Self-organizing neural network for plain text categorization

Claim 3

The method of claim 1 wherein the step of creating a non-baseline node further comprises: applying each received text message to the neural network; for each non-baseline node, calculating a distance from each baseline node in the neural network to determine the connection weights; and based upon the calculating step, associating the non-baseline node with a baseline node having the greatest connection weight.

Claim 4

The method of claim 1 wherein the step of creating a non-baseline node further comprises: applying each received text message to the neural network; for each non-baseline node, calculating a distance from each baseline node and each prior non-baseline node in the neural network to determine the connection weights; and based upon the calculating step, associating the non-baseline node directly with a baseline node having the greatest connection weight unless there exists a prior non-baseline node with greater connection weight than the greatest baseline node in which case the non-baseline node is indirectly associated with the baseline node associated with the prior non-baseline node having greatest connection weight.

Claim 5

The method of claim 3 wherein the step of calculating a distance from each baseline node and each prior non-baseline node uses a least common sub-sequence matching algorithm.

Claim 6

The method of claim 1 further comprising: training the neural network using training data, wherein the training data comprises text messages from the preselected source.

Claim 7

The method of claim 1 further comprising the steps of preprocessing the received text messages to remove duplicates.

Claim 8

The method of claim 1 further comprising the steps of preprocessing the received text messages to break large massages into multiple paragraph-sized units.

Claim 9

The method of claim 1 further comprising the steps of preprocessing the received text messages to remove duplicate words, punctuation, and stop words.

Claim 10

The method of claim 1 further comprising the step of periodically combining the baseline and non-baseline nodes.

Claim 11

The method of claim 9 wherein the step of combining comprises: encoding token sequences from a selected baseline node with token sequences from a selected non-baseline node in a graph; randomly traversing the graph a plurality of times; extracting a child token sequence for each random traversal; comparing the extracted child token sequence with the selected token sequences; and replacing the selected parent sequences with a best matching child token sequence in response to the comparing step.

Claim 12

The method of claim 1 further comprising analyzing atypical received text messages by determining cohesiveness of non-baseline nodes comprising the steps of: identifying a cluster of non-baseline nodes comprising all non-baseline nodes associated with a selected baseline node; calculating a distance between each non-baseline node in the identified cluster and each other non-baseline node in the identified cluster such that smaller calculated distances indicate cohesiveness.

Claim 15

The method of claim 12 further comprising a step of notifying a user to review the text messages associated with atypical non-baseline nodes in response to the step of identifying atypical nodes.

Claim 18

A computer program product comprising: a computer usable medium having computer readable code embodied therein for natural language processing, the computer program product comprising: computer readable program code devices configured to cause a computer to effect a trained neural network comprising a plurality of baseline nodes, wherein each baseline node has a weight that is represented by a text string within the node; computer readable program code devices configured to cause a computer to effect receiving a plurality of text messages from a preselected source; computer readable program code devices configured to cause a computer to effect for each received message, creating a non-baseline node having a weight that is represented by the received text message; computer readable program code devices configured to cause a computer to effect associating each non-baseline node with a selected one of the baseline nodes; and computer readable program code devices configured to cause a computer to effect identifying atypical received text based upon a difference between the non-baseline node associated with the received message and the baseline node to which the non-baseline node is associated.

Claim 19

A computer program product comprising: a computer usable medium having computer readable code embodied therein for natural language processing, the computer program product comprising: computer readable program code devices configured to cause a computer to effect a trained neural network comprising a plurality of baseline nodes, wherein each baseline node has a weight that is represented by a text string within the node; computer readable program code devices configured to cause a computer to effect receiving a plurality of text messages from a preselected source; computer readable program code devices configured to cause a computer to effect for each received message, creating a non-baseline node having a weight that is represented by the received text message; computer readable program code devices configured to cause a computer to effect associating each non-baseline node with a selected one of the baseline nodes; and computer readable program code devices configured to cause a computer to effect identifying atypical received text based upon a difference between the non-baseline node associated with the received message and the baseline node to which the non-baseline node is associated.

Claim 20

The computer program product of claim 15 wherein the computer readable program code devices configured to cause a computer to effect associating each non-baseline node with a selected one of the baseline nodes further comprises: computer readable program code devices configured to cause a computer to effect applying each received text message to the neural network; computer readable program code devices configured to cause a computer to effect for each non-baseline node, calculating a distance from each baseline node and each prior non-baseline node in the neural network to determine a connection weight, wherein the program code for calculating a distance uses a least common sub-sequence matching algorithm; and computer readable program code devices responsive to the calculating step configured to cause a computer to effect associating the non-baseline node directly with a baseline node having the greatest connection weight unless there exists a prior non-baseline node with greater connection weight than the greatest baseline node in which case the non-baseline node is indirectly associated with the baseline node associated with the prior non-baseline node having greatest connection weight.

Claim 21

A computer program product comprising: a computer usable medium having computer readable code embodied therein for natural language processing, the computer program product comprising: computer readable program code devices configured to cause a computer to effect a trained neural network comprising a plurality of baseline nodes, wherein each baseline node has a weight that is represented by a text string within the node; computer readable program code devices configured to cause a computer to effect receiving a plurality of text messages from a preselected source; computer readable program code devices configured to cause a computer to effect for each received message, creating a non-baseline node having a weight that is represented by the received text message; computer readable program code devices configured to cause a computer to effect associating each non-baseline node with a selected one of the baseline nodes; computer readable program code devices configured to cause a computer to effect a self-organizing neural network architecture comprising a one-dimensional circular array of baseline nodes; computer readable program code devices configured to cause a computer to effect randomizing the weight within each of the baseline nodes; computer readable program code devices configured to cause a computer to effect receiving a baseline text messages from the preselected source; computer readable program code devices configured to cause a computer to effect associating the received baseline text message with a closest one of the randomized baseline nodes wherein closeness is measured by a longest common sub-sequence algorithm; computer readable program code devices configured to cause a computer to effect combining the received baseline text message with the associated baseline node using a genetic algorithm; and computer readable program code devices configured to cause a computer to effect iteratively executing the program code effecting the receiving, the associating, and the combining until the neural network achieves a desired state of training.

Claim 22

A computer program product comprising: a computer usable medium having computer readable code embodied therein for natural language processing, the computer program product comprising: computer readable program code devices configured to cause a computer to effect a trained neural network comprising a plurality of baseline nodes, wherein each baseline node has a weight that is represented by a text string within the node; computer readable program code devices configured to cause a computer to effect receiving a plurality of text messages from a preselected source; computer readable program code devices configured to cause a computer to effect for each received message, creating a non-baseline node having a weight that is represented by the received text message; computer readable program code devices configured to cause a computer to effect associating each non-baseline node with a selected one of the baseline nodes; computer readable program code devices configured to cause a computer to effect a self-organizing neural network architecture comprising a one-dimensional circular array of baseline nodes; computer readable program code devices configured to cause a computer to effect randomizing the weight within each of the baseline nodes; computer readable program code devices configured to cause a computer to effect receiving a baseline text messages from the preselected source; computer readable program code devices configured to cause a computer to effect associating the received baseline text message with a closest one of the randomized baseline nodes wherein closeness is measured by a longest common sub-sequence algorithm; computer readable program code devices configured to cause a computer to effect combining the received baseline text message with the associated baseline node using a genetic algorithm; and computer readable program code devices configured to cause a computer to effect iteratively executing the program code effecting the receiving, the associating, and the combining until the neural network achieves a desired state of training.

Claim 23

The computer program product of claim 17 wherein the computer readable program code devices configured to cause a computer to effect least common sub-sequence matching algorithm further comprises computer readable program code devices configured to cause a computer to effect the equation: ##EQU3## wherein len(s1) is the length of the text within the baseline node, len(s2) is the length of the baseline text, and len(LCS) is the length of the longest common sequence between s1 and s2.

Claim 26

The system of claim 19 wherein the software operable in the computer for evaluating relative positions calculates a metric score based upon content of the baseline nodes and the non-baseline nodes.

Claim 35

The system of claim 24 wherein the software operable for combining comprises a genetic algorithm.