Natural language processing via a two-dimensional symbol having multiple ideograms contained therein
First Claim
1. A method of machine learning of written natural languages comprising:
- receiving a string of natural language texts in a first computing system having at least one application module installed thereon;
forming, with the at least one application module in the first computing system, a multi-layer two-dimensional (2-D) symbol from the received string of natural language texts based on a set of rules, the 2-D symbol being a matrix of N×
N pixels of data that contains a super-character, the matrix being divided into M×
M sub-matrices with each of the sub-matrices containing (N/M)×
(N/M) pixels, said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing a meaning formed from a specific combination of a plurality of ideograms, where N and M are positive integers, and N is a multiple of M; and
learning the meaning of the super-character in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed with the at least one application module in the first computing system and transmitted to the second computing system.
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Abstract
A string of natural language texts is received and formed a multi-layer 2-D symbol in a first computing system. The 2-D symbol comprises a matrix of N×N pixels of data representing a “super-character”. The matrix is divided into M×M sub-matrices with each sub-matrix containing (N/M)×(N/M) pixels. N and M are positive integers, and N is preferably a multiple of M. Each sub-matrix represents one ideogram defined in an ideogram collection set. “Super-character” represents a meaning formed from a specific combination of a plurality of ideograms. The meaning of the “super-character” is learned in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed in the first computing system and transmitted to the second computing system. Image process technique includes predefining a set of categories and determining a probability for associating each of the predefined categories with the meaning of the “super-character”.
39 Citations
19 Claims
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1. A method of machine learning of written natural languages comprising:
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receiving a string of natural language texts in a first computing system having at least one application module installed thereon; forming, with the at least one application module in the first computing system, a multi-layer two-dimensional (2-D) symbol from the received string of natural language texts based on a set of rules, the 2-D symbol being a matrix of N×
N pixels of data that contains a super-character, the matrix being divided into M×
M sub-matrices with each of the sub-matrices containing (N/M)×
(N/M) pixels, said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing a meaning formed from a specific combination of a plurality of ideograms, where N and M are positive integers, and N is a multiple of M; andlearning the meaning of the super-character in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed with the at least one application module in the first computing system and transmitted to the second computing system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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Specification