Construction of trainable semantic vectors and clustering, classification, and searching using trainable semantic vectors
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Abstract
An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.
45 Citations
74 Claims
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1-28. -28 (Cancelled)
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29. A method of clustering data points from a dataset comprising the steps:
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constructing a trainable semantic vector for each data point from the dataset in a multi-dimensional semantic space; and
applying a clustering process to the constructed trainable semantic vectors to identify similarities between groups of data points within the dataset. - View Dependent Claims (30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 72)
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41-61. -61 (Cancelled)
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62. A system for clustering data points from a dataset comprising:
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a computer configured to;
construct a trainable semantic vector for each data point from the dataset in a multi-dimensional semantic space; and
apply a clustering process to the constructed trainable semantic vectors to identify similarities between groups of data points within the dataset. - View Dependent Claims (73)
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63-67. -67 (Cancelled)
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68. A computer-readable medium carrying one or more sequences of instructions for clustering data points from a dataset, wherein execution of the one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:
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constructing a trainable semantic vector for each data point from the dataset in a multi-dimensional semantic space; and
applying a clustering process to the constructed trainable semantic vectors to identify similarities between groups of data points within the dataset. - View Dependent Claims (74)
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69-71. -71 (Cancelled)
Specification