KR101985902B1 - A method and computer program for inferring metadata of a text contents creator considering morphological and syllable characteristics - Google Patents

Abstract

According to an embodiment of the present invention, a method for inferring metadata of a text content creator in consideration of morphological and syllable characteristics comprises: a step of receiving text content; a step of dividing the text content into one or more morphemes, and determining a morpheme vector from the divided one or more morphemes based on a morpheme-vector converter; a step of dividing the text content into one or more syllables, and determining a syllable vector from the divided one or more syllables based on a syllable-vector converter; and a step of determining a metadata vector corresponding to metadata of a creator based on the morpheme vector, the syllable vector, and a creator recognizer. The step of determining the metadata vector includes: a step of merging the morpheme vector and the syllable vector to create a content vector; and a step of determining the metadata vector based on the content vector and the creator recognizer.

Description

[0001] METHOD AND COMPUTER PROGRAM FOR INFERRING METADATA OF A TEXT CONTENTS [0002] The present invention relates to a method and apparatus for inferring meta information of a text content creator in consideration of morphological features and syllable characteristics,

Embodiments of the present invention relate to a method and computer program for inferring meta information of a text content creator, and more particularly, to a method for inferring meta information of a content creator based on morpheme and syllable of text content.

2. Description of the Related Art [0002] With the rapid development of information communication technologies, various types of terminals such as mobile communication terminals, personal computers, and the like have been implemented to perform various functions.

In particular, such terminals are implemented to use various functions such as generating secondary information from received information, in a conventional function of simply receiving and displaying information. In such an environment, the amount of content posted on the web is increasing exponentially, which makes it difficult to grasp the attributes of individual contents (for example, to understand the age and gender of the creator).

The present invention intends to deduce the author's meta information based on the morphological characteristics of the text content.

Also, the present invention intends to deduce the author's meta information based on the syllable characteristic of the text content, that is, the dialogue (or speech) of the text content, and deduce the author's meta information with higher accuracy.

In addition, the present invention intends to deduce the author's meta information in consideration of both morphological features and syllable characteristics of text contents.

A method for inferring meta information of a text content creator according to an embodiment of the present invention includes: receiving text content; Dividing the textual content into one or more morphemes, and determining a morpheme vector from the one or more morphemes based on the morpheme-vector transformer; Dividing the textual content into one or more syllables, and determining a syllable vector from the segmented one or more syllables based on a syllable-vector converter; And determining a meta information vector corresponding to the author's meta information based on the morpheme vector, the syllable vector, and the creator recognizer. Wherein the morpheme-vector converter is a data set expressing a correlation between a plurality of morphemes and a plurality of morpheme vectors, the syllable-vector converter is a data set expressing a correlation between a plurality of syllables and a plurality of syllable vectors, May be a plurality of morpheme vectors and a data set expressing a correlation between a plurality of syllable vectors and a plurality of meta information vectors.

The method of inferring the author's meta information may further include generating one or more divided contents by dividing the text contents into predetermined units after the step of receiving the text contents. Wherein determining the morphological vector comprises determining a morphological vector for each of the one or more segmented content, wherein determining the syllable vector comprises determining a syllable vector for each of the at least one segmented content, The determining may determine a meta information vector corresponding to the author's meta information based on the morphological vector of each of the one or more sub-content, the syllable vector of each of the one or more sub-content, and the creator recognizer.

The method for inferring the author's meta information includes: determining meta information of a creator for each of the at least one divided contents based on a meta information vector for each of the at least one divided contents after determining the meta information vector; And determining meta information of the text content creator based on the meta information of the creator of the at least one divided content. The predetermined unit may be a sentence unit.

The method of inferring the author's meta information may further include determining meta information of the text content creator based on the determined meta information vector after determining the meta information vector.

The meta information of the text content creator may include at least one of the age range of the creator, the gender of the creator, the region related to the creator, the political tendency of the creator, the academic background of the creator, and the marriage of the creator.

Wherein the step of determining meta information of the creator comprises the steps of: determining the age range of the text content creator in the form of a probability that the creator belongs to a plurality of candidate age ranges; The gender of the text content creator can be determined.

The method for inferring the author's meta information comprises: learning the morpheme-vector converter based on a plurality of first learning data including a first test morpheme and a first morpheme vector corresponding to the first test morpheme; Learning the syllable-vector converter based on a plurality of second learning data including a first test syllable and a first syllable vector corresponding to the first test syllable; And learning the author recognizer based on third learning data including a second morpheme vector, a second syllable vector, and a meta information vector corresponding to the second morpheme vector and the second syllable vector .

Wherein the determining the meta information vector comprises: combining the morpheme vector and the syllable vector to generate a content vector; And determining the meta information vector based on the content vector and the creator recognizer.

A method of inferring meta information of a text content creator in consideration of morphological features and syllable characteristics according to an embodiment of the present invention includes: receiving text content; Dividing the textual content into one or more morphemes, and determining a morpheme vector from the one or more morphemes based on the morpheme-vector transformer; Dividing the textual content into one or more syllables, and determining a syllable vector from the segmented one or more syllables based on a syllable-vector converter; And determining a meta information vector corresponding to the author's meta information based on the morpheme vector, the syllable vector, and the creator recognizer, wherein the determining the meta information vector comprises: Merging to generate a content vector; And determining the meta information vector based on the content vector and the creator recognizer.

Other aspects, features, and advantages will become apparent from the following drawings, claims, and detailed description of the invention.

According to embodiments of the present invention, the author's meta information can be inferred based on the morphological characteristics of the text content.

In addition, the author's meta information can be inferred based on the syllable characteristic of the text content, that is, the dialogue (or speech) of the text content, and the author's meta information can be deduced with higher accuracy.

In addition, we can deduce the author 's meta information considering both morphological and syllable characteristics of text contents.

FIG. 1 schematically illustrates a content creator meta information reasoning system according to an embodiment of the present invention.
FIG. 2 schematically shows a configuration of a creator meta information inference apparatus according to an embodiment of the present invention.
FIGS. 3A to 3C are diagrams for explaining a method of generating / learning a morpheme-vector converter, a syllable-vector converter, and a creator recognizer according to an author meta information inference apparatus according to an embodiment of the present invention.
4 is a diagram for explaining a method of dividing a text content including a plurality of sentences into divided contents according to an embodiment of the present invention.
FIG. 5 is a diagram for explaining a method of inferring author's meta information from a text content (or a divided content) according to an embodiment of the present invention.
FIG. 6 is a flowchart illustrating a method for inferring meta information of a text content creator by a creator meta information inference apparatus according to an exemplary embodiment of the present invention. Referring to FIG.
FIG. 7 illustrates an example of a screen displayed on a display unit of a user terminal according to an exemplary embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention and methods of achieving them will be apparent with reference to the embodiments described in detail below with reference to the drawings. However, the present invention is not limited to the embodiments described below, but may be implemented in various forms.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals refer to like or corresponding components throughout the drawings, and a duplicate description thereof will be omitted .

In the following embodiments, the terms first, second, and the like are used for the purpose of distinguishing one element from another element, not the limitative meaning. In the following examples, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. In the following embodiments, terms such as inclusive or possessive are intended to mean that a feature, or element, described in the specification is present, and does not preclude the possibility that one or more other features or elements may be added. In the drawings, components may be exaggerated or reduced in size for convenience of explanation. For example, the sizes and shapes of the respective components shown in the drawings are arbitrarily shown for convenience of explanation, and thus the present invention is not necessarily limited to those shown in the drawings.

FIG. 1 schematically illustrates a content creator meta information reasoning system according to an embodiment of the present invention.

Referring to FIG. 1, a content creator meta information reasoning system according to an embodiment of the present invention may include a server 100, a user terminal 200, an external device 300, and a communication network 400 connecting them .

The content creator meta information reasoning system according to an embodiment of the present invention is a system in which the server 100 receives text content from the user terminal 200 and / or the external device 300, and infer the meta information of the creator of the received content can do. Also, the content creator meta information reasoning system can allow the server 100 to acquire content that the author's meta information is known in advance from the user terminal 200 and / or the external device 300, . A more detailed description will be given later.

In the present invention, 'author's meta information' may mean personal information or personal information about the creator such as the age group, sex, residence area, political inclination, etc. of the creator.

In the present invention, 'text content' may mean various contents including text in at least a part of the contents. For example, text content may mean content that contains only text. In this case, the server 100 according to an embodiment of the present invention can infer the author's meta information by analyzing the entire text content. On the other hand, the text content may mean content including content such as images, images, etc. in addition to text. In this case, the server 100 according to an embodiment of the present invention may separate only the text from the content and infer the content creator's meta information therefrom. Hereinafter, for convenience of explanation, it is assumed that the text content includes only text.

In the present invention, the user terminal 200 may refer to various devices capable of transmitting and receiving the above-described text contents to and from the server 100. In this case, the terminal may be the personal computer 202 or the portable terminal 201. 1, the portable terminal 201 is shown as a smart phone, but the spirit of the present invention is not limited thereto.

On the other hand, the user terminal 200 may include display means for displaying the content, and input means for obtaining a user's input on the content. At this time, the input means and the display means can be configured in various ways. For example, the input means may include, but is not limited to, a keyboard, a mouse, a trackball, a microphone, a button, a touch panel,

The external device 300 may refer to various devices that transmit and receive data to and from the server 100 and / or the user terminal 200 via the communication network 400. [

According to an embodiment of the present invention, the external device 300 may be an apparatus that provides learning data for learning a creator recognizer provided in the server 100. [ For example, the external device 300 may be a server that provides content (e.g., a comment on a newspaper article or an article) and meta information of a creator of the content (e.g., meta information of a reporter who wrote the article or meta information of a user who wrote the comment) . The external device 300 may be a single number or a plurality.

According to another embodiment of the present invention, the external device 300 transmits the identification information on the text content to be provided to the user terminal 200 by the external device 300 itself to the server 100, Or may be a device that receives meta information of the text content creator. For example, the external device 300 may be a service-based server that provides a service for providing meta information about articles for which users want to know the author's meta information. However, both of the above-described cases are exemplary, and the spirit of the present invention is not limited thereto.

In the present invention, the communication network 400 connects the server 100, the user terminal 200, and the external device 300. For example, the communication network 400 provides a connection path so that the user terminal 200 can transmit and receive packet data after connecting to the server 100. The communication network 400 may be a wired network such as LANs (Local Area Networks), WANs (Wide Area Networks), MANs (Metropolitan Area Networks), ISDNs (Integrated Service Digital Networks), wireless LANs, CDMA, Bluetooth, But the scope of the present invention is not limited thereto.

In the present invention, the server 100 may receive the text content from the user terminal 200 and / or the external device 300 and infer the meta information of the received content creator. In addition, the server 100 may acquire one or more learning contents marked with meta information from the user terminal 200 and / or the external device 300, and train the creator recognizer based thereon. To this end, the server 100 may include an author meta information inference device as shown in FIG.

FIG. 2 schematically shows a configuration of a creator meta information inference apparatus 110 according to an embodiment of the present invention.

2, the author meta information inference apparatus 110 according to the present embodiment may include a communication unit 111, a control unit 112, and a memory 113. Also, although not shown in the figure, the author meta information inference apparatus 110 according to the present embodiment may further include an input / output unit, a program storage unit, and the like.

The communication unit 111 is a communication unit that allows the author meta information inference apparatus 110 to transmit and receive a signal such as a control signal or a data signal through a wired connection with another network apparatus such as the user terminal 200 and / Hardware, and software.

The control unit 112 may include any type of device capable of processing data, such as a processor. Herein, the term 'processor' may mean a data processing device embedded in hardware, for example, having a circuit physically structured to perform a function represented by a code or an instruction contained in the program. As an example of the data processing apparatus built in hardware, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an ASIC (Application-Specific Integrated Circuit, and an FPGA (Field Programmable Gate Array), but the scope of the present invention is not limited thereto.

The memory 113 performs a function of temporarily or permanently storing the data processed by the author meta-information inferring apparatus 110. The memory 113 may include a magnetic storage medium or a flash storage medium, but the scope of the present invention is not limited thereto.

Although the author meta information inference apparatus 110 is provided in the server 100 in the following description, the author meta information inference apparatus 110 may be provided separately from the server 100 according to role allocation.

Meanwhile, as described above, the server 100, that is, the author meta information inference apparatus 110, receives the text content for inferring the author's meta information from the user terminal 200 and / or the external apparatus 300, The meta information of the creator of the text content can be deduced. The creator meta information inference device 110 may acquire one or more learning contents marked with meta information of the creator from the user terminal 200 and / or the external device 300, and train the creator recognizer based on this.

Hereinafter, a method by which the author meta information inference apparatus 110 generates and / or learns a creator recognizer from one or more learning contents will be described first, and a creator identifier for a text content received based on the creator recognizer generated and / Will be described later.

In the present invention, the 'author recognizer' may be a data set representing a correlation between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors. That is, the author recognizer may represent a correspondence relationship between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors. In this case, the morpheme vector may be a vector generated based on the morphological analysis of the text content in which the author's meta information is to be inferred. The syllable vector may also be a vector generated based on a syllable analysis for the text content for which the author's meta information is to be inferred. Meanwhile, the meta information vector is a vector corresponding to the meta information of the creator of the text content, and may be a vector including information on various items (for example, age group, sex, residence area, political orientation, etc.).

Such 'author recognizer' may be generated by machine learning based on a plurality of learning data. At this time, the learning data may be received from the external device 300 described above. Of course, the learning data may be stored in the memory 113 of the meta information reasoning device 110. [

Meanwhile, the author recognizer may be a data set as described above. For example, the author recognizer may be a data set comprising a plurality of numbers, such as a matrix. However, the present invention is not limited thereto.

Similar to the above-described author recognizer, the 'morpheme-vector converter' may be a data set expressing a correlation between a plurality of morphemes and a plurality of morpheme vectors. The 'syllable-vector converter' may be a data set expressing a correlation between a plurality of syllables and a plurality of syllable vectors.

Both the morpheme-vector converter and the syllable-vector converter described above may be generated by machine learning based on a plurality of learning data similar to the author recognizer. For example, in the case of a morpheme-vector converter, it may be generated based on learning data including a morpheme and a morpheme vector corresponding to the morpheme. Similarly, in the case of a syllable-vector converter, it may be generated based on learning data including a certain syllable and a syllable vector corresponding to the syllable.

FIGS. 3A through 3C are diagrams illustrating a method of generating and / or learning an author meta information inference apparatus 110 according to an exemplary embodiment of the present invention, which includes a morphological-vector converter 520, a syllable-vector converter 620, and a creator recognizer 720 Fig.

Referring to FIG. 3A, the controller 112 of the author meta information inference apparatus 110 according to an embodiment of the present invention generates a morpheme-to-vector converter 520 using the first test morpheme and the first test morpheme A plurality of first learning data 500 including a corresponding first morpheme vector may be received and / or obtained. In other words, the controller 112 according to an embodiment of the present invention can acquire learning data including a morpheme and a morpheme vector corresponding to the morpheme. For example, in the case of the first learning data 510, a morpheme vector 511 corresponding to the morpheme and the morpheme may be included. Here, the number of morphemes corresponding to any one morpheme vector may be a single number or a plurality of morphemes.

The controller 112 according to an embodiment of the present invention may learn the morphological-vector converter 520 based on the received and / or obtained first learning data 500.

As described above, the morpheme-vector converter 520 may be a correlation between a plurality of morphemes generated by a machine learning technique and a plurality of morpheme vectors, that is, mapping information between them.

Therefore, the control unit 112 according to an embodiment of the present invention controls the data set of the morpheme-vector converter 520 so that the morpheme of the first learning data 500 and the morpheme vectors corresponding to the morpheme can be mapped (mapped) Vector converter 520 in a manner such that the morphological-vector converter 520 is updated.

For example, when the data set of the morpheme-vector converter 520 is composed of a plurality of coefficients, the control unit 112 maps the morpheme of the first learning data 500 and the morpheme vectors corresponding to the morpheme corresponding to each other The coefficients constituting the morpheme-vector converter 520 can be appropriately adjusted. At this time, the control unit 112 can acquire the morpheme-vector converter 520 with improved accuracy by updating the coefficients based on the plurality of learning data.

Thus, the present invention can make it possible to deduce the author's meta information based on the morphological feature of the text content.

Referring to FIG. 3B, a controller 112 according to an exemplary embodiment of the present invention includes a plurality of syllable-to- And / or acquire second learning data 600 of FIG. In other words, the controller 112 according to an embodiment of the present invention can acquire the second learning data 600 including the syllable and the syllable vector corresponding to the syllable. For example, in the case of the first learning data 610, a syllable vector 611 corresponding to syllables and syllables may be included. Here, the number of syllables corresponding to any one syllable vector may be a single number or a plurality of syllables.

Then, the controller 112 according to an embodiment of the present invention can learn the syllable-vector converter 620 based on the second learning data 600 received and / or obtained.

As described above, the syllable-vector converter 620 may be a correlation between a plurality of syllables generated by a machine learning technique and a plurality of syllable vectors, that is, mapping information of both syllables.

Accordingly, the control unit 112 according to an embodiment of the present invention may include a data set of the syllable-vector converter 620 so that the syllable of the second learning data 600 and the syllable vector corresponding to the syllable can be mapped (mapped) The syllable-to-vector converter 620 can be learned by updating the syllable-to-

For example, when the data set of the syllable-vector converter 620 is composed of a plurality of coefficients, the controller 112 maps the syllable of the second learning data 600 and the syllable vector corresponding to the syllable, The syllable-to-vector converter 620 may appropriately adjust the coefficients constituting the syllable-vector converter 620 so that the syllable- At this time, the control unit 112 can acquire the syllable-vector converter 620 with a higher accuracy by updating the coefficients based on the plurality of learning data.

Thus, according to the present invention, the author's meta information can be deduced based on the syllable characteristic of the text content, that is, the dialogue (or speech) of the text content, and the author's meta information can be deduced with higher accuracy have.

3C, a controller 112 according to an embodiment of the present invention corresponds to a second morpheme vector, a second syllable vector, a second morpheme vector, and a second syllable vector for generation of the creator recognizer 720 And / or acquire a plurality of third learning data 700 including a meta information vector to be obtained. In other words, the controller 112 according to an embodiment of the present invention can acquire learning data including a morphological vector and a syllable vector, and a meta information vector corresponding to the morphological vector and the syllable vector.

For example, in the case of the first learning data 710, a morpheme vector Vm2, a syllable vector Vs2, and a meta information vector 711 corresponding thereto may be included.

Then, the controller 112 according to an embodiment of the present invention can learn the creator recognizer 720 based on the received and / or obtained third learning data 700. [

As described above, the author recognizer 720 may be a correlation between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors generated by a machine learning technique, that is, mapping information of both.

Therefore, the control unit 112 according to an embodiment of the present invention may update the data set of the author recognizer 720 so that the morphological vector of the learning data and the syllable vector and the meta information vector can correspond to each other (map) The author recognizer 720 can be learned.

For example, when the data set of the author recognizer 720 is composed of a plurality of coefficients, the controller 112 may map the morphological vector of the learning data and the syllable vector and the corresponding meta information vector to each other The coefficients constituting the creator recognizer 720 can be appropriately adjusted. At this time, the control unit 112 can acquire the creator recognizer 720 with improved accuracy by updating the coefficients based on the plurality of learning data.

Thus, the present invention allows the author's meta information to be inferred in consideration of both morphological features and syllable characteristics of the text content.

The controller 112 according to an embodiment of the present invention includes a morpheme-vector converter 520, a syllable-vector converter 620, and a creator recognizer 720 for inferring the author's meta information from the text content ) Can be generated and / or learned.

Hereinafter, a method for inferring meta information of a text content creator based on the morphological-vector converter 520, syllable-vector converter 620, and creator recognizer 720 generated and / or learned by the above-described process will be described .

The control unit 112 according to an embodiment of the present invention may receive the text content for inferring the author's meta information from the user terminal 200 and / or the external device 300. At this time, the text content may include various kinds of contents (e.g., images, images, etc.) in addition to the text content as described above.

On the other hand, the text content may include only one sentence or may include a plurality of sentences. If the text content includes a plurality of sentences, the control unit 112 according to an embodiment of the present invention may divide the text content into a plurality of contents to infer the author's meta information.

4 is a diagram for explaining a method of dividing text content 800 including a plurality of sentences into divided contents 810, 820 and 830 according to an embodiment of the present invention.

For convenience of explanation, it is assumed that the text content 800 includes a plurality of sentences as described above. In this case, the controller 112 according to an embodiment of the present invention may generate the one or more divided contents 810, 820, and 830 by dividing the text content 800 into predetermined units. The predetermined unit may be a sentence unit, a paragraph unit, or a topic unit. However, the present invention is not limited thereto.

Hereinafter, for convenience of explanation, a method of deducing the author's meta information for one divided content 810 will be mainly described.

5 is a diagram for explaining a method of the controller 112 inferring the author's meta information from the text content 810 (or the divided content) according to an embodiment of the present invention.

As described above, the control unit 112 according to an embodiment of the present invention may receive or acquire text content, and may divide it into predetermined units, if necessary, to generate one or more divided contents.

The control unit 112 may divide the text content 810 into one or more morphemes 811 according to an embodiment of the present invention. For example, the control unit 112 may divide the content into one or more morphemes such as' Korea, '' peace, '' for the purpose, 'and' please help 'if the text content is' . The control unit 112 according to an embodiment of the present invention can determine the morpheme vector 821 from one or more morphemes 811 that are divided based on the morpheme-vector converter 520. [

As described above, the morpheme-vector converter 520 may be a correlation between a plurality of morphemes generated by a machine learning technique and a plurality of morpheme vectors, that is, mapping information between them.

Accordingly, the control unit 112 may input one or more morphemes 811 to the morpheme-vector converter 520 and obtain a morpheme vector 821 corresponding to one or more morphemes 811 as a result.

Thus, the present invention can deduce the author's meta information based on the morphological characteristics of the text content.

The control unit 112 according to an embodiment of the present invention may divide the text content 810 into one or more syllables 812. [ For example, as in the above-described example, when the text content is' Please do it for the peace of Korea, 'the control unit 112 sets the content to one or more syllables such as' Daejun', 'Han', 'Min', ' Can be divided. The controller 112 according to an embodiment of the present invention may determine the syllable vector 822 from one or more syllable segments 812 that are segmented based on the syllable-vector converter 620.

As described above, the syllable-vector converter 620 may be a correlation between a plurality of syllables generated by a machine learning technique and a plurality of syllable vectors, that is, mapping information of both syllables.

Accordingly, the control unit 112 may input one or more syllables 812 to the syllable-vector converter 620 and thereby obtain one or more syllables 812 and a syllable vector 822 corresponding thereto.

Thus, the present invention can deduce the author's meta information based on the syllable characteristic of the text content, that is, the dialogue (or speech) of the text content, and can infer the author's meta information with higher accuracy.

The control unit 112 according to an embodiment of the present invention generates a meta information vector corresponding to the author's meta information based on the morpheme vector 821, syllable vector 822 and creator recognizer 720 determined by the above- 830).

Similar to the morpheme-vector converter 520 and the syllable-to-vector converter 620 described above, the author recognizer 720 also includes a plurality of morpheme vectors and a plurality of syllable vectors generated by a Machine Learning technique, A correlation of information vectors, that is, mapping information of both.

The control unit 112 inputs the morpheme vector 821 and the syllable vector 822 to the creator recognizer 720 and outputs the meta information vector 830 corresponding to the morpheme vector 821 and syllable vector 822 as a result, Can be obtained.

Meanwhile, the controller 112 according to an embodiment of the present invention may generate a content vector by merging the morpheme vector and the syllable vector, and determine the meta information vector 830 based on the generated content vector and the creator recognizer 720 have. In this case, merging a vector may mean generating a new vector corresponding to the sum of the number of dimensions of two vectors, and generating a vector of a new dimension (a dimension equal to or less than the sum of the dimensions of two vectors) through a predetermined operation It may mean to do.

The control unit 112 according to an embodiment of the present invention performs a series of processes (a process of determining a morphological vector, a syllable vector, and a meta information vector) on the divided content generated by dividing text content into predetermined units . In other words, the control unit 112 can determine a morphological vector for each of the one or more divided contents. The control unit 112 may also determine a syllable vector for each of the one or more sub-content. In addition, the control unit 112 can determine a meta information vector corresponding to the creator's meta information based on the morphological vector of each of the one or more divided contents, the syllable vector of each of the one or more divided contents, and the creator recognizer. In this way, the control unit 112 can determine the meta information vector for each divided content. On the other hand, a method of determining author's meta information for the entire contents will be described later.

The controller 112 according to an embodiment of the present invention can determine the meta information 840 of the creator of the text content 810 based on the meta information vector 830 determined by the above process. At this time, the meta information of the content creator may include at least one of the age range of the creator, the gender of the creator, the region related to the creator, the political tendency of the creator, the academic background of the creator, and the marriage of the creator. Of course, the above-mentioned items are exemplary and can be used as meta information of the present invention if they are items that can be used as meta information of a person.

The controller 112 according to an embodiment of the present invention can determine meta information for each item of meta information in a probability form of a plurality of options for each item. For example, the control unit 112 may determine the age range of the text content creator in the form of a probability that the creator belongs to a plurality of candidate age groups (10, 20, 30, 40, 50, etc.). Similarly, the control unit 112 can determine the gender of the creator in the form of a probability that the creator is male and a probability that the creator is female. However, the above-mentioned items of age group and gender are illustrative and not intended to limit the scope of the present invention.

On the other hand, when the meta information vector is determined for each of the divided contents, the controller 112 according to an embodiment of the present invention can determine meta information for each divided content.

At this time, the controller 112 according to an embodiment of the present invention may determine the meta information about the full text content by merging the meta information about each divided content. For example, when the probability that the creator belongs to a plurality of candidate ages is determined for each of the divided contents, the control unit 112 calculates the sum of the probabilities of the age groups (for example, the sum of the probabilities belonging to the teenagers, Meta information about the text content can be determined. However, the present invention is not limited thereto.

Thus, the present invention can deduce the author's meta information in consideration of both morphological features and syllable characteristics of the text content.

FIG. 6 is a flowchart illustrating a method by which the author meta information inference apparatus 110 inferences meta information of a text content creator according to an embodiment of the present invention. Hereinafter, the description of the contents overlapping with those described in Figs. 1 to 5 will be omitted.

The author meta information inference apparatus 110 according to an embodiment of the present invention can generate / learn a morpheme-vector converter, a syllable-vector converter, and a creator recognizer (S61)

In the present invention, the 'author recognizer' may be a data set representing a correlation between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors. That is, the author recognizer may represent a correspondence relationship between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors. In this case, the morpheme vector may be a vector generated based on the morphological analysis of the text content in which the author's meta information is to be inferred. The syllable vector may be a vector generated based on a syllable analysis for the text content in which the author's meta information is to be inferred. Meanwhile, the meta information vector is a vector corresponding to the meta information of the creator of the text content, and may be a vector including information on various items (for example, age, sex, residence area, etc.).

Such 'author recognizer' may be generated by machine learning based on a plurality of learning data. At this time, the learning data may be received from the external device 300 described above. Of course, the learning data may be stored in the memory 113 of the meta information reasoning device 110. [

Meanwhile, the author recognizer may be a data set as described above. For example, the author recognizer may be a data set comprising a plurality of numbers, such as a matrix. However, the present invention is not limited thereto.

Similar to the above-described author recognizer, the 'morpheme-vector converter' may be a data set expressing a correlation between a plurality of morphemes and a plurality of morpheme vectors. The 'syllable-vector converter' may be a data set expressing a correlation between a plurality of syllables and a plurality of syllable vectors.

Both the morpheme-vector converter and the syllable-vector converter described above may be generated by machine learning based on a plurality of learning data similar to the author recognizer. For example, in the case of a morpheme-vector converter, it may be generated based on learning data including a morpheme and a morpheme vector corresponding to the morpheme. Similarly, in the case of a syllable-vector converter, it may be generated based on learning data including a certain syllable and a syllable vector corresponding to the syllable.

Referring again to FIGS. 3A through 3C, a method for generating / learning the meta information inference apparatus 110 generates the morpheme-vector converter 520, the syllable-vector converter 620, and the creator recognizer 720 will be described.

3A, the author meta information inference apparatus 110 of the author meta information inference apparatus 110 according to an embodiment of the present invention includes a first test morpheme for generating the morpheme-vector converter 520, A plurality of first learning data 500 including a first morpheme vector corresponding to one test morpheme can be received and / or obtained. In other words, the author meta information inference apparatus 110 according to an embodiment of the present invention can acquire learning data including a morpheme and a morpheme vector corresponding to the morpheme. For example, in the case of the first learning data 510, a morpheme vector 511 corresponding to the morpheme and the morpheme may be included. Here, the number of morphemes corresponding to any one morpheme vector may be a single number or a plurality of morphemes.

Then, the author meta information inference apparatus 110 according to an embodiment of the present invention can learn the morpheme-vector converter 520 based on the received and / or obtained first learning data 500.

As described above, the morpheme-vector converter 520 may be a correlation between a plurality of morphemes generated by a machine learning technique and a plurality of morpheme vectors, that is, mapping information between them.

Therefore, the author meta information inference apparatus 110 according to an embodiment of the present invention includes a morpheme-vector converter 520 (corresponding to the morpheme-vector converter) so that the morpheme of the first learning data 500 and the morpheme vectors corresponding to the morpheme can be mapped Vector converter 520 in a manner that updates the data set of the morpheme-vector converter 520. [

For example, when the data set of the morpheme-vector converter 520 is composed of a plurality of weights, the author meta information inference apparatus 110 calculates the morpheme of the first learning data 500 and the morpheme vector corresponding to the morpheme The coefficients constituting the morpheme-vector converter 520 can be appropriately adjusted so as to correspond to each other. At this time, the author meta information inference apparatus 110 can acquire the morpheme-vector converter 520 with improved accuracy by updating the coefficients based on the plurality of learning data.

Thus, the present invention can make it possible to deduce the author's meta information based on the morphological feature of the text content.

Referring to FIG. 3B, the author meta information inference apparatus 110 according to an embodiment of the present invention generates a syllable-vector converter 620 by generating a first test syllable and a first syllable vector corresponding to a first test syllable And / or acquire a plurality of second learning data 600, In other words, the author meta information inference apparatus 110 according to an embodiment of the present invention can acquire second learning data 600 including a syllable and a syllable vector corresponding to the syllable. For example, in the case of the first learning data 610, a syllable vector 611 corresponding to syllables and syllables may be included. Here, the number of syllables corresponding to any one syllable vector may be a single number or a plurality of syllables.

Then, the author meta information inference apparatus 110 according to an embodiment of the present invention can learn the syllable-vector converter 620 based on the second learning data 600 received and / or obtained.

As described above, the syllable-vector converter 620 may be a correlation between a plurality of syllables generated by a machine learning technique and a plurality of syllable vectors, that is, mapping information of both syllables.

Therefore, the author meta information inference apparatus 110 according to an embodiment of the present invention includes a syllable-vector converter 620 so that syllables of the second learning data 600 and syllable vectors corresponding to the syllables can be mapped to each other The syllable-to-vector converter 620 can be learned by updating the data set of the syllable-to-vector converter 620.

For example, when the data set of the syllable-vector converter 620 is composed of a plurality of coefficients, the author meta information inference apparatus 110 calculates the syllable of the second learning data 600 and the syllable vector corresponding to the syllable The coefficients constituting the syllable-vector converter 620 can be appropriately adjusted so that they can be mapped (mapped) to each other. At this time, the author meta-information inference apparatus 110 can acquire the syllable-vector converter 620 with improved accuracy by updating the coefficients based on the plurality of learning data.

Thus, according to the present invention, the author's meta information can be deduced based on the syllable characteristic of the text content, that is, the dialogue (or speech) of the text content, and the author's meta information can be deduced with higher accuracy have.

3C, the author meta information inference apparatus 110 according to an exemplary embodiment of the present invention includes a second morpheme vector, a second syllable vector, and a second morpheme vector for generating the author identifier 720, It is possible to receive and / or acquire a plurality of third learning data 700 including a meta information vector corresponding to a syllable vector. In other words, the author meta information inference apparatus 110 according to an embodiment of the present invention can acquire learning data including a morphological vector and a syllable vector, and a meta information vector corresponding to the morphological vector and the syllable vector.

For example, in the case of the first learning data 710, a morpheme vector Vm2, a syllable vector Vs2, and a meta information vector 711 corresponding thereto may be included.

Then, the author meta information inference apparatus 110 according to an embodiment of the present invention can learn the author recognizer 720 based on the received and / or obtained third learning data 700.

As described above, the author recognizer 720 may be a correlation between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors generated by a machine learning technique, that is, mapping information of both.

Therefore, the author meta information inference apparatus 110 according to an embodiment of the present invention updates (updates) the data set of the author recognizer 720 so that morphological vectors of the learning data and syllable vectors and meta information vectors can be mapped The author recognizer 720 can be learned in such a manner that the creator recognizer 720 is updated.

For example, when the data set of the author recognizer 720 is composed of a plurality of coefficients, the author meta information inference apparatus 110 maps the morphological vector of the learning data and the syllable vector and the corresponding meta information vector to each other ) Of the creator recognizer 720 so as to be able to adjust the values of the coefficients. At this time, the author meta information inference apparatus 110 can acquire the creator recognizer 720 with improved accuracy by updating the coefficients based on the plurality of learning data.

Thus, the present invention allows the author's meta information to be inferred in consideration of both morphological features and syllable characteristics of the text content.

According to the process described above, the author meta information inference apparatus 110 according to an embodiment of the present invention includes a morpheme-vector converter 520, a syllable-vector converter 620, and a syllable- The creator recognizer 720 can be generated and / or learned.

The creator's meta information inference apparatus 110 according to the embodiment of the present invention can receive the text content for inferring the author's meta information (S62). At this time, as described above, Content (e.g., images, images, etc.).

On the other hand, the text content may include only one sentence or may include a plurality of sentences. The author meta information inference apparatus 110 according to an embodiment of the present invention may divide the text content into a plurality of contents and infer the author's meta information.

Hereinafter, steps S63 to S66 will be described with reference to FIG.

The author meta information inference apparatus 110 according to an embodiment of the present invention can divide the text content 810 into one or more morphemes 811 and determine a morpheme vector 821 from the divided morpheme 811 (S63)

For example, the author meta-information reasoning device 110 may determine whether the text content is' please do it for the peace of Korea, 'such as' Korea,' 'peace,' 'for the purpose, And the morpheme vector 821 can be determined based thereon.

As described above, the morpheme-vector converter 520 may be a correlation between a plurality of morphemes generated by a machine learning technique and a plurality of morpheme vectors, that is, mapping information between them.

Thus, the author meta information inference device 110 may input one or more morphemes 811 into the morpheme-vector converter 520 and obtain a morpheme vector 821 corresponding to one or more morphemes 811 as a result .

Thus, the present invention can deduce the author's meta information based on the morphological characteristics of the text content.

The author meta information inference device 110 according to an embodiment of the present invention can divide text content 810 into one or more syllables 812 and determine syllable vectors 822 from the one or more syllable segments 812 (S64)

The creator meta information inference device 110 may generate the content such as' Dae ',' Han ',' Min ',' Country ', etc. if the text content is' It may be divided into one or more syllables and the syllable vector 822 may be determined based thereon.

As described above, the syllable-vector converter 620 may be a correlation between a plurality of syllables generated by a machine learning technique and a plurality of syllable vectors, that is, mapping information of both syllables.

Thus, author meta information inference device 110 may input one or more syllables 812 into syllable-vector converter 620 and thereby obtain syllable vectors 822 corresponding to one or more syllables 812 .

Thus, the present invention can deduce the author's meta information based on the syllable characteristic of the text content, that is, the dialogue (or speech) of the text content, and can infer the author's meta information with higher accuracy.

The author meta information inference apparatus 110 according to an embodiment of the present invention generates a meta information corresponding to the creator's meta information based on the morpheme vector 821, syllable vector 822 and creator recognizer 720 determined by the above- The meta information vector 830 can be determined (S65)

Similar to the morpheme-vector converter 520 and the syllable-to-vector converter 620 described above, the author recognizer 720 also includes a plurality of morpheme vectors and a plurality of syllable vectors generated by a Machine Learning technique, A correlation of information vectors, that is, mapping information of both.

The author meta information inference apparatus 110 inputs the morpheme vector 821 and the syllable vector 822 to the author recognizer 720 and outputs meta information 822 corresponding to the morpheme vector 821 and syllable vector 822 Vector 830 can be obtained. Meanwhile, the author meta information inference apparatus 110 according to an embodiment of the present invention generates a content vector by merging the morpheme vector and the syllable vector, and generates a meta information vector 830 (830) based on the generated content vector and the creator recognizer 720. [ ) May be determined. In this case, merging a vector may mean generating a new vector corresponding to the sum of the number of dimensions of two vectors, and generating a vector of a new dimension (a dimension equal to or less than the sum of the dimensions of two vectors) through a predetermined operation It may mean to do.

The creator meta information inference apparatus 110 according to an embodiment of the present invention performs a series of processes (a morphological vector, a syllable vector, and a meta information vector for the divided content generated by dividing a text content into predetermined units Process) can be performed in the same manner. In other words, the author meta information inference apparatus 110 may determine a morphological vector for each of the one or more divided contents. The author meta information inference device 110 may also determine a syllable vector for each of the one or more sub-content items. The author meta information inference apparatus 110 can also determine a meta information vector corresponding to the creator's meta information based on the morphological vector of each of the one or more divided contents, the syllable vector of each of the one or more divided contents, and the creator recognizer. In this manner, the author meta information inference apparatus 110 can determine a meta information vector for each divided content. On the other hand, a method of determining author's meta information for the entire contents will be described later.

The author meta information inference apparatus 110 according to an embodiment of the present invention can determine the meta information 840 of the creator of the text content 810 based on the meta information vector 830 determined by the above- S66) At this time, the meta information of the content creator may include at least one of the age range of the creator, the gender of the creator, the region related to the creator, the political tendency of the creator, the academic background of the creator, and the marriage of the creator. Of course, the above-mentioned items are exemplary and can be used as meta information of the present invention if they are items that can be used as meta information of a person.

The author meta information inference apparatus 110 according to an embodiment of the present invention can determine meta information for each item of meta information in a probability form of a plurality of options for each item. For example, the author meta information inference apparatus 110 may determine the age range of the text content creator in the form of a probability that the creator belongs to a plurality of candidate age groups (10, 20, 30, 40, 50, etc.). Similarly, the author meta information inference apparatus 110 can determine the author's gender in the form of a probability that a creator is male and a probability that a creator is female. However, the above-mentioned items of age group and gender are illustrative and not intended to limit the scope of the present invention.

Meanwhile, when the meta information vector is determined for each of the divided contents, the author meta information inference apparatus 110 according to an embodiment of the present invention can determine meta information about each divided content.

At this time, the author meta information inference apparatus 110 according to an embodiment of the present invention may determine meta information about the entire text content by merging the meta information about each divided content. For example, when the probability that the creator belongs to a plurality of candidate age ranges is determined for each of the divided contents, the author meta information inference device 110 calculates the sum of the probabilities of the age groups (for example, the sum of the probabilities belonging to the teenagers, Etc.) to determine the meta information for the full text content. However, the present invention is not limited thereto.

Thus, the present invention can deduce the author's meta information in consideration of both morphological features and syllable characteristics of the text content.

7 is an illustration of a screen 900 displayed on the display unit of the user terminal 200 according to an embodiment of the present invention.

For convenience of explanation, the server 100 provides a service for providing the author's meta information on the text content inputted by the user through the user terminal 200, and the user can access the server 100 ) As shown in Fig.

Assuming the above, the server 100 according to the embodiment of the present invention can provide meta information of the speculated content creator to the user terminal 200 together with the screen 900. [

At this time, the screen 900 includes a region 910 in which the content of the content input by the user is displayed, an area 920 in which the sex of the inferred creator is displayed in the form of probability, and the age range of the inferred creator is displayed in the form of probability Region 930. [0034]

The embodiments of the present invention described above can be embodied in the form of a computer program that can be executed on various components on a computer, and the computer program can be recorded on a computer-readable medium. At this time, the medium may be a computer-executable program. Examples of the medium include a magnetic medium such as a hard disk, a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floptical disk, And program instructions including ROM, RAM, flash memory, and the like.

Meanwhile, the computer program may be designed and configured specifically for the present invention or may be known and used by those skilled in the computer software field. Examples of computer programs may include machine language code such as those produced by a compiler, as well as high-level language code that may be executed by a computer using an interpreter or the like.

The specific acts described in the present invention are, by way of example, not intended to limit the scope of the invention in any way. For brevity of description, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of such systems may be omitted. Also, the connections or connecting members of the lines between the components shown in the figures are illustrative of functional connections and / or physical or circuit connections, which may be replaced or additionally provided by a variety of functional connections, physical Connection, or circuit connections. Also, unless explicitly mentioned, such as " essential ", " importantly ", etc., it may not be a necessary component for application of the present invention.

Accordingly, the spirit of the present invention should not be construed as being limited to the above-described embodiments, and all ranges that are equivalent to or equivalent to the claims of the present invention as well as the claims .

100: Server
110: meta information reasoning device
111:
112:
113: Memory
200: user terminal
300: External device
400: communication network

Claims (2)

A method for inferring meta information of a text content creator by considering a morphological feature and a syllable feature in a meta information reasoning device,
Receiving textual content;
Dividing the textual content into one or more morphemes, and determining a morpheme vector from the one or more morphemes based on the morpheme-vector transformer;
Dividing the textual content into one or more syllables, and determining a syllable vector from the segmented one or more syllables based on a syllable-vector converter; And
And determining a meta information vector corresponding to the author's meta information based on the morpheme vector, the syllable vector, and the creator recognizer,
The step of determining the meta information vector
Merging the morpheme vector and the syllable vector to generate a content vector; And
Determining the meta information vector based on the content vector and the creator recognizer,
The morpheme-vector converter
A data set expressing a correlation between a plurality of morphemes and a plurality of morpheme vectors,
The syllable-vector converter
A data set expressing a correlation between a plurality of syllables and a plurality of syllable vectors,
The creator recognizer
A method of deducing meta information of a content creator considering morphological and syllable characteristics, which is a data set expressing a correlation between a plurality of morpheme vectors and a plurality of syllable vectors and a plurality of meta information vectors. A computer program stored in a computer-readable medium for executing the method of claim 1 using a computer.

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