CONSTRUCTING AN ANALYSIS OF A DOCUMENT

• US 20130110839A1
• Filed: 10/31/2011
• Published: 05/02/2013
• Est. Priority Date: 10/31/2011
• Status: Abandoned Application

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Micro Focus LLC (Open Text Corporation)
• Inventors: Kirshenbaum, Evan R.
• Application Number:
  US13/286,024
• Publication Number:
  US 20130110839A1
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/35 Clustering; Classification

Abstract

Systems, methods, and computer-readable and executable instructions are provided for constructing an analysis of a document. Constructing an analysis of a document can include determining a plurality of features based on the document, wherein each of the plurality of features is associated with a subset of a set of concepts. Constructing an analysis of a document can also include constructing a set of concept candidates based on the plurality of features, wherein each concept candidate is associated with at least one concept in the set of concepts. Furthermore, constructing an analysis of a document can include choosing a subset of the set of concept candidates as winning concept candidates and constructing an analysis that includes at least one concept in the set of concepts associated with at least one of the winning concept candidates.

Claims

1. A method for constructing an analysis of a document, comprising:

determining a plurality of features based on the document, wherein each of the plurality of features is associated with a subset of a set of concepts;

constructing a set of concept candidates based on the plurality of features, each concept candidate associated with at least one concept in the set of concepts;

choosing a subset of the set of concept candidates as winning concept candidates; and

constructing an analysis that includes at least one concept in the set of concepts associated with at least one of the winning concept candidates.

2. The method of claim 1, wherein a feature in the plurality of features is a potential concept indicator, and wherein choosing the subset of the set of concept candidates includes selecting a concept from the subset of the set of concepts associated with the feature as a referent for that feature.

3. The method of claim 1, wherein the subset of concept candidates are chosen based on a first weighted association between one of the plurality of features and a first concept candidate in the set of concept candidates.

4. The method of claim 3, wherein choosing the subset of concept candidates as winning concept candidates comprises:

determining a first vote associated with the first concept candidate based on the first weighted association;

determining a second vote associated with a second concept candidate in the set of concept candidates based on a second weighted association between the one of the plurality of features and the second concept candidate;

selecting the first concept candidate as a winning concept candidate; and

removing the second vote.

5. The method of claim 1, wherein choosing the subset of concept candidates as winning concept candidates is based on a conditional probability between a first concept candidate and a second concept candidate.

6. The method of claim 1 further comprising adding a first concept candidate associated with a first concept to the set of concept candidates based on a conditional probability between the first concept and a second concept associated with a second concept candidate in the set of concept candidates.

7. The method of claim 1, further comprising excluding a first one of the plurality of features from being used to construct the set of concept candidates based on the presence of a second one of the plurality of features.

8. The method of claim 1, further comprising mapping each of the plurality of features to an object that indicates a number of times each of the plurality of features appears in the document.

9. The method of claim 8, wherein the number of times that a first feature appears in the document is based on the number of times a second feature appears in the document.

10. A system for constructing an analysis of a document, comprising:

a memory;

a processor coupled to the memory, to;

determine, based on a plurality of features extracted from the document, a set of categories that organize a set of concept candidates within the set of categories;

choose a subset of the set of concept candidates as winning concept candidates using a feature weight and a concept probability;

wherein the feature weight indicates a distribution of a feature in the document and the concept probability includes a likelihood that a first concept candidate is in the subset if a second concept candidate is in the subset; and

construct an analysis, wherein the analysis includes an association between a concept associated with a first one of the winning concept candidates and a category in the set of categories.

11. The system of claim 10, wherein the winning concept candidates are further chosen based on the set of categories.

12. The system of claim 10, wherein the analysis further includes a category path demonstrating a sequence of progressively narrower categories in the set of categories, the category path associated with a second one of the winning concept categories.

13. The system of claim 10, wherein an action is performed based on the constructed analysis, and wherein the action includes at least one of synthesizing a user profile, classifying the document, recommending the document to a user, including the document in a publication, altering the configuration of a location of the document so as to emphasize the document or make it easier to find, determining a price to charge for accessing the document, determining a location for the document, sending a reference to the document to a user, and determining a management policy to apply to the document.

14. A computer-readable non-transitory medium storing a set of instructions for constructing an analysis of a document executable by the computer to cause the computer to:

associate each of a plurality of features extracted from the document with a set of concepts and construct a first concept candidate and a second concept candidate based on the plurality of features;

choose the first concept candidate as a winning concept candidate based on a conditional probability between the first concept candidate and the second concept candidate;

compute a score for the winning concept candidate; and

construct an analysis based on the score, wherein the analysis includes a concept associated with the winning concept candidate.

15. The medium of claim 14, wherein the score is indicative of at least one of a degree to which the document is about the concept and a confidence that the concept is mentioned in the document.