cannot be judged by this keyword that the document is = defense category or information category. If you combine the characteristics of a category, then To clearly represent a certain category, such as a firewall plus a computer hacker, it can represent a category; if a firewall plus a fire, it can represent a fire category. In order to provide a combined category, 'combining a single keyword into multiple' can be calculated than only a single keyword. The present invention can be more applied to extract features in a category from documents and documents. This category will appear in a category The key will be applicable to the subsequent concept construction and in view of this, the main feature selection method of the present invention. In the present invention, the keywords to represent a certain category of features are more accurate and the persuasive concept construction is automatically followed. The features extracted from multiple texts represent a word, and these keywords are used as the construction of the feature Ontology Base.

0213-8269TW (N); STLC-02-B-9ll5; andyp.ptd Page 4

569112 V. Description of the invention (2) In order to achieve the above-mentioned purpose of the invention, the selection of the category features of Taiji Rizhishan ... includes the following steps: ==-type combination "documents of the same category, and then filtering out the words ...: labeling The given words are discarded, and the rest of the words form the key :: together = with only one keyword to build a keyword set;: chapter of all documents in the order of appearance of the document vector, if the keywords in the text "'if not If the keyword is included, the table is 0, and the child is the number of the table, which is the number of keywords. Then, the file vector of all elements in key I is used to build the keyword he \ § into the vector name. The number of prime elements in the transfer is the number of documents; then the key sub-directional key vector, that is, M determines a threshold of 19 / low frequency threshold, filters the element A 1 of the key vector The number of elements in the keyword gate whose number is lower than the threshold value is a vector to form a feature value set, that is, pairwise = s keyword's Greek μ, rich # and ν, and 6 Ν-1 keyword vector as a ν d Π ίί 舁: form a complex N key vector, where N is greater than equal: two-valued two: ^ The number of elements in the vector is higher than the threshold vector ΓΓΛ "" N_1 keywords, any N-1 keyword == is higher than the threshold; repeat- Steps until all eligible combinations are completed. Examples The figure shows the architecture of the present invention. File 11 is the key to the generation of the same type of documents. These documents must be marked and part-of-speech first; the sentences in the file can be used, for example, the CKIP (Chlnese k⑽wledge inf

569112 V. Description of the invention (3) P ocessing) The sameness and part-of-speech tagging system breaks it into two one by one, but not all words can be regarded as features of this category, such as: pretend Words with a relatively high frequency and words with only one word are not likely to be treated as category features. Therefore, the thesaurus is disabled ^: sub-words that are not used in this category; all documents are processed after word breaking 1 2 0 The word filter 140 program will filter the words that are not used to avoid the 'materials: two Λ-style negative #. After the above steps are processed, the key word = H Λ is placed. All the files of this category τ and their households are included in the form of "file code {keyword}". ^ 彳 wei Γ a invention The combined category feature selection method 16 is applied to the combined category feature 丨 7. Next, please refer to FIG. 2 'showing the current keyword set 21 of the present invention, which is to form a keyword set based on the keywords first created in the previous θ graph]] Conjunction, = 隼, the keywords that appear, such as: key combination The second set 4 contains all the files. Then, in the establishment of the file vector 2 the second set is represented by 1 'S does not contain, then it is represented by 0. If the file contains a certain keyword \ {1, L 0, L ..., U, all Files are created J: Let the pieces say · In the step of inverting into a keyword vector 23 〇 Steps: 'Reverse all file vectors by name, so * keywords are treated as vectors now in files 1, 3, At 4 o'clock, we use fu ° 'if there is a keyword f 1 all the keyword vectors are established, these are listed as #, so the number of keyword vectors may be very large. The frequency is not high, so use the == key in the step.

569112 V. Description of the invention (4) 2 Interpolation is used to remove such keywords that appear relatively infrequently. When the number of occurrences of 1 in the keyword vector is less than this threshold, they are removed, and the rest are 1 key vector. Then enter the combination phase, combining all 1 keyword vectors in pairs to become 2 keyword vectors. The combination method is, for example, 1 keyword vector flU, 1, U and 1 keyword vector f2 {1,1, 0,: [丨 Combined into ^ + 丨 2 keywords. At this time, two keyword vectors 丨 1 and {2 are used as a Boolean operation of eight ^^, U, 0,1,1} AND {1,1,0,1}, The result is {1, 〇, 〇, u, this = 2 vector of keywords f1 + f2, in the threshold filtering 26, the same coin selection kicks 2 times in the keyword vector 1 less than the threshold, get 2 Key vector. In the step, it is checked whether 270+ can be combined again, and judging from the human law, when the η keyword vector is to be combined into the n + 1 keyword vector, it must:; 2 7 ϊ ί The number of elements of 1 is higher than the threshold value? ϊ :, the number of η keywords t in the bond direction 篁, the number of any η keywords toward-and f 1 is higher than the threshold. Through π, ′, we can find the feature combination of all lengths, broken shape and set 28 0. Features of the combined combination Fig. 3 shows an embodiment of the present invention.

Dl, D2, D3, and D4 indicate that here ^ Gas ZZ, 3, and 4 are classified as financial management documents; after the word segmentation, 3M is a word, file! There are stock market, investment, economy, = key capital, economy, stock market, file 3 has economy, tourism, travel, investment, and investment 'file 4 there is investment, stock market; the next will be related to: a set of keywords F⑴: stock market, f2: investment, f3: economy: bonds form gold '... tourism, f6: Taipei city

569112 V. Description of the invention (5) D1U, 1,1,1,0,0}, D2 {1,1,1, 〇, 1, 〇}, D3 {1,1,1,0,1,1} , D4 {1,1, 〇, 〇, 〇, 〇}; then invert all file vectors into keyword vectors, so you can get keyword vectors, flU, 1,1,1}, f2 {l, 1, 1,1}, f3 {l, 1,1,0}, f4 {l, 0, 〇, 〇}, f5 {〇, 1,1, 〇}, f6 {〇, 〇, 1,0}, These are called 1-key vectors. If the threshold is set to 3, f 4, f 5 and f 6 will be kicked out, fl, f2, and f3 can be combined into a 2-keyword vector, and the 1-keyword vector can be calculated by AND to get fl + f2 {l, l, l, l}, fl + f3 {l, l, l, 〇}, f2 + f3 {l, l, l, 〇}, because fl + f2, fl + f3, f2 + f3 The number of l in these two keyword vectors is greater than the threshold value 3, so fl + f2, fl + f3, f2 + f3 are all 2 keywords, and can be composed of 3 keywords fl + f2 + f3; Fl + f3 is calculated by AND to get fl + f2 + f3 {l, 1,1, 〇}. Check that the number of 丨 in the fi + f2 + f3 keyword vector is greater than the threshold 3, so f 1 + f 2 + f 3 It is a valid 3 keywords; it can be seen that if the keywords fl + f2 + f3 appear together in the article, then 0fl ^ market, investment, f3: when the economy appears in the article together, the stock judgment article belongs to financial management Article. Therefore, by using the combined category feature method proposed by the present invention, a single keyword can be combined into multiple keywords with different characteristics, which can be better than calculating only the frequency of occurrence of a single keyword. . Only half more accurate and although the present invention has been disclosed in the preferred embodiment as above, lacking and limiting the present invention, anyone skilled in this art can make some changes without departing from its scope and scope. With retouching, tinby, the essence of the invention is therefore protected by the invention

〇213-8269TWF (N); STLC-〇2-B-9115; andyp.ptd

569112 V. Description of Invention (6) The scope shall be determined by the scope of the attached patent application. Page 9 0213-8269TWF (N); STLC-02-B-9115; andyp.ptd 569112 The diagram simply illustrates that in order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the following examples are given, and With the accompanying drawings, the detailed description is as follows: Figure 1 is a diagram showing the architecture of the present invention. Fig. 2 is a flowchart showing the present invention. Fig. 3 shows an embodiment of the present invention. Explanation of symbols 11 0 ~ files of the same category; 1 2 0 ~ word segmentation; 1 3 0 ~ stop word dictionary; 1 4 0 ~ stop word filtering; 1 50 ~ database; 1 6 0 ~ combined category feature selection Method: 1 0 0 ~ combined category features; 210 ~ established keyword set; 220 ~ established file vector; 230 ~ reversed into keyword vector; 2 4 0 ~ filtered; 2 5 0 ~ combined keyword vector; 2 6 0 ~ threshold filtering; 270 ~ check whether recombination is possible; 28 0 ~ combined feature set;

D1, D2, D3, D4 ~ files; F ~ keyword collection.

0213-8269TWF (N); STLC-02-B-9115; andyp.ptd Page 10

Claims (1)

/ 、 1Please patent scope 1 · A combined category feature selection method of keyword creation of all files' includes the following steps: • Key J :: Key: Set in the keyword set · Use keywords as vector names 2 sequentially build file vectors; pre-vectors, · invert all file vectors to create keywords and combinations that filter low-frequency occurrences; and key vector to form special sets: and, 2 if the scope of the patent application, item 1, The σ method judges that the category feature selection of the keyword set V4 includes the combination: ::: part-of-speech tag plurals of the same category file. With: .. k 4 verbal auxiliary words, adjectives and only, two and, the remaining words form the key Words such as, ', and words, discarding these plural plural files of the same category and forming a keyword set. The keywords that have appeared are ranked in a certain order. 3 As in the method of item 1 of the scope of the patent application, the method of creating a file vector is selected; the category feature selection table of the combination of 4 is not included. If the keyword is not included The number of keywords is the number of keywords. Show 'Elements of this file vector 4. As the third method of the scope of patent application, in which the category feature selection of the combination of keywords is described as 1, if it does not exist in the document °%, if the keyword exists in the document That is, the number of elements is the document age. 17 is represented by 0, and the key vectors are arranged in order. ’In the β-hai keyword vector, the file is selected with a certain 5. Select the category features as described in item 4 of the scope of patent application 569112 VI. Patent application scope ~ A " method, wherein filtering a low-frequency keyword vector refers to setting a threshold value, and filtering the key vector with a number of elements below 1 is the key to the threshold value Word vectors to form plural key vectors. m 6 · The method for selecting category features of a combination as described in item 5 of the scope of patent application, wherein the combination key vector includes the following steps: 1. A pair of N-1 key vectors are combined to perform the Bollinger operation of A, a N key word vector 'where N is greater than or equal to two, which must be met. The number of 7 key elements set to 1 by the n key number is higher than the threshold value, and the N key is given to N-1 keywords. The elements of any keyword vector are 1 薏 higher than the threshold; cause 岣 to repeat the above steps until all the groups meeting the above conditions form the N key vectors into a feature value set. Fancheng; Ke7. — A readable medium (medium), which is sufficient to perform the following steps: Imaginary knowledge, the keywords of all files are created. The key $ 二 二 KEY in the keyword set will be key Word as vector name & 2 to create a file vector word vector; said to invert all file vectors to create a filtering combination of keywords with a low frequency of occurrence: key word vectors to form eigenvalues 8. Such as the 7th fruit of the scope of patent application ^ Establishing a keyword collection system includes the following steps: “Available media, in which word segmentation and part-of-speech tagging are plural and similar a 丨 丄 filter out word auxiliary words, describe 1 * files, and Gu Yi and words with only one word. 0213 8269TWF (N); STLC-02-B-9115; andyp.ptd 569112 --------- 6. For patent application, the rest of the words form keywords, and they are listed = these plural documents of the same category have appeared The keywords are arranged in a certain order to form a keyword set. Another sequence is to establish 9 texts :: Special: The readable media described in the seventh item, which contains the key; two Γ table text package, the keyword: a 1 means' if not including words Number. I mean, the number of elements in the file vector is the readable media described in item 9 of the patent application. If the key word exists in the file, i means that : It is expressed in °; the number of elements of the keyword vector is ", it is λ, such as the keyword vector towel, and the files are arranged in a certain order., ..., σ is called readable as described in item 10 of the patent scope. Media, complex, 'filtering low-frequency keyword vectors refers to keywords with a _threshold value, y off = vector elements with a number of 1 below this threshold, k-direction®, forming a complex keyword vector. Key 12 12 · As described in the readable media described in item 11 of the scope of the patent application, in 1, the combination of the keyword vector includes the following steps: The number of element 1 is higher than the threshold value, and among the N key N-1 keywords, any element of the N] keyword vector is higher than the threshold value; repeat the above steps until all the above conditions are met The combination is completed; the N key vectors are formed into a feature value set. 0213-8269TWF (N); STLC-02-B-9115; andyp.ptd page 13