JPH06208588A - Document retrieving system - Google Patents

Abstract

PURPOSE:To make it possible to regard a document as the document concerned even when a retrieving word is not quite the same as the word in the document. CONSTITUTION:A retrieving word is inputted by a user through a retrieving word input means 1. A document point applying means 2 analyzes the morpheme of the inputted retrieving word and compares a word decomposed at its part of speech with a keyword stored in each word in a document, so that even when the retrieving word does not completely coincide with the word in the document, the word can be retrieved. The means 2 applies a point corresponding to each inputted retrieving word to each document. A document ranking means 3 sorts documents to which points are applied in the descending order of points. A document output means 4 outputs a retrieved result 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 search method, and more particularly, to a document search method that allows a search word to be regarded as a corresponding document even if the search word is not exactly the same as the word in the document. For example, it is applied to a document management device, an image management device, and the like.

[0002]

2. Description of the Related Art The following are known documents which describe the prior art relating to the present invention. The “similar document retrieval apparatus” proposed in Japanese Patent Laid-Open No. 2-2458 / 1990 automatically extracts a keyword from a document that does not have a keyword by performing a morphological analysis, etc. This is a document search function. When a search word is input, a document with a high degree of similarity is output, and independent words are extracted from the document even if keywords are not added to the document in advance, and the frequency is high. The keywords are used in order from the one, and the similarity is determined by comparing with the search word. However, if the document does not contain the exact same word as the search term, it will not be considered as a relevant document, and independent words will be extracted from the document and used as keywords in descending order of frequency and compared with the search term. The method has the drawback that the more frequently appearing words are more important, and an accurate search cannot be performed.

In addition, "text-based retrieval method based on semantic attributes" (Hiroshi Matsuo, 1)
Vol32, No9, Sep.1991 p1172-1179) deals with similarity relations of various expressions by searching the search instruction sentence by the semantic similarity with the headline sentence of each text based on the semantic attribute of the word. It is a thing. That is, the target card is retrieved from a DB (database) that stores a large number of cards with headwords based on the headline sentences. The headline sentences are treated as an index instead of the entire document, and the search words and the headline sentences are used. The partial agreement of is also recognized. However, since the headline sentence is the search target, there is a drawback that the entire document cannot be searched.

[0004]

[Purpose] The present invention has been made in view of the above circumstances, and it is possible to regard a search word as a corresponding document even if the search word is not exactly the same as the word in the document. Since the score is given to the keyword of, the purpose is to provide an accurate search, and to provide a document search method for searching the entire document (that is, not only the headline sentence). .

[0005]

In order to achieve the above object, the present invention provides (1)
A morpheme analysis means for morphologically analyzing the input search word; a part-of-speech decomposed word obtained by the morpheme analysis means;
Comprising a comparing means for comparing the keywords stored in word units in the document, and it is possible to search even if the search word and the word in the document are not completely matched, further,
(2) A score according to the search word is given to each document by calculating the degree of coincidence between the search word and the keyword in each document, and (3) the search in (2) above. By assigning a score to a document according to a word, it is possible to output documents in order from a document matching a search word. Further, (4) In (2), the search word in each document is added. An appropriate score is to give a basic point to the last word in the word string of the search word, increase the importance from the basic point as going back to the front of the word string, and use the total of the importance as the score of the document. Further, (5) In (2), by using a compound word base that is one of the keyword features for calculating the degree of coincidence between the search word and the document,
When giving scores to a document, high scores are not given to words that are unlikely to be keywords, and (6)
In the above (2), by using the proper noun constituent word which is one of the keyword features for the calculation of the degree of coincidence between the search word and the document, a word that is unlikely to be a keyword when giving a score to a document is used. In addition to not giving a high score, (7) In (2) above, by using a prefix modification, which is one of the keyword features, for the calculation of the degree of coincidence between the search word and the document, By giving a score to the prefix, further, (8) in (2), by using the place name identification word, which is one of the keyword features, in calculating the degree of coincidence between the search word and the document. , When adding scores to a document, we did not give high scores that are unlikely to be keywords, and furthermore,
(9) In (2), by using the era identification word that is one of the keyword features for the calculation of the degree of coincidence between the search word and the document, it is difficult to be a keyword when giving a score to the document. The word is not given a high score, or (10) the morpheme analysis means for morphologically analyzing the input search word, and the importance degree for setting the importance degree to each of the word groups obtained by the morpheme analysis means. Setting means, coincidence degree calculating means for calculating the coincidence degree of a keyword composed of a word group given to a registered document from the importance degree, and document score calculating means for calculating the document score of the document from the coincidence degree And a document output means for outputting the documents by the document score calculation means in the order of the document scores, and the degree of importance of the word of the search word that matches the word included in the keyword by the coincidence degree calculation means. Be coincidence degree, furthermore, (1
1) In the above (10), the degree of coincidence is increased when the word sequence included in the keyword matches the word sequence included in the search word by the coincidence degree calculating means, and further, (12) In the above (10), the matching degree when the keyword search word is completely matched by the matching degree calculation means does not change according to the number of words included in the search word, and (13) In the above (10) The average score of the matching degree between the keyword of the registered document and the search word is used as the document score by the document score calculation means, and (14) in (10), the average score of the registered documents is used as the registered document keyword. A value obtained by dividing the sum of the degree of coincidence of the search terms by the number of keywords having the degree of coincidence of 1 or more is used as the document score, and further, (15) In (10), the document score calculation means of the registered document is used. Ki To a maximum value of the word and the search word matching degree between the document score, further, (16) the (1
In 0), the document score calculation means changes the calculation method of the document score according to the appearance position of the keyword in the document, and (17) In (16), the appearance position of the keyword is the title of the document. In the case of, the document score is calculated using a value obtained by multiplying the degree of coincidence obtained by the coincidence degree calculating means, and (18) in (16), the appearance position of the keyword is the first in the document. In the case of the first sentence of the paragraph, the document score is calculated using a value obtained by multiplying the degree of coincidence obtained by the degree-of-coincidence calculating means by a coefficient, and further, (1
9) In (16) above, when the appearance position of the keyword is in the first paragraph, second sentence or later of the document, the document score is calculated using a value obtained by multiplying the matching degree obtained by the matching degree calculation means by a coefficient. Further, (20) In the above (16),
If the appearance position of the keyword is the first sentence after the second paragraph of the document, the document score is calculated by using the value obtained by multiplying the degree of coincidence obtained by the coincidence degree calculation means by a coefficient, and further, (2
1) In the above (16), when the appearance position of the keyword is from the second paragraph to the second sentence of the document, the document score is calculated using a value obtained by multiplying the coincidence obtained by the coincidence calculation means by a coefficient. (22) In (10), the document score calculation means changes the calculation method of the document score according to the succeeding word of the keyword, and (23)
In the above (22), when the subsequent word of the keyword is the case particle "ga", the document score is calculated using a value obtained by multiplying the degree of coincidence obtained by the coincidence degree calculating means by a coefficient, and (24) ) In (22), when the succeeding word of the keyword is the auxiliary particle “ha”, the document score is calculated using a value obtained by multiplying the matching degree obtained by the matching degree calculation means by a coefficient. 25) In (22), when the succeeding word of the keyword is the case particle "wo", the document score is calculated using a value obtained by multiplying the coefficient of coincidence obtained by the coincidence degree calculating means, and (26) In (22), the succeeding word of the keyword is the case particle “ga” / the sub particle “ha”.
/ When the particle particle is other than "wo", the document score is calculated using a value obtained by multiplying the degree of coincidence obtained by the coincidence degree calculating means, and (27) In (10), the document score is calculated. The calculation means changes the calculation method of the document score according to the appearance position of the keyword in the document and the subsequent word, or (28) the morpheme analysis means for morphologically analyzing the input search word and the morpheme analysis means Degree setting means for setting the degree of importance for each of the selected word groups, and the degree of coincidence calculation for calculating the degree of coincidence with the keyword given to the registered document using the degree of importance set by the degree of importance setting means Means, a document score calculating means for calculating the document score of the document from the coincidence degree calculated by the coincidence degree calculating means, and a document output means for outputting the documents in the document score order by the document score calculating means. The score corresponding to the search word is given to each document by calculating the degree of coincidence between the search word and the keyword in each document, and the documents are output in the order of the score. ), The importance of the word is set by the importance setting means according to the appearance position of the word, and (30) in (29), the importance of the word is set by the importance setting means. In this case, the importance of the word is set according to the number of constituent words of the search word, and further, (31) in (29), when the importance of the word is set by the importance setting means, The importance is set according to the part of speech, and further, (32) above (3)
In 1), when the importance level of the word is set by the importance level setting means, the importance level is set according to a keyword feature that describes a grammatical / semantic feature that is not described by the part of speech of the word. (33) In (28) above, when the degree of coincidence between the document keyword and the search word is calculated by the degree-of-coincidence calculation means, the sum of the importance degrees of the words common to the keyword and the search word is taken as the degree of coincidence. Is (34) above (3
In 3), when the degree of coincidence between the document keyword and the search word is calculated by the degree-of-coincidence calculating means, the degree of coincidence is increased when the word sequence included in the keyword and the word sequence included in the search term match. Furthermore, (35) said (33)
In the calculation of the degree of coincidence between the document keyword and the search word by the degree-of-coincidence calculating means, the degree of coincidence is increased when the keyword and the unseen word of the search term match, further, (36) above In (33), when the degree of coincidence between the document keyword and the search word is calculated by the degree-of-coincidence calculating means, the degree of coincidence is increased when the keyword matches the first word of the search word. is there. Hereinafter, description will be given based on examples of the present invention.

FIG. 1 is a block diagram for explaining an embodiment of a document search system according to the present invention. In the figure, 1 is a search word input means, 2 is a document score giving means, 3 is a document ranking means, 4 Is a document output unit, and 5 is a document to which a keyword is added. First, a user inputs a search term.
Next, the document score assigning means 2 assigns a score to each document according to the input search word. Note that, here, it is assumed that the document 5 is prepared in which words are preliminarily sectioned and the keywords are added. Next, the document ranking means 3 sorts the documents to which the scores have been added in descending order of score, and the document output means 4 outputs the sorted documents.

FIG. 2 is a flow chart for explaining the operation of the document score giving means in FIG. step1 ； The search word is subjected to morphological analysis and a part of speech is added to each word. step2 ； Give importance to each of these words according to the rules. step3 ； If the word of the keyword that each document has and the word of the search word partially match, the degree of importance given to the word of the search word is given, and the degree of importance is summed up for each keyword, and the degree of matching of the keyword is calculated. calculate. step4 ； The degree of coincidence is summed up for each document, and it is set as the score of the document.

In FIG. 2, "importance" is a value given to each word by morphologically analyzing the search word. The “coincidence degree” is a value obtained by adding the degree of importance of a search word corresponding to a keyword and a search word (part) in the document, and summing up for each word. The “score” is a value when the degree of coincidence is finally summed up for each document.

FIG. 3 is a flow chart for explaining the importance giving rule for a search word. As described above, it is assumed that the search word is morphologically analyzed and decomposed into parts of speech. First, the first important thing is to put the pointer at the end (step 1). In other words, the processing is performed while returning backward from the end of the word string. First, set n as the base point and sum as 0 (step
2). Next, it is determined whether or not the word has a keyword feature (step 3). Here, it is divided into those that are given and those that are not given, but those that have been given are the processing of the part above the broken line in FIG. 3 (herein called phase 1), and those that have not been given are broken lines. The processing of the lower part (here called phase2) will be performed. The keyword features will be described later.

First, the processing for phase 1, that is, a keyword feature is given will be described. First,
It is judged whether the keyword feature is "prefix modification" (step 4). The “prefix modification” is a prefix having a function of modifying a subsequent word, which will be described later. If there is no "prefix modification", set n to that word (step
5). Then, n is added to the value of sum and 1 is added to the value of n (step 6). Then, it is judged whether or not the word is the beginning of the word string (step 7), and if it is not the beginning, the process returns to the previous word (step 8), returns to step 3, and repeats the same processing. In other words, the values of n and sum become larger as the process goes before word publishing. If it is at the beginning, the processing for the keyword feature added ends here, and returns to the end (st
ep11) Start processing of phase2. When the keyword feature is "prefix modification" in step 4, the basic point of the word is set (step 9), and the basic point is added to sum (step 10).

Next, the process of phase 2 is performed. step11
Then, when I returned to the end, I added 1 to the sum I summed up in phase 1.
Is added (step 12). Next, as in phase 1, the presence or absence of keyword features is checked (step 13). In fact, features with no features have already been processed in phase 1, so here we will target those with no features. If there is a feature, it is confirmed whether it is the head of the word string (step 16), and the process is terminated. By the way, if there is no feature in step 13,
Set sum (step 14). Then, sum is added again to the total sum up to now, and 1 is further added (step 15). Then, it is judged whether or not the word is the head of the word string (step 16), and if it is not the head, the process returns to the previous word (step 17), returns to step 12, and repeats the same processing. In other words, in phase 2, sum is added as it goes to the front of the word string. In other words, a keyword feature is assigned a higher importance as it is positioned before the word string, and a keyword feature is assigned no matter how many keyword features are added (even if they are consecutive). The importance of one word that is not read is higher.

Here, the keyword features used in the above description will be described. There are five types of keyword features: compound word base, proper noun constituent words, prefix modification, place name identification, and era identification. Table 1 below summarizes the parts of speech, the characteristics, and the roles that can be given to each feature.

[0013]

[Table 1]

Other than the "prefix modification", when it appears alone, it is less likely to be a keyword, or has a characteristic that the distinctiveness is low. Even if we look only at "devices," this alone is not a characteristic word. The same applies to "place name identifiers" and "era name identifiers". "Tokyo" is "University of Tokyo"
There are many matching words such as "Tokyo company", "Tokyo school", "Tokyo company Tokyo branch", etc., and there are many matching words in the document for "Tokyo" alone. From such an intention, even if these words to which the keyword features are added are located in front of the word string, the importance is increased by one point at a time. On the other hand, the general noun or proper noun without a keyword feature is s
um makes it more important. "Prefix modification" is a little different from other features. Usually, the prefix is not considered to be a keyword, but the prefix that is considered to have a great effect on modifying subsequent words such as "new" and "large" is "prefix modification". For these, I decided to give only the basic points.

Next, the above rule will be described by taking the case where the following words are search words as an example. Example 1 Keio University Institute of Medical Science . Morphological analysis is performed and decomposed into parts of speech. (Results of morphological analysis) Keio University Institute of Medical Science → Keio / University / Medicine / Science / Research / Center Assign importance to each word according to the rules.

[0016]

[Table 2]

As described above, the importance (score) is calculated as follows.
The base point (here, 2 points) is given to the last word of. Key
For words with word features,
The process of adding one point to each word is repeated. Key
Words that do not have features (here, "Keio") are
Add one more to the total of all priorities. this
Includes the keyword "University Institute of Medical Science"
Even if there is a document, the keyword "Keio"
This is because it is considered that the document including is more important. Example 2Research and development of new materials  ． Morphological analysis is performed and decomposed into parts of speech. (Results of morphological analysis) New material R & D → New / Material / Research / Development. Assign importance to each word according to the rules.

[0018]

[Table 3]

An example of the handling of the prefix and the handling of the word to which the keyword feature is not given is at a position other than the beginning of the word string. The prefix with the keyword feature "prefix modification" is
A basic point (2 points) is given in order to make a difference in score from the prefix not given. In Example 1, those without a keyword feature were at the beginning of the word string, and were therefore calculated in order from the importance of the last word string. In this example 2, the word having no keyword feature (in this case, "material") is in the middle of the word string, but the flow is the same. I want to give the most importance to that word, so
The importance of "material" was added by adding 1 to the total importance of other words.

Up to this point, the processing of step 2 in FIG. 2 has been completed. In this way, the search terms are given importance. Next, a score is given for each document using this importance. As described in step 3 and step 4 of FIG. 2, when the word of the keyword of each document and the word of the search word match (even if they partially match), the score is given to the importance of the word of the search word. It is obtained by finding the degree of coincidence and finally summing them. The method of scoring will be described using the above-mentioned Example 2, “New Material Research”. That is,
This is the case when "new material research" is used as the search term. The importance of each word of this search term is shown once again.

[0021]

[Table 4]

Next, it is assumed that the following keywords are described in a certain document. At this time, the degree of coincidence of each keyword in the document is calculated as follows.

[0023]

[Table 5]

When the degree of coincidence is calculated, the degree of coincidence for each document is summed up. This value is the score for the document. For example, in this document, this means 13 + 11 + 10 = 34, and the score is 34 points. In this way, when the scores are given to all the documents, the scores are sorted by the document ranking means, and the documents having the highest scores are outputted by the document output means.

FIG. 4 is a diagram for explaining another embodiment of the document search system according to the present invention. In the figure, 11 is a search word input means, 12 is a document score giving means, 13 is a document ranking means, and 14 is a document ranking means. Is a document output means. Search term input means 11
Enter the user's search term. Document score giving means 12
Is given to all registered documents according to the input search word. It should be noted that keywords registered in advance in units of words are added to each registered document. The document ranking unit 13 sorts the registered documents in descending order of document score. The document output unit 14 outputs the search result to the user.

FIG. 5 is a block diagram of the document score assigning means in FIG. 4, in which 21 is a morpheme analyzing means, 22 is an importance assigning means, 23 is a matching score calculating means, 24 is a document score calculating means, and 25 is a document score calculating means. Is a registered document. The morphological analysis means 21 assigns a part of speech to each word in the morphological analysis of the search word. In the importance assigning means 22, the importance is a value representing the importance of each word given to each word obtained as a result of morphological analysis of search words. The importance is calculated for each word according to the rule described later. In the coincidence calculation means 23,
The degree of matching is a value indicating the degree of matching between each keyword assigned to the registered document 25 and the search word. In the document score calculation means 24, the document score is a value indicating the degree of coincidence between the registered document and the search word. It is calculated from the degree of coincidence between each keyword assigned to the registered document and the search term.

Each means will be specifically described below. Importance degree giving means First, a method of calculating the importance degree given to each word obtained as a result of morphological analysis of a search word will be described. Importance is calculated according to the following rules. The importance of the word in the part-of-speech group 1 which is the closest to the end without any search word is the basic point. The degree of importance of the part-of-speech group 1 other than that is set to a value obtained by adding an increment to the degree of importance of the part-of-speech group 1 that is closest to the position and rearward. The importance of the prefix with the keyword feature "prefix modification" is the basic point. The importance of the prefix without the keyword feature "prefix modification" is 0. The importance of part-of-speech group 2 is the sum of the importances of part-of-speech group 1 in (1), (2) the importance of a prefix with prefix modification, and (3) the importance of part-of-speech group 2 behind the position. Three of the totals is the value obtained by adding the increment to the total. The importance of words other than the above is 0.

However, the part-of-speech group 1 is (1) a general noun with the keyword feature “compound word base”, (2) a proper noun with the keyword feature “proper noun constituent word”, and (3) a keyword feature “place name”. It is a proper noun with "identifier", and (4) keyword feature proper noun with "era identifier." What is part-of-speech group 2?
(1) General nouns without keyword features, (2) Proper nouns without keyword features, (3) Numbers, (4) Suffix,
(5) An unregistered word or the like. The keyword features are summarized in Table 6 below.

[0029]

[Table 6]

The processing flow for assigning importance according to this rule is shown in FIG. 3 described above. Hereinafter, examples with importance are shown in Tables 7 and 8 below. There are 2 basic points here,
The increment is 1 point.

[0031]

[Table 7]

[0032]

[Table 8]

Matching Degree Means Means Next, a method of calculating the matching degree between the keyword and the search word based on the importance of each word of the search word will be described. Figure 1 above
In the embodiment of FIG. 2, the degree of coincidence is the sum of the degrees of importance of the search words that match the words included in the keyword. On the other hand, in the method according to the tenth aspect, the product of the degrees of importance of the search words that match the words included in the keyword is taken as the degree of coincidence. Below, "new material research and development" shown in Example 2 of assigning importance is used as a search term, and the keyword is "new material research".
Table 9 below shows an example of the calculation of the degree of coincidence when the terms “material research” and “research material” are changed.

[0034]

[Table 9]

The method of claim 11 is characterized in that the degree of coincidence becomes large when the coincidence degree calculating means coincides the word sequence included in the keyword with the word sequence included in the search word. Therefore, a new "adjacent point" is introduced, and the adjacency point is multiplied by the degree of coincidence for each match between the word sequence included in the keyword and the word sequence included in the search word. Again, Table 10 below shows an example of calculation of the degree of coincidence when the keyword is changed and the search term is “new material research and development”. Here, the adjacent points are two.

[0036]

[Table 10]

When the keyword is “new material research”, the search word and the keyword are completely the same, and the sequences of “new” and “material” and “material” and “research” are the same in the constituent words. There is. Therefore, adjacent points (2 points) are multiplied twice in the calculation of the degree of coincidence (indicated by underlining in Table 10). The claims 10 and 11 are compared. In claim 10, “material research” and “research material”
Was the same as 24. However, in the method of claim 11, since the word order of the keyword and the search word is taken into consideration, in claim 11, the degree of coincidence between the search word “new material research and development” and “material research” whose word order partially matches is doubled. ,
It is 48.

The method of claim 12 is characterized in that, when the keyword and the search word completely match, the degree of matching does not change depending on the number of words included in the search word. Therefore, a "normalization coefficient" is newly introduced so that the degree of coincidence becomes the normalization coefficient when the keyword and the search word completely match. First, the score of the search word is calculated from the importance of the constituent words of the search word. The search word score is the degree of matching when the keyword is equal to the search word. For example, the coincidence calculation method is claim 1.
In the case of the method of 1, the search word score of the search word “new material research and development” is 2 × 3 × 2 × 2 × 2 × 2 = 768. Normalization is performed by dividing the degree of coincidence between the keyword and the search word by the search word score document and applying a normalization coefficient. For example, if the normalization coefficient is 1000 points and the search word and the keyword match, the degree of matching is as shown in Table 11 below.

[0039]

[Table 11]

When the normalization is not performed, the degree of coincidence differs depending on the search word, but the normalization process makes the degree of coincidence equal regardless of the search word. In addition, the search term is "new material research and development"
As an example, Table 12 below shows an example of calculation of the degree of coincidence when the keyword is changed.

[0041]

[Table 12]

Document Score Calculation Means Finally, a method for calculating the document score based on the degree of coincidence between the keyword and the search word will be described. In the embodiment shown in FIGS. 1 and 2,
The sum of the degree of coincidence between each keyword assigned to the registered document and the search word for all the keywords in the registered document was used as the document score. Therefore, there is a drawback that the score of the document becomes large when the number of keywords added to the registered document is large, but the method of claim 13 or claim 14 hardly depends on the number of keywords. According to the method of claim 13, the average value of the coincidence between each keyword of the registered document and the search word is used as the document score. That is, a value obtained by dividing the sum of the degrees of coincidence between each keyword of the registered document and the search word by the number of keywords of the document is used as the document score. As an example, Table 13 below shows a case where “new material research and development” is used as a search term and keywords added to documents are “new material research”, “material research”, “research material”, and “Ricoh”.

[0043]

[Table 13]

Since the number of keywords in this document is 4, the sum of coincidences is divided by 4. According to the method of claim 14, the sum of the degrees of coincidence between each keyword of the registered document and the search word is 1
The value obtained by dividing the number of keywords as above is used as the document score.

[0045]

[Table 14]

Unlike the thirteenth aspect, since the number of keywords whose matching degree is 1 or more is 3, the sum of matching degrees is divided by 3. In the method of claim 15, the maximum value of the degree of coincidence between each keyword of the registered document and the search word is used as the document score.

[0047]

[Table 15]

Next, an embodiment of claim 16 will be described. The method of assigning importance and the method of calculating the degree of coincidence are the same as those in the above-mentioned embodiment, and therefore their explanations are omitted. The document score calculation method will be described below. The document score calculation is to calculate a document score from the degree of matching between each keyword added to the registered document and the search word. Although a plurality of calculation methods have been proposed in the above-described embodiments, the average value method will be used for the description below. However, the method proposed in the present invention can be applied to the maximum value method and the like. In the average value method, the average value of the coincidence between each keyword of the registered document and the search word is used as the document score.
As an example, the case where “new material research and development” is used as the search term and the keywords added to the document are “new material research”, “material research”, “research material” and “Ricoh” are shown.

[0049]

[Table 16]

In the method of claim 16 of the present invention, the calculation result of the document score changes depending on the appearance position of the keyword. Generally, the importance of the keyword varies depending on the appearance position in the document, and it is effective to find the search result that meets the user's request by changing the calculation result of the document score depending on the appearance position. In configuration (17), when the appearance position of the keyword is a title, the value obtained by multiplying the matching score (original matching score) obtained by the matching score calculation unit by the title coefficient (weighted matching score) is used for the document score calculation. In the above example, assume that the appearance positions of the keywords are as shown in the following table. Here, when the coefficient for title is 2, the weighted coincidence degree of “material research” appearing in the title is 61.
Calculated as × 2 = 122. As a result, the document score also differs from the previous value.

[0051]

[Table 17]

In the configurations (18) to (21), the appearance positions of the keywords are respectively the first sentence of the first paragraph, the second sentence of the first paragraph, the second sentence of the second paragraph and the first sentence of the second paragraph, and the second sentence of the second paragraph and later. In the case of, the weighted coincidence with the coefficient is used for the calculation of the document score. In the example above, the first sentence first sentence coefficient is 1.
5, the calculation of the document score when the coefficient for the second sentence after the first paragraph is 1.2, the coefficient for the first sentence after the second paragraph is 1, and the coefficient for the second sentence after the second paragraph is 0.8 Is shown in Table 18 below.

[0053]

[Table 18]

In the method of claim 17 of the present invention, the calculation result of the document score changes depending on the subsequent word of the keyword. Generally, the importance of a keyword varies depending on the succeeding word of the keyword. Therefore, it is effective to find the search result that meets the user's request by changing the calculation result of the document score depending on the succeeding word. In the configuration (23), when the subsequent word of the keyword is the case particle “ga”, the value obtained by multiplying the matching degree (original matching degree) obtained by the matching degree calculation means by the “ga” coefficient (weighted matching degree) is used. Used for document score calculation. In the example above,
It is assumed that the succeeding words of each keyword are as shown in the following table. Here, when the coefficient for “ga” is set to 2, the weighted coincidence degree of “new material research” whose subsequent word is “ga” is 249.
Calculated as x2 = 498. As a result, the document score also differs from the previous value.

[0055]

[Table 19]

In the configurations (24) to (26), the subsequent words of the keyword are other than the sub particle "ha", the case particle "wo", the case particle "ga", and the sub particle "wa" / case particle "wo" (others). )
In the case of, the weighted coincidence with the coefficient is used for the calculation of the document score. In the example above, the coefficient for "ha" is 1.5,
The following table 20 shows the calculation of the document score when the coefficient for "wa" is 1 and the coefficient for other is 0.5.

[0057]

[Table 20]

In the method of the configuration (27) of the present invention, the calculation result of the document score changes depending on the appearance position of the keyword and the succeeding word. The documents introduced in the configurations (16) to (26) are collectively applied to calculate the document score. In the previous example, the document score is calculated as follows.

[0059]

[Table 21]

The document retrieval method described above is characterized by the following. The search can be performed even when the search word input by the user partially matches the keyword assigned to the document. At the time of search, the degree of matching (matching degree) between the search word and the keyword is calculated. Therefore, the search process is performed according to the following steps. S1: The search word is divided into words by morphological analysis. S2: The importance of each word is set. S3: The degree of coincidence is calculated from the degree of importance of the words having the search word and the keyword in common. However, this method has some improvements. (A) In the importance setting of S2, it is necessary to scan the search word twice from back to front. Therefore, the importance setting is complicated. (B) In the calculation of the degree of coincidence in S3, in paragraph numbers (0022) to (0024), since the order of the keyword and the word of the search word is ignored, the degree of coincidence is the same even for a keyword having a different word order. It becomes a value. For example, with this method, it is not possible to distinguish between "material research" and "research material" with different word order consisting of the same constituent words. (C) As shown in the paragraph number (0035), keywords having different word orders can be distinguished by introducing adjacent points, but a product operation is used to calculate the degree of coincidence. Generally, in a computer, the product operation is slower than the sum operation, so that the document search is slow in this method.

In the embodiment described below, for the improvement point (a), the search word is scanned once. Regarding the improvement point (b), when the word order matches in the calculation of the matching score, a bonus score is given according to the word order match. Regarding the improvement point (c), the product operation is not used for the coincidence calculation. Figure 6
Is a block diagram for explaining still another embodiment of the document retrieval system according to the present invention, in which 31 is a document retrieval means and 32 is a document retrieval means.
Is a search word input means, 33 is a document score giving means, 34 is a document sort means, 35 is a document exit means, 36 is an index word file, 37 is a document file, and 38 is a document registration means.

The document registration means 38 saves the document input by the user and the keywords attached thereto in the document file and the index word file. A plurality of keywords can be set in one registration document, and one keyword may be a compound word composed of a plurality of constituent words (for example,
"Document search" is a compound word composed of two words, "document" and "search". The index word file 36 has a structure in which (a plurality of) keywords for each registered document can be identified. The document search means 31 searches the index word file 36 for a document that matches the search word input by the user, and presents the result to the user. The document search is performed by the search word input means 3
2, a document score assigning means 33, a document sorting means 34, and a document output means 35. The search word input means 32 inputs the search word of the user. The document score assigning means 33 calculates a score according to the input search word for all registered documents. The document sorting means 34 sorts the registered documents in descending order of document score. The document output means 35 outputs the search result to the user.

FIG. 7 is a block diagram of the document score giving means in FIG. 6, in which 41 is a morpheme analyzing means, 42 is an importance setting means, 43 is a document score calculating means, and 44 is a matching degree calculating means. . The morpheme analysis means 41 morphologically analyzes the search word, divides it into words, and determines the part of speech for each word. In the document search device of the present invention, a compound word composed of a plurality of words can be used as a search word input by the user. In the degree-of-importance setting means 42, the degree of importance is a value representing the degree of importance of each word given to each word obtained as a result of morphological analysis of search words. Details of the setting method will be described later. In the document score calculation means 43, the document score is a value representing the degree of coincidence between the registered document and the search word. It is calculated from the matching degree of the search word with each keyword given to the registered document. Here, the degree of coincidence is a value representing the degree of coincidence between each keyword assigned to the registered document and the search word. It is calculated from the importance of each word of the search word, and details of the calculation method will be described later. The document score calculation method is the method described above (paragraph number (0042) to
(0055)) is used.

The importance degree setting means and the coincidence degree calculating means will be described below. First, the importance setting means will be described. When the importance level is set, the search word input by the user is divided into words by morphological analysis. A search word Q composed of n (n> 0) words is written as q ₁ ... Q _n . For example, the search term "document search device" is "document"
It is composed of three words, "search" and "device", and q ₁ = document, q ₂ = search, and q ₃ = device. The importance of the word q included in the search word is written as w (q). In the present invention, the importance of a word is given as follows. -The importance of the unsuccessful word in the search term is the basic point α. -The importance of words other than the tail is the value obtained by adding the value obtained by multiplying the distance from the tail by the position coefficient β to the basic point.

When this is written as an expression, it becomes as follows. w (qi) = α + β * (n−i) (1) In this method, it is not necessary to scan the search word twice as described in the prior art, and all the constituent words of the search word can be scanned once. The degree of importance can be set. The importance setting is shown as an example. The search term is “new material fiber development”. This search term is divided into four words, “new”, “material”, “fiber”, and “development”. When the parameters of the above equation are α = 10 and β = 2, the importance of each word is as shown in Table 22 below.

[0066]

[Table 22]

In the above-described method, as the number of constituent words of the search word increases, the importance of the word closer to the head increases, so that the number of constituent words of the search word is large even if the head words of different search words are the same. There is a problem that the degree of importance of the word becomes higher. According to the method of claim 20,
By applying a bias according to the number of constituent words of the search term,
Avoid such problems. That is, the constituent word number coefficient γ is introduced to set the importance. w (qi) = α + β * (n−i) + γ * n (2) Especially, if γ = −β, the importance of the leading word can always be the same value independently of the number of constituent words. If the parameters are α = 12, β = 2, γ = -2 for the search term “new material fiber development” used in the previous example, the importance of each word is as shown in Table 23 below. Become.

[0068]

[Table 23]

In the above-mentioned method, the importance is set by the same expression regardless of the nature of the word. However, some search terms are important and some are not, depending on the nature of the word, and it is desirable to give high importance to the important ones. For example, prefixes are generally less important than nouns because they play an auxiliary role. Therefore,
According to the method of claim 21, it is possible to change the setting parameters (α, β, γ) of the importance according to the part of speech of the word. For example, parameters for nouns (general nouns, sahen nouns, etc.) are α [noun] = 12, β [noun] = 2, γ [noun] =-2, and parameters for prefixes are α [prefix].
= 4, β [prefix] = 0, and γ [prefix] = 0. At this time, the importance of each word of the search word “new material fiber development” is as shown in Table 24 below.

[0070]

[Table 24]

In the above-mentioned method, if the word parts of speech are the same, the importance is set by the same formula. However, whether or not it is important as a search term is not determined only by the part of speech, but depends on the nature of the document targeted by the search system. In the above-mentioned embodiment, the keyword feature is proposed as a description of the grammatical / semantic features of a word smaller than the part of speech. For example, in a fiber-related document retrieval system, nouns related to fibers often appear in documents.
It is less important as a search term than general nouns. Therefore, a keyword feature “compound word base” is added to the noun “fiber” to distinguish this word from other general nouns. Therefore, according to the method of claim 22, it is possible to change the setting parameters (α, β, γ) of the importance according to not only the part of speech of the word but also the keyword feature. For example, α [noun / presence] = 12 depending on the presence / absence of a keyword feature “compound word base” for parameters for nouns
β [noun / presence] = 2, γ [noun / presence] =-2,
α [noun / no feature] = 1, β [noun / no feature] = 1, γ
[Noun / no feature] = -1. Assuming that the parameters for the prefix are the same as before, the importance of each word of the search word "new material fiber development" is as shown in Table 25 below.

[0072]

[Table 25]

Next, the coincidence calculation method will be described. In the matching degree calculation, the degree of matching between one of the keywords added to the document and the index word is calculated using the importance set for the constituent words of the search word. Basically, the sum of the degrees of importance set for the constituent words common to the keyword and the search word is defined as the degree of coincidence between the keyword and the search word. For example, it is assumed that "new material fiber development" is used as the search term and the importance is set as shown in Table 25. Here, using the three words “new material”, “new development”, and “synthetic fiber” as keywords, the degree of coincidence is calculated.

1. Keyword: “new material” (“new” and “material” are constituent words) At this time, two words “new” and “material” are common with the search word. Consistency = w (new) + w (material) = 4 + 8 = 12 2. Keywords: "fiber material development"("fiber""material"
“Development” is a constituent word) At this time, the three words “fiber”, “material”, and “development” are common to the search word. Consistency = w (fiber) + w (material) + w (development) = 3 + 8
+ 4 = 15 3. Keywords: "Synthetic fiber sales"("Synthetic""Fiber"
At this time, only “fiber” is common with the search word. Consistency = w (fiber) = 3

In the above method, when a plurality of words are common to the search word and the keyword, it is impossible to distinguish whether or not they are different depending on the appearance order of the common words. In other words, the keyword is "material fiber" for the search term "new material fiber development".
However, the degree of coincidence is the same for "fiber materials". But,
Since the appearance order of the "material" and "fiber" is the same as that of the "fiber material", the "material fiber" should have a higher degree of coincidence than the "fiber material". Therefore, when there are a plurality of words common to the search word and the keyword, a bonus point is added when the order of the words (word arrangement) is the same in the search word and the keyword. The bonus point (hereinafter, referred to as “adjacent point”) is proportional to the number of matching word sequences, and the adjacency per word sequence is δ. When δ = 3, the degree of coincidence with the same search word and keyword as above is as follows.

1. Keyword: The sequence of "new material", "new" and "material" is common. Matching degree = w (new) + w (material) + δ = 4 + 8 + 3 = 15 2. Keyword: “textile material development” Three words are common, but there is no one with the same word sequence. Consistency = w (fiber) + w (material) + w (development) = 3 + 8
+ 4 = 12 In the above method, it is not possible to distinguish between the case where the search word and the keyword completely match and the case where the search word is included in the keyword. That is, the degree of coincidence with the search term “new material fiber development” is the same whether the keyword is “new material fiber development” or “new material fiber development center”. To solve this problem, in the claim, when the search word and the first word of the keyword match, the δ head,
In the claims, when the search word and the unsuccessful word of the keyword match, δ [unsuccessful] is added as a bonus point. If δ [start] = δ [untailed] = 2, the degree of coincidence with the same search word and keyword as before is as follows.

1. Keyword: The word sequence of "new material", "new", and "material" is common, and "new" is the search term.
Both of the keywords are at the beginning. Matching rate = w (new) + w (material) + δ + δ [start] = 4 + 8
+ 3 + 2 = 17 2. Keywords: "textile material development""Development" is unsuccessful in both search terms and keywords. Consistency = w (fiber) + w (material) + w (development) + δ [untailed] = 3 + 8 + 4 + 2 = 14

[0078]

As is apparent from the above description, the present invention has the following effects. (1) Effect corresponding to claim 1: By performing morphological analysis on a search word and comparing a word that has been decomposed as a result of part-of-speech with a keyword stored in a part-of-speech unit in the document, the search word and the word in the document are You can search even if they do not match exactly. (2) Effect corresponding to claim 2: By calculating the degree of coincidence between the search word and the keyword in each document, it is possible to give each document a score according to the search word. (3) Effect corresponding to claim 3: Since it is possible to give a score to a document in accordance with a search word, it is possible to sequentially output documents in accordance with the search word. (4) Effect corresponding to claim 4: The score according to the search word in each document is to give a basic point to the last word of the word string, and increase the score as going back to the front of the word string. Since the total score is used as the score of the document, the higher the score of the word located before the word sequence, the higher the score. (5) Effect corresponding to claim 5: When calculating the degree of coincidence between a search word and a document, it is difficult to use the compound word base, which is one of the keyword features, as a keyword when scoring a document. You can avoid giving high scores to words. (6) Effect corresponding to claim 6: When calculating the degree of coincidence between a search word and a document, it is difficult to use the proper noun constituent word, which is one of the keyword features, as a keyword when giving a score to the document. A word can be given no high score. (7) Effect corresponding to claim 7: For the calculation of the degree of coincidence between the search word and the document, by using the prefix modification which is one of the keyword features, it is possible to give a score to the prefix having a special meaning. it can. (8) Effect corresponding to claim 8: For the calculation of the degree of coincidence between the search word and the document, by using the place name identification word, which is one of the keyword features, it is difficult to use the word as a keyword when scoring the document. You can avoid giving a high score to. (9) Effect corresponding to claim 9: With regard to the degree of coincidence between the search word and the document, by using the era identification word, which is one of the keyword features, it is possible to make the word difficult to become a keyword when giving a score to the document. Can avoid giving high scores. (10) Effect corresponding to claim 10: The degree of coincidence can be accurately calculated by using the degree of coincidence calculation means as the product of the degrees of importance of the words of the search word that match the word included in the keyword. (11) Effect corresponding to claim 11: The degree of coincidence can be accurately calculated by taking into consideration the arrangement order of words in the calculation of the degree of coincidence. (12) Effect corresponding to claim 12: By introducing a normalization process according to the degree of importance given to a search term in the calculation of the degree of match, the degree of match can be accurately determined without depending on the length of the search term. Can be calculated. (13) Effect corresponding to claim 13: The document score can be accurately calculated without depending on the number of keywords in the document by calculating the average value of the coincidence between the keyword of the registered document and the search word. (14) Effect corresponding to claim 14: The document score is obtained by dividing the document score by a value obtained by dividing the sum of the coincidence between the keyword of the registered document and the search word by the number of keywords having a coincidence of 1 or more. It can be calculated accurately without depending on the number of keywords in. (15) Effect corresponding to claim 15: The document score can be accurately calculated without depending on the number of keywords in the document by setting the maximum value of the degree of coincidence between the keyword of the registered document and the search word as the document score. (16) Effect corresponding to claim 16: Since the weighted coincidence degree and the document score are calculated depending on the appearance position of the keyword in the registered document, the document score becomes more accurate than the conventional one. (17) Effect corresponding to claim 17: Since the weighted coincidence degree and the document score are calculated by the subsequent words in the registered document of the keyword, the document score becomes more accurate than the conventional one. (18) Effects corresponding to claims 18 to 22: Since the importance degree setting means sets the importance degree of the word according to the position of the constituent word of the search word, the importance degree can be set accurately and the search accuracy is improved. To do. In addition, the search speed is improved because the search word is scanned once. (19) Effects corresponding to claims 23 to 26: Since the degree of coincidence calculation means reflects the order (word sequence) of the constituent words of the search word and the keyword in the degree of coincidence, the degree of coincidence calculation can be accurately performed and the retrieval can be performed. The precision word is improved. Further, since the coincidence degree calculation is only a sum operation, the search speed is improved.

[Brief description of drawings]

FIG. 1 is a configuration diagram for explaining an embodiment of a document search system according to the present invention.

FIG. 2 is a flowchart for explaining the operation of the document score giving means in FIG.

FIG. 3 is a flowchart for explaining a rule of giving importance to a search word according to the present invention.

FIG. 4 is a configuration diagram for explaining another embodiment of the document search system according to the present invention.

5 is a block diagram of the document score giving means in FIG.

FIG. 6 is a configuration diagram for explaining still another embodiment of the document search system according to the present invention.

7 is a configuration diagram of the document score giving means in FIG.

[Explanation of symbols]

1 ... Search word input means, 2 ... Document score giving means, 3 ... Document ranking means, 4 ... Document output means, 5 ... Documents given from keywords.

Claims (26)

[Claims] 1. A morphological analysis means for morphologically analyzing an input search word, and a comparison means for comparing a part-of-speech decomposed word obtained by the morphological analysis means with a keyword stored in word units in a document. A document search method that consists of and can perform a search even if the search word and the word in the document do not exactly match. 2. The document search system according to claim 1, wherein a score according to the search word is given to each document by calculating the degree of coincidence between the search word and the keyword in each document. 3. The document retrieval system according to claim 2, wherein the documents are scored according to the retrieval word so that the documents can be sequentially output in order from the retrieval word. 4. The score according to the search word in each of the documents is to give a basic point to the last word of the word string of the search word,
3. The document retrieval method according to claim 2, wherein the importance is increased from the basic point as going back to the front of the word string, and the total of the importance is used as the score of the document. 5. The compound word base, which is one of the keyword features, is used for the calculation of the degree of coincidence between the search word and the document, so that a word that is unlikely to be a keyword when scoring a document is high. 3. The document retrieval system according to claim 2, wherein no score is given. 6. The use of proper noun constituent words, which is one of the keyword features, in the calculation of the degree of coincidence between the search word and the document, so that the word that is unlikely to be a keyword when scoring a document is high. 3. The document retrieval system according to claim 2, wherein no score is given. 7. A score is given to a special prefix by using a prefix modification, which is one of the keyword features, in calculating the degree of coincidence between the search word and the document. Document search method described in 2. 8. The calculation of the degree of coincidence between the search word and the document does not give a high score which is unlikely to be a keyword when giving a score to a document, by using a place name identification word, which is one of the keyword features. The document retrieval system according to claim 2, wherein the document retrieval system is configured as described above. 9. An era identification word, which is one of the keyword features, is used in the calculation of the degree of coincidence between the search word and the document, so that the word that is unlikely to be the keyword when scoring the document is high. 3. The document retrieval system according to claim 2, wherein no score is given. 10. A morpheme analysis means for performing a morpheme analysis of an input search word, an importance degree setting means for setting an importance degree to each of the word groups obtained by the morpheme analysis means, and an importance degree assigned to a registered document from the importance degree. Matching degree calculating means for calculating the matching degree of the keyword composed of a group of words, document score calculating means for calculating the document score of the document from the matching degree, and the documents in the document score order by the document score calculating means. A document retrieval method comprising: a document output unit for outputting, and the coincidence degree calculating unit sets the product of the importance levels of the search words that match the word included in the keyword as the coincidence level. 11. The document according to claim 10, wherein the matching degree calculation means increases the matching degree when the word arrangement included in the keyword and the word arrangement included in the search term match. Search method. 12. The document retrieval system according to claim 10, wherein the degree of coincidence when the keyword search word is completely matched by the degree-of-match calculation means does not change according to the number of words included in the search word. 13. The document retrieval method according to claim 10, wherein the document score calculation means uses the average value of the coincidence between the keyword of the registered document and the search word as the document score. 14. The document score is calculated by dividing the sum of the degrees of coincidence between the keyword of the registered document and the search word by the number of keywords having a degree of coincidence of 1 or more by the document score calculation means. Document search method described in 10. 15. The document retrieval system according to claim 10, wherein the document score calculation means uses the maximum value of the degree of coincidence between the keyword of the registered document and the search word as the document score. 16. The document retrieval method according to claim 10, wherein the document score calculation means changes the calculation method of the document score according to the appearance position of the keyword in the document. 17. The document retrieval method according to claim 10, wherein the document score calculation means changes the calculation method of the document score according to the subsequent word of the keyword. 18. A morpheme analysis means for performing a morpheme analysis of an input search word, an importance degree setting means for setting an importance degree to each of the word groups obtained by the morpheme analysis means, and the importance degree setting means. Coincidence degree calculating means for calculating the degree of coincidence with the keyword assigned to the registered document using the degree of importance, and document score calculation for calculating the document score of the document from the coincidence degree calculated by the coincidence degree calculating means Means and a document output means for outputting the documents in order of the document scores by the document score calculation means. By calculating the degree of coincidence between the search word and the keyword in each document, the score corresponding to the search word is given to each document. A document retrieval method characterized by giving documents and outputting documents in the order of their scores. 19. The document retrieval method according to claim 18, wherein the importance setting means sets the importance of the word in accordance with the appearance position of the word. 20. The document retrieval method according to claim 19, wherein when the importance of the word is set by the importance setting means, the importance of the word is set according to the number of constituent words of the search word. 21. The document search method according to claim 19, wherein when the importance of the word is set by the importance setting means, the importance is set according to the part of speech of the word. 22. When setting the importance level of a word by the importance level setting means, the importance level is set according to a keyword feature that describes a grammatical / semantic feature not described by the part of speech of the word. 22. The document search method according to claim 21. 23. When calculating the degree of coincidence between a document keyword and a search word by the degree-of-coincidence calculating means, the total degree of importance of words common to the keyword and the search term is taken as the degree of coincidence. Document search method described in 18. 24. When the degree of coincidence between a document keyword and a search word is calculated by the coincidence degree calculating means, the degree of coincidence is increased when the word sequence included in the keyword and the word sequence included in the search term match. 24. The document search system according to claim 23. 25. When the degree of coincidence between the document keyword and the search word is calculated by the degree-of-coincidence calculating means, the degree of coincidence is increased when the keyword and the unseen word of the search term match. The document search method according to Item 23. 26. When the degree of coincidence between a document keyword and a search word is calculated by the coincidence degree calculating means, the degree of coincidence is increased when the keyword matches the first word of the search term. Document search method described in No. 23.