Multi-magnitudinal vectors with resolution based on source vector features

Abstract

Methods, systems and computer program products for resolving multiple magnitudes assigned to a target vector are disclosed. A target vector that includes one or more target vector dimensions is received. One of the target vector dimensions is processed to determine a total number of magnitudes assigned to the processed target vector dimension. Also, a source vector that includes one or more source vector dimensions is received. The received source vector is processed to determine a total number of features associated with the source vector. When it is detected that the total number of magnitudes assigned to the processed target vector dimension exceeds one, one of the assigned magnitudes is selected based on one of the determined features associated with the source vector.

What is claimed is: 
     
         1 . A non-transitory computer readable medium encoded with instructions executable by a processor of a computing system for performing vector comparison in natural language processing, the instructions comprising instructions for:
 receiving a target vector and a source vector, the target vector including a first target vector dimension comprising a first term vector including a first word or phrase and a second target vector dimension comprising a second term vector including a second word or phrase;   assigning a first magnitude comprising a first weight to the first target vector dimension based on an association of the first term vector with the source vector, wherein the first magnitude is assigned based on determining a similarity measure between the first term vector and the source vector; and   determining whether a current dimension of the target vector is a last dimension to be processed.   
     
     
         2 . The non-transitory computer readable medium of  claim 1 , further comprising assigning a second magnitude comprising a second weight to the second target vector dimension based on an association of the second term vector with the source vector. 
     
     
         3 . The non-transitory computer readable medium of  claim 2 , wherein the second magnitude is assigned based on determining a similarity measure between the second term vector and the source vector. 
     
     
         4 . The non-transitory computer readable medium of  claim 1 , wherein the similarity measure is determined by measuring an angle between the target vector and the source vector. 
     
     
         5 . The non-transitory computer readable medium of  claim 1 , wherein the instructions further comprise instructions for:
 storing the source vector in a source data storage memory; and   storing the target vector in a semantic data storage memory.   
     
     
         6 . The non-transitory computer readable medium of  claim 1 , wherein the source vector is created by a parser from one or more source documents. 
     
     
         7 . The non-transitory computer readable medium of  claim 1 , wherein the similarity measure between the first term vector and the source vector compares a morphological characteristic, a syntax, a proximity to one or more additional terms or phrases associated with the source vector, a frequency, a date or a time, a location, an origination location of a source document, an event location, a source document analysis location, or a purpose for which the similarity measure is occurring. 
     
     
         8 . The non-transitory computer readable medium of  claim 1 , wherein prior to determining whether a current dimension of the target vector is a last dimension to be processed, further comprising determining a total number of dimensions present in the target vector. 
     
     
         9 . The non-transitory computer readable medium of  claim 1 , wherein the instructions further comprise instructions for:
 determining whether a source vector dimension has more than one possible magnitude, wherein the magnitude comprises a weight.   
     
     
         10 . A system for performing vector comparison in natural language processing, the system comprising at least one processing unit coupled to a memory, wherein the memory is encoded with computer executable instructions that, when executed, cause the at least one processing unit to:
 receive a target vector and a source vector, the target vector including a first target vector dimension comprising a first term vector including a first word or phrase; and   assign a first magnitude comprising a first weight to the first target vector dimension based on an association of the first term vector with the source vector, wherein the first magnitude is assigned based on determining a similarity measure between the first term vector and the source vector.   
     
     
         11 . The system of  claim 10 , wherein the target vector includes a second target vector dimension comprising a second term vector including a second word or phrase. 
     
     
         12 . The system of  claim 11 , wherein the system is configured to assign a second magnitude comprising a second weight to the second target vector dimension based on an association of the second term vector with the source vector. 
     
     
         13 . The system of  claim 10 , wherein the system is configured to determine whether a current dimension of the target vector is a last dimension to be processed. 
     
     
         14 . The system of  claim 13 , wherein prior to determining whether a current dimension of the target vector is a last dimension to be processed, the system is configured to determine a total number of dimensions present in the target vector. 
     
     
         15 . The system of  claim 10 , wherein the similarity measure is determined by measuring an angle between the target vector and the source vector. 
     
     
         16 . The system of  claim 10 , wherein the memory comprises:
 a source data storage memory configured to store the source vector; and   a semantic data storage memory configured to store the target vector.   
     
     
         17 . The system of  claim 10 , wherein the source vector is created by a parser from one or more source documents. 
     
     
         18 . The system of  claim 10 , wherein the similarity measure between the first term vector and the source vector compares a morphological characteristic, a syntax, a proximity to one or more additional terms or phrases associated with the source vector, a frequency, a date or a time, a location, an origination location of a source document, an event location, a source document analysis location, or a purpose for which the similarity measure is occurring. 
     
     
         19 . The system of  claim 10 , wherein the instructions further cause the at least one processing unit to:
 determine whether a source vector dimension has more than one possible magnitude, wherein the magnitude comprises a weight.   
     
     
         20 . The system of  claim 10 , wherein the first magnitude is associated with an indexed data structure.