Taxonomy generation for document collections
First Claim
1. A computer-executable method of generating a content taxonomy of a multitude of documents (210) stored on a computer system, said method comprising:
- a subset-selection-step (201), for selecting a subset of said multitude of documents;
a taxonomy-generation-step (202 to 205), for generating a taxonomy for said subset, wherein said taxonomy is a tree-structured taxonomy-hierarchy, and wherein said subset is divided into a set of clusters with largest intra-similarity, and wherein each of said clusters of largest intra-similarity is assigned to a leaf-node of said taxonomy-hierarchy as outer-clusters, and wherein inner-nodes of said taxonomy-hierarchy order said subset, starting with said outer-clusters, into inner-clusters with increasing cluster size and decreasing similarity, and wherein said taxonomy-generation-step further comprises a first-feature-extraction-step (202) for extracting for each document of said subset its features, and for computing its feature statistics in a feature-vector (212) as a representation of said document; and
a routing-selection-step (206), for computing, for each unprocessed document of said multitude of documents not belonging to said subset, similarities with said outer-clusters, and for assigning said document to the leaf-node of said taxonomy-hierarchy being the outer-cluster with largest similarty.
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Abstract
This mechanism relates to a method within the area of information mining within a multitude of documents stored on computer systems. More particularly, this mechanism relates to a computerized method of generating a content taxonomy of a multitude of electronic documents. The technique proposed by the current invention is able to improve at the same time the scalability and the coherence and selectivity of taxonomy generation. The fundamental approach of the current invention comprises a subset selection step, wherein a subset of a multitude of documents is being selected. In a taxonomy generation step a taxonomy is generated for that selected subset of documents, the taxonomy being a tree structured taxonomy hierarchy. Moreover this method comprises a routing selection step assigning each unprocessed document to the taxonomy hierarchy based on largest similarity.
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Citations
31 Claims
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1. A computer-executable method of generating a content taxonomy of a multitude of documents (210) stored on a computer system, said method comprising:
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a subset-selection-step (201), for selecting a subset of said multitude of documents;
a taxonomy-generation-step (202 to 205), for generating a taxonomy for said subset, wherein said taxonomy is a tree-structured taxonomy-hierarchy, and wherein said subset is divided into a set of clusters with largest intra-similarity, and wherein each of said clusters of largest intra-similarity is assigned to a leaf-node of said taxonomy-hierarchy as outer-clusters, and wherein inner-nodes of said taxonomy-hierarchy order said subset, starting with said outer-clusters, into inner-clusters with increasing cluster size and decreasing similarity, and wherein said taxonomy-generation-step further comprises a first-feature-extraction-step (202) for extracting for each document of said subset its features, and for computing its feature statistics in a feature-vector (212) as a representation of said document; and
a routing-selection-step (206), for computing, for each unprocessed document of said multitude of documents not belonging to said subset, similarities with said outer-clusters, and for assigning said document to the leaf-node of said taxonomy-hierarchy being the outer-cluster with largest similarty. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A computer program product comprising a computer usable medium having computer readable program code means embodied in said medium for generating a content taxonomy of a multitude of documents stored on a computer system, said computer readable program code means comprising:
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a subset selector for selecting a subset of said multitude of documents;
a taxonomy generator for generating a taxonomy for said subset, wherein said taxonomy generator further comprises a first-feature-extractor for extracting for each document of said subset its features, and for computing its feature statistics in a feature-vector as a representation of said document; and
a routing selector for computing, for each unprocessed document of said multitude of documents not belonging to said subset, similarities with said outer-clusters and for assigning said document to the leaf-node of said taxonomy-hierarchy being the outer-cluster with largest similarity, wherein said taxonomy is a tree-structured taxonomy-hierarchy, and wherein said subset is divided into a set of clusters with largest intra-similarity, and wherein each of said clusters of largest intra-similarity is assigned to a leaf-node of said taxonomy-hierarchy as outer-clusters, and wherein inner-nodes of said taxonomy-hierarchy order said subset, starting with said outer-clusters, into inner-clusters with increasing cluster size and decreasing similarity.
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17. A system for generating a content taxonomy of a multitude of documents stored on a computer system, said system comprising:
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means for selecting a subset of said multitude of documents;
means for generating a taxonomy for said subset, wherein said taxonomy is a tree-structured taxonomy-hierarchy, and wherein said subset is divided into a set of clusters with largest intra-similarity, and wherein each of said clusters of largest intra-similarity is assigned to a leaf-node of said taxonomy-hierarchy as outer-clusters, and wherein inner-nodes of said taxonomy-hierarchy order said subset, starting with said outer-clusters, into inner-clusters with increasing cluster size and decreasing similarity, and wherein said means for generating further comprises a first-feature-extractor means for extracting for each document of said subset its features, and for computing its feature statistics in a feature-vector as a representation of said document; and
means for computing, for each unprocessed document of said multitude of documents not belonging to said subset, similarities with said outer-clusters, and for assigning said document to the leaf-node of said taxonomy-hierarchy being the outer-cluster with largest similarity. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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Specification