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US 12,737,398 B2Granted
Method of generating keyword information and an electronic device performing thereof
AssigneeDunamu Inc.
InventorsJaehyuk Kim, Dongjun Lee, Seokhyun Hwang
Life of this patent
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Abstract
Provided is a method of generating keyword information by an electronic device. The method includes identifying a text set including at least one text element. The method includes using a named entity recognition (NER) model based on deep learning, identifying keywords in the at least one text element. The method includes based on the text set, determining degrees of association of keyword pairs included in a keyword set including the keywords. The method includes obtaining information on a query word that is input by a user. The method includes based on at least one among the degrees of association, generating information on response words corresponding to the query word.
Claims17
A method of generating keyword information performed by an electronic device, the method comprising:
- identifying a text set including at least one text element;
- using a named entity recognition model based on deep learning, identifying keywords in the at least one text element;
- based on the text set, determining degrees of association of keyword pairs included in a keyword set including the keywords by:
- identifying total frequency in which the keyword pairs are included in each of the at least one text element;
- and
- based on the keyword set and the total frequency, determining a co-occurrence graph that includes nodes and edges connecting the nodes,
- wherein each of the nodes corresponds to one of the keywords included in the keyword set,
- wherein a weight of each of the edges includes at least one of a first sub weight and a second sub weight that are related to a first node and a second node that are connected to the each of the edges,
- wherein the first sub weight is identified based on first total frequency in which a keyword corresponding to the first node and a keyword corresponding to the second node are included together in each of the at least one text element and second total frequency in which the keyword corresponding to the first node is included in each of the at least one text element, and
- wherein the second sub weight is identified based on the first total frequency and third total frequency in which the keyword corresponding to the second node is included in each of the at least one text element;
- obtaining information on a query word that is input by a user;
- and
- based on at least one among the degrees of association, generating information on response words corresponding to the query word.
10 more elements
An electronic device comprising:
- a transceiver;
- at least one processor;
- and
- a memory configured to store one or more instructions executed by the at least one processor,
- wherein the at least one processor, executing the one or more instructions, is configured to:
- identify a text set including at least one text element;
- using a named entity recognition model based on deep learning, identify keywords in the at least one text element;
- based on the text set, determine degrees of association of keyword pairs included in a keyword set including the keywords by:
- identifying total frequency in which the keyword pairs are included in each of the at least one text element;
- and
- based on the keyword set and the total frequency, determining a co-occurrence graph that includes nodes and edges connecting the nodes,
- wherein each of the nodes corresponds to one of the keywords included in the keyword set,
- wherein a weight of each of the edges includes at least one of a first sub weight and a second sub weight that are related to a first node and a second node that are connected to the each of the edges,
- wherein the first sub weight is identified based on first total frequency in which a keyword corresponding to the first node and a keyword corresponding to the second node are included together in each of the at least one text element and second total frequency in which the keyword corresponding to the first node is included in each of the at least one text element, and
- wherein the second sub weight is identified based on the first total frequency and third total frequency in which the keyword corresponding to the second node is included in each of the at least one text element;
- obtain information on a query word that is input by a user;
- and
- based on at least one among the degrees of association, generate information on response words corresponding to the query word.
15 more elements
A non-transitory computer-readable recording medium having a program for executing a method by a computing device, the method comprising:
- identifying a text set including at least one text element;
- using a named entity recognition model based on deep learning, identifying keywords in the at least one text element;
- based on the text set, determining degrees of association of keyword pairs included in a keyword set including the keywords by:
- identifying total frequency in which the keyword pairs are included in each of the at least one text element;
- and
- based on the keyword set and the total frequency, determining a directed weighted co-occurrence graph;
- obtaining information on a query word that is input by a user;
- and
- based on at least one among the degrees of association, generating information on response words corresponding to the query word by:
- based on the directed weighted co-occurrence graph, identifying at least one keyword related to the query word among the keywords included in the keyword set;
- and
- based on a degree of association of a keyword pair consisting of one of the at least one keyword and the query word, generating the information on the response words corresponding to the query word,
- wherein the degree of association of a keyword pair consisting of one of the at least one keyword and the query word is determined through a predetermined calculation using at least one sub weight related to a node corresponding to the one of the at least one keyword and a node corresponding to the query word on the directed weighted co-occurrence graph.
10 more elements
Dependencies are inferred from the claim text.