Joint embedding of corpus pairs for domain mapping
First Claim
1. A system, comprising:
- a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise;
a first learning component that;
analyzes first domain data associated with a domain comprising a first corpus, resulting in first analyzed data;
a second learning component that;
analyzes second domain data associated with a second domain comprising a second corpus, resulting in second analyzed data;
an identification component that;
based on the first analyzed data and the second analyzed data, identifies equivalent terms between the first domain data and the second domain data; and
a joint embedding component that;
based on the equivalent terms, the first analyzed data, and the second analyzed data, jointly embeds the first domain data and the second domain data, resulting in jointly embedded data and wherein the jointly embedding comprises analyzing a defined word, a sequence of words in a fixed window on either side of the defined word and generating a prediction of a context in which the defined word is likely to appear; and
in response to the jointly embedded data, outputs a model associated with the jointly embedded data employed to leverage across other domains to generate additional terms.
1 Assignment
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Accused Products
Abstract
Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding. The scoring the joint embedding affinity comprises: transforming the embedded representation of the first keyword and the embedded representation of the second keywords via the trained model; determining an affinity value based on comparing the first keyword to the second keywords; and based on the affinity value, aggregating the joint embedding of the embedded representation of the first keyword and the embedded representation of the second keywords within the second domain corpus.
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Citations
20 Claims
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1. A system, comprising:
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a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise; a first learning component that; analyzes first domain data associated with a domain comprising a first corpus, resulting in first analyzed data; a second learning component that; analyzes second domain data associated with a second domain comprising a second corpus, resulting in second analyzed data; an identification component that; based on the first analyzed data and the second analyzed data, identifies equivalent terms between the first domain data and the second domain data; and a joint embedding component that; based on the equivalent terms, the first analyzed data, and the second analyzed data, jointly embeds the first domain data and the second domain data, resulting in jointly embedded data and wherein the jointly embedding comprises analyzing a defined word, a sequence of words in a fixed window on either side of the defined word and generating a prediction of a context in which the defined word is likely to appear; and in response to the jointly embedded data, outputs a model associated with the jointly embedded data employed to leverage across other domains to generate additional terms. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer program product for generating training data, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable to:
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analyze first domain data associated with a domain comprising a first corpus, resulting in first analyzed data; analyze second domain data associated with a second domain comprising a second corpus, resulting in second analyzed data; based on the first analyzed data and the second analyzed data, identify equivalent terms between the first domain data and the second domain data; based on the equivalent terms, the first analyzed data, and the second analyzed data, jointly embed the first domain data and the second domain data, resulting in jointly embedded data first domain data and the second domain data; and a joint embedding component that; based on the equivalent terms, the first analyzed data, and the second analyzed data, jointly embeds the first domain data and the second domain data, resulting in jointly embedded data and wherein the jointly embedded data is generated based on analyzing a defined word, a sequence of words in a fixed window on either side of the defined word and generating a prediction of a context in which the defined word is likely to appear; and in response to the jointly embedded data, outputs a model associated with the jointly embedded data employed to leverage across other domains to generate additional terms; and in response to the jointly embedded data, output a model associated with the jointly embedded data. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15)
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16. A computer-implemented method, comprising:
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analyzing, by a device operatively coupled to a processor, first domain data associated with a domain comprising a first corpus, resulting in first analyzed data; analyzing, by the device, second domain data associated with a second domain comprising a second corpus, resulting in second analyzed data; based on the analyzing the first domain data and the analyzing the second domain data, identifying, by the device, equivalent terms between the first domain data and the second domain data; based on the equivalent terms, the first analyzed data, and the second analyzed data, jointly embedding, by the device, the first domain data and the second domain data, resulting in jointly embedded data and wherein the jointly embedded data is generated based on analyzing a defined word, a sequence of words in a fixed window on either side of the defined word and generating a prediction of a context in which the defined word is likely to appear; and in response to the jointly embedding, outputting, by the device, a model associated with the jointly embedded data. - View Dependent Claims (17, 18, 19, 20)
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Specification