JP2022069790A - Information processor, information processing method, and program - Google Patents

Abstract

To provide an information processing system, an information processor, an information processing method and a program which can improve retrieval accuracy so as to obtain a retrieval result close to the intention of a retrieval user using a category of document data and field information in calculation of a retrieval score.SOLUTION: In an information processing system (document retrieval system), retrieval processing stores a weight set in each field included in a document for each category of a document, and calculates a score of the document based on the weight set in the fields, and a relation between the field and a retrieval word.SELECTED DRAWING: Figure 9

Description

The present invention relates to an information processing apparatus, an information processing method, and a program.

The conventional full-text search based only on word frequency does not consider the importance or meaning of words. Therefore, there is a problem that even if a word that appears infrequently but is important is hit, it does not appear in the top of the search, or a document that hits a word with the same character but different nuances appears in the search results.

Patent Document 1 discloses a technique for obtaining a search result close to the user's search intention by using the field information of the document data for the calculation of the search score.

Japanese Unexamined Patent Publication No. 2005-053468

Patent Document 1 describes a technique for obtaining a search result close to the user's search intention by using the field information of the document data for the calculation of the search score.

However, it is necessary for the user to input the score reflection ratio for each field every time the search is performed, which is complicated when the number of fields is large. In addition, there is a problem that the part where the field is not prepared as the metadata of the document in advance is not stored in the field. Furthermore, since there is no concept of document categories, there is a problem that field information cannot be extracted and scores can be calculated according to each category.

Therefore, it is desirable to add category information to the document data and define field extraction information for each category.

Therefore, an object of the present invention is to improve the search accuracy so that a search result close to the intention of the search user can be obtained by using the category and field information of the document data in the calculation of the search score.

The information system of the present invention has a storage means for storing weights set in each field included in a document for each document category, weights set in the fields, and a relationship between the fields and search terms. Based on this, it is characterized by comprising a calculation means for calculating the score of the document.

The information processing method of the present invention includes a storage step for storing weights set in each field included in a document for each category of a document, weights set in the field, and a relationship between the field and a search term. Based on this, it is characterized by comprising a calculation step for calculating the score of the document.

In the program of the present invention, the computer stores the weight set in each field included in the document for each category of the document, the weight set in the field, and the relationship between the field and the search term. Based on the above, it is characterized in that it functions as a calculation means for calculating the score of the document.

According to the present invention, it is possible to improve the search accuracy so that a search result close to the intention of the search user can be obtained by using the category and field information of the document data in the calculation of the search score.

It is a figure which shows the system configuration of an information processing system. It is a figure which shows the hardware configuration of an information processing apparatus. It is a flowchart which shows the flow of the process in this Example. It is a flowchart which shows an example of the document registration process in embodiment of this invention. It is a flowchart which shows an example of the field extraction processing in embodiment of this invention. It is a flowchart which shows an example of the field extraction process by a keyword in embodiment of this invention. It is a flowchart which shows an example of the field extraction process by a pattern in embodiment of this invention. It is a flowchart which shows an example of the field extraction process by the morphological analysis in embodiment of this invention. It is a flowchart which shows an example of the search process in embodiment of this invention. It is a flowchart which shows an example of the update process of the search session statistical information in embodiment of this invention. It is a flowchart which shows an example of the field weight update process in Embodiment of this invention. It is a figure which shows an example of the extraction definition list screen in embodiment of this invention. It is a figure which shows an example of the extraction definition detail screen in embodiment of this invention. It is a figure explaining the distance between a field name and a keyword in embodiment of this invention. It is a figure which shows an example of the field weight update process in embodiment of this invention. It is a figure which shows an example of the table of the search session statistical information in embodiment of this invention. It is a figure which shows an example of the calculation of the field score in embodiment of this invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The embodiments described below are examples of specific implementations of the present invention, and are examples of the configurations described in the claims.

FIG. 1 is a diagram showing an example of a system configuration of a document retrieval system according to an embodiment of the present invention. The document retrieval system 2000 according to the embodiment of the present invention includes an information processing apparatus 100, a document DB 107, and a client PC 108. The information processing apparatus 100 is composed of a document registration processing unit 101, a document search processing unit 102, a morpheme analysis dictionary 103, a registered document index 104, an extraction definition DB 105, and a search session statistical information 106, and is used in an external document DB 107 or a document search system. It is connected to the client PC used when registering a document so that it can communicate with it via a network.

The document registration processing unit 101 is a functional unit that executes processing related to a document received from a user. Specifically, text extraction processing, category assignment, and field extraction processing are performed, a search index is created, and processing such as storage in the registered document index 104 is performed.

The document search processing unit 102 is a functional unit that searches for an indexed document by using a search term received from the user. When a search term is received from the user, the text score and field score are calculated from the indexed document, and the respective are added up and reflected in the search result.

The morphological analysis dictionary 103 is a dictionary used when performing morphological analysis.

The registered document index 104 is a DB that stores the text extracted from the document to be registered and the search index for each field. Processing is performed by the search processing unit 102 using this DB.

The extraction definition DB 105 is a DB that stores the extraction definitions defined for each category. The field is extracted by the extraction method set as the extraction definition of the category stored in the present extraction definition DB. Extraction methods include extraction by keywords, extraction by patterns, and extraction by morphological analysis.

The search session statistical information 106 is a DB for updating the user's search session statistical information. It is used when updating the search session statistics of the user and updating the field weight of the extraction definition.

The document DB 107 is a DB in which a document is stored. External DBs such as cloud services are also included.

The client PC 108 is used when accepting a document registration from a user.

FIG. 2 is a block diagram showing an example of the hardware configuration of the information processing apparatus according to the embodiment of the present invention.

As shown in FIG. 2, the information processing apparatus includes a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, a storage device 204, and an input controller 205 via a system bus 200. A voice input controller 206, a video controller 207, a memory controller 208, and a communication I / F controller 209 are connected.

The CPU 201 comprehensively controls each device and controller connected to the system bus 200.

The ROM 202 or the external memory 213 is a control program executed by the CPU 201 such as a BIOS (Basic Input / Output System) or an OS (Operating System), a computer-readable program for realizing this information processing method, and various necessary programs. Holds data (including data table).

The RAM 203 functions as a main memory, a work area, and the like of the CPU 201. The CPU 201 realizes various operations by loading a program or the like necessary for executing the process from the ROM 202 or the external memory 213 into the RAM 203 and executing the loaded program.

The input controller 205 controls input from an input device such as a keyboard 210 or a pointing device such as a mouse (not shown). When the input device is a touch panel, the user can give various instructions by pressing (touching with a finger or the like) the icon, the cursor, or the button displayed on the touch panel.

Further, the touch panel may be a touch panel such as a multi-touch screen that can detect a position touched by a plurality of fingers.

The video controller 207 controls the display on an external output device such as the display 212. The display shall include the display of a notebook computer integrated with the main body. The external output device is not limited to the display, and may be, for example, a projector. Further, as for the device capable of accepting the above-mentioned touch operation, an input device is also provided.

The video controller 207 can control a video memory (VRAM) for display control, can use a part of the RAM 203 as a video memory area, or can separately provide a dedicated video memory. It is possible.

The memory controller 208 controls access to the external memory 213. The external memory is connected to a boot program, various applications, font data, user files, edit files, an external storage device (hard disk) for storing various data, a flexible disk (FD), or a PCMCIA card slot via an adapter. Compact flash (registered trademark) memory etc. can be used.

The communication I / F controller 209 connects to and communicates with an external device via a network, and executes communication control processing on the network. For example, communication using TCP / IP, a telephone line such as ISDN, and communication using a 3G line of a mobile phone are possible.

The CPU 201 enables display on the display 212 by, for example, executing an outline font expansion (rasterization) process in the display information area in the RAM 203. Further, the CPU 201 enables a user instruction with a mouse cursor or the like (not shown) on the display 212.

Various programs described later for realizing the present invention are stored in the external memory 213, and are executed by the CPU 201 by being loaded into the RAM 203 as needed. Further, a definition file and various information tables used when executing the above program are also stored in the external memory 213, and detailed explanations thereof will be described later.

Next, the flow of processing in the present invention will be described with reference to FIG.

In step S301, as presets, the field extraction definition information (including the field weight) for each category and the default extraction definition (extraction definition used for the file in which the category is not set or the field not specified in the field weight set) are set. Accept settings. The field extraction definition information is information in which the field name, the method for extracting the field, and the extraction definition are associated with each category, as shown in the extraction definition detail screen 1300. For example, according to the extraction definition information shown in FIG. 13, for a document in the category of "construction outline", the field extracted by keywords such as "office" and "hospital" can be extracted as the field of "building use". It will be possible.

The set extraction definition information is stored in the extraction definition DB 105.

In step S302, the document registration process is executed for the document (search target document) received from the user. In the document registration process, the text of the search target document is extracted, categories are assigned, fields of the search target document are extracted, and a search index is constructed for the text and fields. The details of the document registration process will be described later with reference to FIG.

In step S303, the document search process is executed based on the search term received from the user. In the document search process, a search process using the search index constructed in step S302 is performed. The details of the document retrieval process will be described later with reference to FIG.

Next, the document registration process executed by the document registration processing unit according to the embodiment of the present invention will be described with reference to the flowcharts of FIGS. 4 to 8.

The flowchart of FIG. 4 is a flowchart showing a process of registering a document in the document registration processing unit 101.

In step S401, it is determined whether or not the processing is completed for all the documents to be registered. If the processing is completed (Yes in S401), the processing of the flowchart is completed, and if there is a document that has not been processed (No in S401), the process proceeds to step S402.

In step S402, a text extraction process is performed on the document. The text extraction process is realized by a technique generally disclosed, and any technique / method may be used.

In step S403, a category is assigned to the document. The category is given to classify what type of document the document is, and in the case of this embodiment, the construction outline, the purchase order, the minutes, etc. are the classification examples of the category. The category assignment here may be performed automatically by a computer or manually by the user.

In step S404, a field extraction process is performed. The field extraction process will be described later with reference to FIG.

In step S405, a search index is created for the text extracted in step S402 and each field extracted in step S404, and stored in the registered document index 104. The search index is a search index used at the time of processing the document search process shown in FIG.

The flowchart of FIG. 5 is a flowchart showing a process of extracting a field from a document.

In step S501, the category of the document given in step S403 is acquired.

In step S502, the extraction definition information of the category set in step S301 is acquired from the extraction definition DB 105. If the category cannot be acquired in step S501, the default extraction definition is acquired.

In step S503, it is determined whether or not the extraction process is completed for all the fields defined in the extraction definition. If it is completed (Yes in S503), the flowchart is terminated, and if not (No in S503), the process proceeds to step S504.

In step S504, the process is branched according to the extraction method of the field to be processed set in the extraction definition information. For example, in the example of FIG. 13, it means that the field of "address" is extracted by morphological analysis. If the extraction method is "keyword", the process proceeds to step S505, if the extraction method is "pattern", the process proceeds to step S506, and if the extraction method is "morphological analysis", the process proceeds to step S507.

In step S505, the extraction process using keywords is performed. The details of the extraction process using keywords will be described later using the flowchart of FIG.

In step S506, the extraction process using the pattern is performed. The details of the extraction process using the pattern will be described later using the flowchart of FIG. 7.

In step S507, extraction processing by morphological analysis is performed. The details of the extraction process by the morphological analysis will be described later in step S508 using the flowchart of FIG. 8, and the extracted field is recorded as the field of the document. At this time, it is recorded in association with the field name of the extraction definition information.

The flowchart of FIG. 6 is a flowchart showing a process of extracting a field from a document by a keyword method.

In step S601, it is determined whether or not all the keywords of the extraction definition have been processed. If all the processes have been performed (Yes in S601), the flowchart is terminated, and if not (No in S601), the process proceeds to step S602.

In step S602, an unprocessed keyword is acquired from the extraction definition.

In step S603, a keyword match for the document is executed. Any method may be used for this keyword match.

In step S604, it is determined whether or not the keyword exists in the document. If it does not exist (No in S604), the process proceeds to step S601, and if it exists (Yes in S604), the process proceeds to step S605.

In step S605, it is determined whether or not the field name exists near the existing keyword. As shown in FIG. 13, the extraction definition related to the extraction process by keyword is registered in which the field name and the keyword are associated with each other. If the field name associated with the keyword exists near the detected keyword (step S605: YES), the process proceeds to step S606, and if it does not exist (step S605: NO), the process proceeds to step S601. return.

Here, the distance between the keyword (Value) and the field name (Key) will be specifically described with reference to FIG.

As shown in FIG. 13, the field name “building use” is associated with five keywords of office, hospital, restaurant, parking lot, and hotel. Therefore, the keyword extracted in the document shown in FIG. 14 is V1. It becomes "hospital", V2 "office", V3 "parking lot". Of these, V1 and V2 are on the same line as the key K1 "building use" and can be said to be close in distance. On the other hand, V3 is 5 lines away from K1 and is far from K1, and it is considered that it is used in a context different from "building use". Therefore, when extracting a keyword, it is possible to prevent erroneous extraction by considering the distance between this keyword and the field name and not targeting distant ones.

Returning to the description of FIG.

In step S606, the keyword extracted in step S605 is recorded as the value of the field of the document.

FIG. 7 is a flowchart showing a field extraction process using a regular expression pattern.

In step S701, the regular expression pattern set in the extraction definition information is acquired. As an example of the regular expression pattern, in the case of the related law of the field name 1302 in FIG. 13, it is ". + (Law | Ordinance)" of the extraction definition 1304. This is a regular expression for detecting a character string whose "law" or "regulation" is a suffix matching, and according to this condition, for example, "Building Standard Law" and "Noise Countermeasure Ordinance" can be extracted.

In step S702, pattern matching of the regular expression acquired in step S701 is performed on the document.

In step S703, it is determined whether or not processing has been performed for all the parts matched in step S702. If the processing is performed in all patterns (Yes in S703), the processing of the flowchart is terminated, and if not (No in S703), the processing proceeds to step S704.

In step S704, the matched portion is recorded as the value of the field of the document. Also, a part of the matched part using the regular expression function such as group or named forward reference may be used as the value of the field.

FIG. 8 is a flowchart showing a field extraction process using part of speech obtained by morphological analysis.

In step S801, the extraction definition is acquired. For example, in the present embodiment, the sequence of part of speech defined in the extraction definition 1304 of the address of the field name 1302 in FIG. 13 is acquired. In this case, the extraction definition is defined by the sequence of [noun-proprietary noun-region]. In other words, this means extracting a sequence of words belonging to a region category in a proper noun in a noun, and a character string such as "Konan, Minato-ku, Tokyo" is extracted.

In step S802, morphological analysis is performed on the document.

In step S803, it is determined whether or not there is a sequence of part of speech that matches the extraction definition acquired in step S801. If there is no sequence of part of speech (No in S803), the process of the flowchart is terminated, and if not (Yes in S803), the process proceeds to step S804.

In step S804, the matched portion is recorded as the value of the field of the document.

Subsequently, with reference to FIGS. 9 and 17, the processing executed by the document retrieval processing unit in the embodiment of the present invention will be described.

FIG. 9 is a flowchart showing a process in which the search processing unit 102 receives a search term from a user as an input and searches for an indexed document.

In step S901, the search term from the user is acquired.

In step S902, it is determined whether or not there is a document whose document score has not been calculated for all the indexed documents. If there is a document whose document score has not been calculated (Yes in S902), the process proceeds to step S903, and if not (No in S902), the process proceeds to step S908.

In step S903, a document whose document score has not been calculated is acquired.

In step S904, the search score for the body of the document is calculated. The search score is a numerical value indicating the degree of association with the search term. The search score for the text is called the text score. In this embodiment, the text score is a value calculated by a known search score calculation method.

In step S905, it is determined whether or not there is a field for which the field score has not been calculated. If it exists (Yes in S905), the process proceeds to step S806, and if not (No in S905), the process proceeds to step S907.

In step S906, a field for which the field score has not been calculated is acquired, and the search score for the field is calculated. This score is called the field score.

An example of the method of calculating the field score will be described with reference to FIG. The case where the search term "AAA Co., Ltd. Osaka" is accepted from the user will be described. ..

FIG. 17A is a diagram showing a document titled Osaka Prefectural Police Department Minutes, in which the document is divided into fields and the values and weights of each field are associated with each other. FIG. 17B is a diagram showing a document titled XX project outline, in which values and weights are associated with each field as in FIG. 17A. The weight is a value determined by the category of the document. The contents shown as the values of each field in FIG. 17 include character strings that do not match the extraction definition for the sake of explanation, but as explained in steps S506, S604, and S704, each field is shown. What is registered as the value of is a character string that matches the extraction definition.

First, the number of occurrences of the search term is counted for each field.

In the document of FIG. 17A, "Osaka" appears once in the title field 1803, "Osaka" appears 0 times in the personal name field 1804, and "Osaka" appears three times in the text field 1705. Then, the number of appearances of the search term in each field is multiplied by the weight set for each field to obtain the field score.

The weight set in the title field 1803 is 2 as shown in 1806, and Osaka appears once, so 1 × 2 = 2. Similarly, the personal name field 1804 has 0 × 5 = 0, and the text field 1805 has 3 × 1 = 3. The total value (2 + 0 + 3 = 5) is calculated as the field score of the document "Minutes of Osaka Prefectural Police Department".

Similarly, the outline of the XX project in Fig. 17B. When calculating the field score of PDF, 1 × 5 = 5 because the search term AAA Co., Ltd. appears once in the company name field, and 1 × 5 = 5 because Osaka appears once in the address field. Since AAA Co., Ltd. and Osaka appear once each in the text field, 2 × 1 = 2. The total value (5 + 5 + 2 = 12) of these is the outline of the XX project. Calculated as a PDF field score.

In step S907, the text score of the document calculated in step S904 and the field score of the document calculated in step S906 are added up. This value is called the document score.

In this embodiment, the score obtained by adding the text score and the field score is used as the document score, but only the field score, which is the score considering the weight of each field, may be used.

In step S908, the search results are shown to the user in descending order of the document score. In this embodiment, since a calculation method is used in which the document score of a document having a strong relevance to the search term is high, the search results are shown to the user in descending order, but the document score of the document having a strong relevance to the search term is shown. When a calculation method in which is smaller is used, it is displayed in ascending order. That is, documents that are strongly related to the search term are sorted and displayed so that they are displayed at the top of the search results.

As described above, a large weight is set for the fields that characterize the document of the relevant category, such as "person name", "company name", and "address" in the extraction definition information, and the weight for each set field is taken into consideration. By calculating the search score, even if the document contains the same number of search terms, it is possible to display the document that better suits the intention of the user (searcher) (the user is looking for) at the top. Become.

In step S909, the search session statistical information update process is performed. It will be described later using the flowchart of FIG.

In step S910, the field weight update process is performed. It will be described later using the flowchart of FIG.

FIG. 10 is a flowchart showing a process of updating statistical information in a user's search session. The search session is a period from when the user acquires the search result to when the search result is discarded.

In step S1001, 1400 of the search session statistical information table FIG. 15 is initialized. For all the document information included in the search result, the document ID and the category are set, and the number of session views is set to 0.

In step S1002, it is determined whether or not the search session has ended. If it is finished (Yes in S1002), the process of the flowchart is finished, and if not (No in S1002), the process proceeds to step S1003.

In step S1003, it is determined whether or not the user has selected the document as the search result. If it is not selected (No in S1003), the process proceeds to step S1002, and if not (Yes in S903), the process proceeds to step S1004.

In step S1004, the information of the document selected by the user is acquired.

In step S1005, the entry of the document in the search session statistics table is updated. In this case, 1 is added to the number of session views of the table.

FIG. 11 is a flowchart showing a process of updating the field weight of the extraction definition using the search session statistical information. By updating the field weights according to the search information, it is expected that the search accuracy will be further improved.

In step S1101, 1400 of the search session statistical information table FIG. 15 is acquired. Note that only the search session statistical information table for which the search session has ended is acquired here.

In step S1102, the number of views is totaled for each category of hit documents. The hit document is a document searched by the search processing unit. It is used to improve the accuracy of the next search by totaling whether the user has viewed the document that was hit as a result of the search.

In step S1103, it is determined whether or not there is an unprocessed category among the categories aggregated in step S1102. If there is an unprocessed category (Yes in S1103), the process proceeds to step S1004, and if not (No in S1103), the process proceeds to step S1109.

In step S1104, the extraction definition of the unprocessed category is acquired.

In step S1105, there is unprocessed field information included in the search term (a field in which the word used as the search term is a corresponding field among the fields set as the extraction definition of the category and is an unprocessed field). Determine if. If there is unprocessed field information (Yes in S1105), the process proceeds to step S1106, and if not (No in S1105), the process proceeds to step S1103.

In step S1106, it is determined whether or not the number of session views of the category is larger than 0. If it is larger than 0 (Yes in S1106), the process proceeds to step S1107, and if not (No in S1106), the process proceeds to step S1108.

In step S1107, it is considered that the number of session views of the category is larger than 0 and the field can contribute to the search, and the field weight of the field is added by (session view number) × 0.01. This calculation formula is just an example, and other calculation methods may be used.

In step S1108, it is considered that the number of session views of the category is 0 and the field does not contribute to the search, and the field weight of the field is subtracted by 0.01. This calculation formula is just an example, and other calculation methods may be used.

In step S1109, the search session statistical information table that is no longer needed is discarded.

Here, an example of the field weight update process will be described with reference to FIG. First, it is assumed that a search term including "address" and "building use" is used as the search term, and the search session statistical information table at the end of the search session is 1400 in FIG. Further, it is assumed that the extraction definitions of the categories "construction outline", "order form", and "minutes" are 1500, 1600, and 1700, respectively. In the field weight update formula, if the number of session views is larger than 0, (session views) x 0.01 is added, and if it is 0, 0.01 is subtracted. In this case, the number of sessions viewed for each category of the search results from Table 1300 is 2 for the construction outline and 0 for the purchase order and the minutes. The field weight update of the construction outline is 2 x 0.01 = 0.02 because both the field "address" (1501 in Fig. 16) and "building use" (1502 in Fig. 16) are defined. The fields are added and the updated field weights are 3.02 and 2.02, respectively. Since only the "address" (1601 in FIG. 16) is defined for updating the field weight of the purchase order, the subtraction is 0.01, and the updated field weight is 0.09. The field weights in the minutes are not updated because neither "address" nor "building use" is defined.

FIG. 12 is a screen for confirming, adding, and deleting the currently defined extraction definition. The extraction definition list screen 1200 includes an extraction definition addition button 1201, a batch deletion button 1202, a check box 1203, an edit button 1204, and an individual deletion button 1205.

The extraction definition addition button 1201 transitions to the extraction definition detail screen (FIG. 13) by pressing the button, and is for creating a new extraction definition.

The batch delete button 1202 deletes all the extraction definitions for which the check box 1203 is enabled by pressing the button 1202.

The check box 1203 is for enabling batch deletion by using the batch deletion button 1202.

The edit button 1204 transitions to the extraction definition detail screen (FIG. 13) by pressing the button, and edits the selected extraction definition.

The individual deletion button 1205 is for deleting the selected extraction definition by pressing the button.

FIG. 13 is a screen for adding, confirming, and editing the details of the extraction definition. The extraction definition detail screen 1300 includes a category name text box 1301, a field name text box 1302, an extraction method pull-down list 1303, an extraction definition text box 1304, a field weight text box 1305, a field deletion button 1306, and an extraction definition field addition button 1307. ..

When the extraction definition addition button 1101 on the extraction definition list screen 1100 is pressed to move to this screen, the category name text box 1301 is blank, the field name text box 1302, the extraction method pull-down list 1303, and the extraction definition text box. 1304, field weight text box 1305 is not displayed in the initial state. When the edit button of the extraction definition list screen 1200 transitions to this screen, the contents of the extraction definition are the category name text box 1301, the field name text box 1302, the extraction method pull-down list 1303, the extraction definition text box 1304, and the field weight. It is displayed in the text box 1305.

The category name text box 1301 is for setting a name to be given to this extraction definition.

The field name text box 1302 is for setting the name of the field.

The extraction method pull-down list 1303 is for selecting an extraction method. Here, select from "keyword", "pattern", and "morphological analysis".

Extract definition text box 1304 is for setting the definition of extraction. If the extraction method is "keyword", set the list of keywords to be extracted, if it is "pattern", set the regular expression pattern, and if it is "morphological analysis", set the sequence of morphemes to be extracted.

The field weight text box 1305 is for setting the field weight.

The field deletion button 1306 is for deleting the extraction definition of the field by pressing the field deletion button 1306.

When the extraction definition field addition button 1307 is pressed, a blank field name text box 1302, extraction method pull-down list 1303, extraction definition text box 1304, field weight text box 1305, and field deletion button 1306 are added to the bottom line and a new field is added. Can be defined.

In this way, it is expected that the search accuracy will be improved by setting the extraction definition for each category. For example, when comparing the construction outline of the building design document in the registration document with the order form, the information of the address (construction location) of the construction outline is a very important item such as the topography and the applicable local ordinances are different. Since the address of the purchase order is not particularly important information, by setting the field weight higher (for example, 3) in the construction outline and lower (for example, 0.1) in the purchase order, the importance of each category in the same field is set. Can express the difference between. By setting in this way, when searching by address, it is expected that the construction outline with a high search score will be displayed at the top of the search results, and the purchase order with a low search score will be displayed at the bottom of the search. Search results are likely to be in line with the user's intentions.

If you do not set the field weight for each category, if you search by address, if you set to emphasize the address in all categories of documents, both the construction outline and the purchase order will be mixed in the search results, which is convenient. It is thought that the sex is reduced.

The search session statistical information table 1400 of FIG. 15 is a table for holding the statistical information of the search session, and the document ID 1401 composed of the items of the document ID 1401, the category 1402, and the number of session views 1403 is the document hit by the search. It is an item for identification, and the ID of the hit document is registered.

The category of the document is registered in the category 1402.

The session viewing number 1403 records the number of times the user has viewed the document during the search session.

Although the present embodiment has been described above, the present invention can be implemented as, for example, a system, an apparatus, a method, a program, a recording medium, or the like. Specifically, it may be applied to a system composed of a plurality of devices, or may be applied to a device composed of one device.

Further, the program in the present invention is a program in which a computer can execute the processing methods of the flowcharts shown in FIGS. 3 to 11, and the storage medium of the present invention is a program in which the computer can execute the processing methods in FIGS. 3 to 11. Is remembered.

As described above, a recording medium recording a program that realizes the functions of the above-described embodiment is supplied to the system or device, and the computer (or CPU or MPU) of the system or device stores the program in the recording medium. Needless to say, the object of the present invention can be achieved by reading and executing.

In this case, the program itself read from the recording medium realizes the novel function of the present invention, and the recording medium on which the program is recorded constitutes the present invention.

Recording media for supplying programs include, for example, flexible disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, DVD-ROMs, magnetic tapes, non-volatile memory cards, ROMs, EEPROMs, and silicon. A disc or the like can be used.

Further, by executing the program read by the computer, not only the function of the above-described embodiment is realized, but also the OS (operating system) or the like running on the computer is actually realized based on the instruction of the program. Needless to say, there are cases where a part or all of the processing is performed and the processing realizes the functions of the above-described embodiment.

Further, after the program read from the recording medium is written in the memory provided in the function expansion board inserted in the computer or the function expansion unit connected to the computer, the function expansion board is based on the instruction of the program code. It goes without saying that there are cases where the CPU or the like provided in the function expansion unit performs a part or all of the actual processing, and the processing realizes the functions of the above-described embodiment.

Further, the present invention may be applied to a system composed of a plurality of devices or a device composed of one device. It goes without saying that the present invention can also be applied when it is achieved by supplying a program to a system or an apparatus. In this case, by reading the recording medium containing the program for achieving the present invention into the system or device, the system or device can enjoy the effect of the present invention.

Further, by downloading and reading a program for achieving the present invention from a server, database, or the like on a network by a communication program, the system or device can enjoy the effect of the present invention. It should be noted that the present invention also includes all the configurations in which each of the above-described embodiments and modifications thereof are combined.

2000 Document retrieval system 100 Information processing device 101 Document registration processing unit 102 Document retrieval processing unit 103 Morphological analysis dictionary 104 Registered document index 105 Extraction definition DB
106 Search session statistics 107 Document DB
108 Client PC

Claims (7)

A storage means for storing the weights set in each field contained in the document for each document category,
A calculation means for calculating the score of the document based on the weight set in the field and the relationship between the field and the search term.
An information processing system characterized by being equipped with. The information processing system according to claim 1, wherein the calculation means calculates a score of the document based on a weight set in the field and the number of search terms included in the field. The information processing system according to claim 1 or 2, further comprising a table means for displaying search results by the search term based on a score calculated by the calculation means. The information processing system according to claim 3, further comprising an adjusting means for adjusting the weight of the field related to the category of the document based on the browsing record for the document displayed on the display means. A reception method that attaches conditions for extracting fields from a document,
A field extraction means for extracting a field from a document to be searched according to the conditions accepted by the reception means, and a field extraction means.
The information processing system according to any one of claims 1 to 4, wherein the information processing system is provided. It is an information processing method in an information processing system provided with a storage means for storing weights set in each field included in a document for each document category.
An information processing method characterized in that a calculation means of the information processing system includes a calculation step of calculating a score of the document based on a weight set in the field and a relationship between the field and a search term. A storage means that stores the weights set in each field contained in the document for each document category on the computer.
A program for functioning as a calculation means for calculating the score of the document based on the weight set in the field and the relationship between the field and the search term.

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