KR20010001989A - Performance Evaluation Method for Keyfact-based Text Retrieval Model - Google Patents

Abstract

PURPOSE: A retrieval performance measurement method for a key fact based text retrieval model is provided to calculate a weight value on a document and a query language and give the document a priority based on a similarity between the document and the query. CONSTITUTION: A retrieval performance measurement method for a key fact based text retrieval model comprises the steps of extracting key words from a set of documents and queries(S01), asking if setting a variable value of retrieval performance assessment elements on the extracted result(S02), if the answer is positive, giving the variable value of retrieval performance assessment elements on a new document size, a key fact frequency within the document, and a key fact weight constant(S03), making a document and a query vector including the assessment variable value(S04), calculating a key fact weight value of the document and the query by a key fact weight equation(S05), obtaining a retrieval performance result of the key fact based text retrieval model for searching for an optimal retrieval model according to characteristics of a retrieval target document domain(S06), giving the documents a priority number(S07), and assessing the retrieval model(S08).

Description

Performance Evaluation Method for Keyfact-Based Text Retrieval Model

The present invention relates to a method for measuring the search performance of a key fact-based text search model, and more particularly, to a key fact-based text search model and a key performance weighting method for finding a key fact weighted constant in various areas. It is about.

As a conventional text information retrieval method, keyword-based text information retrieval has been used. The keyword-based text information retrieval method has low accuracy due to the following problems. The problem of keyword-based text information retrieval is firstly, because the document is represented by the word of the noun vocabulary of keywords, it does not accurately represent the meaning of the document and thus the representativeness of the document expression is reduced, thus acting as a fundamental factor of the deterioration of the search accuracy. Second, in the case of querying with natural words, sentences, or keyword words, the keyword-based information retrieval expresses the query as a keyword and thus has a disadvantage in that the user cannot accurately perform the search. Therefore, keyword-based information retrieval is fundamentally limited in search accuracy in terms of document retrieval around the word keyword.

Key fact-based text information retrieval has been proposed as a solution to overcome the problems of keyword-based text information retrieval.

Key fact-based text information retrieval comes from the concept that various expressions represent a fact because it is a fact rather than a word that represents the content of a sentence. And, in the case of Korean, such a form is a compound noun form bound by a possessive investigation, a noun phrase form modified by an adjective form, a form of a compound noun combined with the verb 'hada' for the purpose of a noun, and has the same meaning. There are several cases in which sentences exist in different ways, such as forms with keywords that are very similar if they are not the same meaning.

Since these key facts express and search a part of a sentence having the same meaning as one key fact, it is theoretically possible to perform a more accurate search than a keyword-based information search. In order to construct such a key fact-based text information retrieval system, there must be a key fact-based text retrieval model for indexing and retrieval of key fact units. The key fact-based text retrieval model first classifies the types of key facts that can appear in document sets and query documents, and defines the weight constants of the key facts according to the types. In addition, we construct key fact-based text search models that reflect these key weight weighting constants, and should be made to show the optimal search performance with the best search model in various areas.

However, there is no proper search performance measurement method for finding key fact-based text search models that can exhibit optimal search performance in various areas and finding key fact weighting constants to be applied to such text search models.

As a result, the development of an appropriate key fact-based text information retrieval system has been delayed.

The present invention is to solve the above problems, it is to propose a key fact-based text search model and key performance weighting method for finding a key fact weighted constant that can exhibit an optimal search performance in various areas.

1 is a block diagram schematically illustrating a key fact-based text information retrieval system in which a retrieval performance measuring method of a key fact-based text retrieval model according to the present invention is implemented;

2 is a flowchart illustrating a method of measuring search performance of a key fact-based text search model according to an embodiment of the present invention.

According to the present invention for solving the above problems, a weight calculation device for calculating the weights for documents and query terms when indexing documents and query terms in units of key facts, and documents and query terms using a key fact-based text search model. A method for measuring the retrieval performance of a key fact-based text retrieval model used in a key fact-based text information retrieval system including a retrieval device displaying a search result on a display after ranking documents by calculating the similarity of . The retrieval method of retrieval performance includes setting parameters for retrieval performance such as document set size, key fact frequency in documents, and weight constant of key fact type classification, and key fact-based text retrieval model to measure retrieval performance. And setting and ranking the documents according to the document similarity values of the documents and the query by the search performance measurement results using the corresponding text search models.

Preferably, the step of setting the variable for search performance measurement includes extracting (S01) a keyword for a document set and a query word document to be searched, and updating the value of the variable for search performance measurement with respect to the extraction result; The step of setting the text search model includes constructing a vector of a document and a query including the value of the variable for the search performance measurement. In the step for setting the variable for the search performance measurement, a key fact weighting equation expressed by the following equation is given. Calculate the key fact weights of documents and queries.

Here, w is a key fact weights _{xk, xk} tf is the frequency of key facts, N is the size of the document, the document frequency df _k is the key fact, _{kfType #} C represents a key fact constant weight.

The text search model may be an internal model represented by the following equation.

The text search model may be a cosine coefficient model represented by the following equation.

The text search model may be a dice coefficient model represented by the following equation.

The text search model may be a Jakard coefficient model represented by the following equation.

These and other features and advantages of this invention will become more apparent from the following description of the preferred embodiments.

[Description of Preferred Embodiment of the Invention]

Hereinafter, with reference to the accompanying drawings will be described in detail a preferred embodiment of the search performance measurement method of the key fact-based text search model according to the present invention.

FIG. 1 schematically illustrates a key fact-based text information retrieval system for explaining an information retrieval process performed in such a system. As shown in FIG. 1, the key fact-based text information retrieval process is largely divided into a key fact extraction step, a key fact document and a query index step, and a key fact based text search model.

The key fact extraction apparatus 1a shown in FIG. It is a device for extracting key facts.

The weight calculator 1b for the document and the query word calculates the weight for the document and the query word in the step of indexing the document and the query in key fact units.

The retrieval device 1c is a part which displays the search results on the display device after ranking the documents by calculating the similarity between the documents and the query words using a key fact-based text retrieval model.

In the key fact-based text information retrieval, the text of the document set and the query word is expressed in key fact units by the key fact extractor 1a, and all the key facts are expressed in the form of [object, property]. . At this time, the types of key facts are classified as shown in Table 1 below according to the types of key facts that make up objects and attributes.

TABLE 1

As shown in Table 1, the categorized key fact types cause the text set of the document set and the query word to be represented as key facts. As a result, the key facts in the search are differentially reflected according to the types of key facts, so that high accuracy can be obtained in the key fact-based search.

As shown in Table 1, each key fact weight constant is assigned according to the key fact type. These weight constants are given differentially in the order of C _{KfType I} < C _{KfType II} < C _{KfType III} < C _{KfType IV} < C _{KfType V} < The value of the weight constant is used as an evaluation item variable in the search performance measurement of the key fact-based text search model, and is determined experimentally by considering the distribution characteristics of the key fact type of the key fact search domain.

The weight constant of the key fact is reflected in the key fact weighting equation represented by Equation 1 below and used in a key fact-based text model based on key facts.

Here, w is a key fact weights _{xk, xk} tf is the frequency of key facts, N is the size of the document, the document frequency df _k is the key fact, _{kfType #} C represents a key fact constant weight.

In general, keyword-weighted text search calculates keyword weighting considering only keyword frequency (tf), keyword document frequency (df), and document size (N). In order to perform the search, the weight factor C _{kfType #} of the key fact type is reflected in the key fact calculation formula in consideration of the importance of the search effect according to the key fact type.

Key fact-based text information retrieval requires a key fact-based text retrieval model retrieval performance measurement device. In other words, by measuring the search performance of a given key fact-based text search model, it is necessary to select a key fact-based text search model that best represents the search performance effect of information retrieval in the domain of a search document.

The search performance measurement variables that measure the search performance of the key fact-based text search model are the size of the document set (N), the key fact frequency in the document (tf _xk ), and the weight constant (C _{kfType #} ) of the key fact type classification. to be.

Key fact-based text retrieval model based on key fact weighting values includes an inner product model (Equation 2) represented by Equations 2 to 5 below, which is a search model of a vector space model, and a cosine coefficient model (Cosine). A Coefficient Model (Equation 3), a Dice Coefficient Model (Equation 4), a Jaccard Coefficient Model (Equation 5), and the like are used.

The results of retrieval performance using these models are shown as results by ranking documents according to document similarity values of documents and queries.

2 is a flowchart illustrating a search performance measurement method using a key fact-based text search model.

When the key fact-based text search is started, first, keywords for the document set and the query word document to be searched are extracted (S01), and whether the variable value of the search performance evaluation item is set for the extracted result (S02), If the answer is yes, change the current search performance evaluation environment so that the search performance measurement variables for the new document size (N), key fact frequency (tf _xk ), and key weight weighting constant (C _{KfType #} ) It is given (S03).

After the current search performance evaluation environment does not need to be changed or the change is completed, a vector of documents and queries including the values of the evaluation variable for search performance measurement is constructed (S04). By doing so, key-fact weights of documents and queries are calculated (S05). Subsequently, the actual search performance results of the key fact-based text search model for the key fact-based text search model to find the search model showing the optimal search performance according to the characteristics of the search target document domain including the evaluation variable environment are obtained. After ranking the documents (S06), the search results are displayed (S07). Subsequently, the search model showing the best search result is evaluated for the document ranking result (S08). Depending on whether the search measurement evaluation of the desired search environment has been satisfactorily performed, a conditional statement for completing or reassessing the evaluation is performed (S09).

In changing the current search performance evaluation environment (S03), if the key fact frequency (tf _xk ), which is a variable of the search performance measurement evaluation item of key fact search, is differentiated and other evaluation item variables are left as it is, By increasing the frequency of the key facts that appear 10 times, 50 times, and 100 times, we can find out which of the key fact-based retrieval models can increase the search similarity of the documents in such a document domain environment. Decisions can be made as to whether a search model can be adopted.

In addition, in the case of performing the search performance measurement of the key fact-based text search model for increasing the weight constant C _{KfType #} of the key fact type at a different rate from the order of the same ratio, In addition to selecting a search model that best reflects the ratio of the weight constants of the fact type, the value of the key fact type weight constant suitable for the environment of the search target domain can be determined.

In addition, depending on how the search performance measurement evaluation variable is set, various types of search performance measurement with the optimal key fact-based text search model according to the characteristics of the search target domain can be obtained.

While the invention has been described above based on the preferred embodiments thereof, these embodiments are intended to illustrate rather than limit the invention. It will be apparent to those skilled in the art that various changes, modifications, or adjustments to the above embodiments can be made without departing from the spirit of the invention. Therefore, the protection scope of the present invention will be limited only by the appended claims, and should be construed as including all such changes, modifications or adjustments.

As described above, according to the present invention, a key fact-based text retrieval model having a high retrieval performance as compared with keyword-based text information retrieval can be developed according to the domain characteristics of the search target document. Therefore, a key fact-based text information retrieval system suitable for each domain characteristic can be effectively developed.

Claims (8)

A weight calculation device that calculates weights for documents and queries when indexing documents and queries in key fact units, and ranks documents by calculating the similarity between documents and queries using key fact-based text search models. A method for measuring the retrieval performance of a key fact-based text retrieval model for use in a key fact-based text information retrieval system comprising a retrieval device for displaying a result on a display device, Setting variables for retrieval performance such as document size, key fact frequency in the document, and weight constants of the key fact type classification; Setting up a key fact-based text retrieval model to measure retrieval performance; And ranking the documents according to the document similarity values of the documents and the query, and displaying the results of the search performance measurement using the corresponding text search models. The method of claim 1, The setting of the search performance measurement variable includes extracting a keyword for a document set and a query word document (S01) to be searched, and updating a value of the search performance measurement variable with respect to the extraction result. The text search model setting step includes a step of constructing a vector of a document and a query including a value of a search performance measurement variable, The retrieval performance measurement method of a key fact-based text retrieval model according to claim 2, wherein the variable setting step for retrieval performance is calculated by calculating a key fact weight value of a document and a query by a key fact weighting equation represented by the following equation. Where w _xk is the key fact weight, tf _xk is the frequency of the key fact, N is the size of the document, df _k is the document frequency of the key fact, and C _{kfType #} is the key fact weight constant. The method according to claim 1 or 2, A search method of measuring performance of a key fact-based text search model, wherein the text search model is an internal model represented by the following equation. The method according to claim 1 or 2, And a text search model is a cosine coefficient model represented by the following equation. The method according to claim 1 or 2, The search method for measuring the search performance of a key fact-based text search model, characterized in that the text search model is a dice coefficient model represented by the following equation. The method according to claim 1 or 2, And a text search model is a Jakard coefficient model represented by the following equation. The method according to claim 1 or 2, The method of measuring the search performance of a key fact-based text search model, characterized in that in the step of updating the value of the search performance measurement variable only changes the frequency of the key fact. The method according to claim 1 or 2, And updating only the weight constant of the key fact type in a step of updating the value of the search performance measurement variable.