SYSTEMS AND METHODS FOR CHARACTERIZING LINKED DOCUMENTS USING A LATENT TOPIC MODEL

• US 20100161611A1
• Filed: 12/01/2009
• Published: 06/24/2010
• Est. Priority Date: 12/18/2008
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Guo, Zhen, Zhu, Shenghuo, Chi, Yun
• Granted Patent: US 8,234,274 B2
• Time in Patent Office: Days
• US Class Current: 707/738
• CPC Class Codes: G06F 16/94 Hypermedia Hyperlinking G06F40/134
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems and methods are disclosed for extracting characteristics from a corpus of linked documents by deriving a content link model that explicitly captures direct and indirect relations represented by the links, and extracting document topics and the topic distributions for all the documents in the corpus using the content-link model.

Claims

1. A method for characterizing a corpus of documents each having one or more links, comprising:

a. forming a Bayesian network using the documents;

b. determining a Bayesian network structure using the one or more links;

c. generating a content link model; and

d. determining one or more topics in the corpus and topic distribution for each document.

2. The method of claim 1, wherein the content link model captures direct and indirect relationships represented by the links.

3. The method of claim 1, comprising applying a Bayesian inference to extract document topics.

4. The method of claim 1, comprising applying non-negative matrix factorization to the content link model.

5. The method of claim 1, comprising performing a maximum likelihood estimation in the content link model.

6. The method of claim 1, comprising performing a Bayesian inference in the BPT model.

7. The method of claim 1, wherein the Bayesian network encodes direct and indirect relations, and wherein relationships are derived explicitly from links or implicitly from similarities among documents.

8. The method of claim 1, comprising applying the topics for characterizing, representing, summarizing, visualizing, indexing, ranking, or searching the documents in the corpus.

9. The method of claim 1, comprising applying the topics and the topic distributions to derive features for document clustering or classification.

10. The method of claim 1, comprising applying EM to estimate the parameters in the citation-topic model and the Bernoulli-process topic model.

11. The method of claim 1, comprising applying into a nonnegative matrix factorization (NMF) to estimate parameters for the Bayesian network.

12. The method of claim 11, comprising applying the NMF to perform maximum likelihood estimation for the citation topic model.

13. The method of claim 11, comprising applying the NMF to perform Bayesian inference for the Bernoulli-process topic model.

14. A method for extracting characteristics from a corpus of linked documents, comprising:

a. deriving a content link model that explicitly captures direct and indirect relations represented by the links, and

b. extracting document topics and the topic distributions for all the documents in the corpus using the content-link model.

15. The method of claim 14, comprising applying inference algorithms based on non-negative matrix factorization to the content model.

16. The method of claim 14, comprising generating a Bayesian network for the documents that encodes the direct and indirect relations, wherein the relations are derived either explicitly from links or implicitly from the similarities among documents.

17. The method of claim 14, comprising applying the topics for characterizing, representing, summarizing, visualizing, indexing, ranking, or searching the documents in the corpus.

18. The method of claim 14, comprising applying the topics and the topic distributions to derive features for document clustering or classification.

19. A system for extracting characteristics from a corpus of linked documents, comprising:

a. computer readable code to derive a content link model that explicitly captures direct and indirect relations represented by the links, and

b. computer readable code to extract document topics and the topic distributions for all the documents in the corpus using the content-link model.

20. A system for characterizing a corpus of documents each having one or more links, comprising:

a. means for forming a Bayesian network using the documents;

b. means for determining Bayesian network structure using the one or more links;

c. means for generating a content link model; and

d. means for applying the content link model to determine one or more topics in the corpus and topic distribution for each document.