US 2010/0161527 A1 · EFFICIENTLY BUILDING COMPACT MODELS FOR LARGE TAXONOMY TEXT CLASSIFICATION

Abstract

A taxonomy model is determined with a reduced number of weights. For example, the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class. For each node of the taxonomy, the training example documents are processed to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node. For each node of the taxonomy, a sparse weight vector is determined for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node. The sparse weight vectors can be learned by solving an optimization problem using a maximum entropy classifier, or a large margin classifier with a sequential dual method (SDM) with margin or slack resealing. The determined sparse weight vectors are tangibly embodied in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy.

Claims18

Dependent claims

A method of determining a taxonomy model, wherein the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class, the method comprising:

for each node of the taxonomy, processing the training example documents to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node,
for each node of the taxonomy, determining a sparse weight vector for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node;
and
tangibly embodying the determined sparse weight vectors in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy.

1 more element

A computer program product comprising at least one tangible computer readable medium having computer program instructions tangibly embodied thereon, the computer program instructions to configure at least one computing device to determine a taxonomy model, wherein the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class, including to:

for each node of the taxonomy, process the training example documents to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node,
for each node of the taxonomy, determine a sparse weight vector for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node;
and
tangibly embody the determined sparse weight vectors in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy.

1 more element

A computer system having at least one computing device configured to determine a taxonomy model, wherein the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class, including to:

process computer program instructions to, for each node of the taxonomy, process the training example documents to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node,
process computer program instructions to, for each node of the taxonomy, determine a sparse weight vector for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node;
and
process computer program instructions to tangibly embody the determined sparse weight vectors in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy.

1 more element

Dependencies are inferred from the claim text.

Efficiently building compact models for large taxonomy text classification

Life of this patent

Efficiently building compact models for large taxonomy text classification

Life of this patent

Abstract

Claims18