System And Method For Providing A Topic-Directed Search

• US 20100057716A1
• Filed: 01/15/2009
• Published: 03/04/2010
• Est. Priority Date: 08/28/2008
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Hong, Lichan, Stefik, Mark J., Pirolli, Peter L., Card, Stuart K.
• Granted Patent: US 8,010,545 B2
• Time in Patent Office: Days
• US Class Current: N/A
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification

Abstract

A system and method for providing a topic-directed search is provided, which advantageously harnesses user-provided topical indexes and an ability to characterize indexes according to how articles fall under their topical organizations. A corpus of articles and an index that includes topics from the articles is maintained. For each topic, a coarse-grained topic model is built, which includes the characteristic words included in the articles relating to the topic and scores assigned to the characteristic words. A search query is executed against the index. The topics that match the search terms are chosen by their scores. The topics that match the coarse-grained topic models and the articles corresponding to the search query are presented. In contrast to conventional search engines, search results are organized according to topic and search results can be offered across multiple indexes, where part of returned results are selected from most-relevant indexes with their most-relevant topics.

Claims

1. A computer-implemented system for providing a topic-directed search, comprising:

articles of digital information and one or more indexes comprising topics that each relate to one or more of the articles, that are both electronically-stored in persistent storage;

a computer comprising a processor and memory within which code for execution by the processor is stored, comprising;

a topic builder that, for each topic, builds a coarse-grained topic model comprising the characteristic words comprised in the articles relating to the topic and scores assigned to the characteristic words; and

a search engine executing a search query comprising search terms against one of the indexes, and choosing the topics that match the search terms by their scores; and

a user interface visually presenting the topics that match the chosen coarse-grained topic models and the articles corresponding to the search query.

2. A system according to claim 1, further comprising:

a model builder comprising code for execution by the processor that comprises;

a random sampler selecting a random sampling of the articles; and

a scoring module determining frequencies of occurrence of the characteristic words comprised in the articles in the random sampling and in positive training examples, and identifying a ratio of the frequencies of occurrence for the characteristic words comprised in the random sampling and the positive training examples,

wherein the ratios of the characteristic words are included as the scores of the coarse-grained topic models.

3. A system according to claim 2, further comprising;

a pre-computing monitor comprising code for execution by the processor that comprises;

a counter monitoring a number of articles comprised in the topics of the index; and

an analyzer periodically re-determining the frequencies of occurrence of the characteristic words comprised in the articles in the random sampling when the number of articles has changed by a predetermined amount,

4. A system according to claim 2, wherein a sampling of articles matching fine-grained topic models for each topic is selected in lieu of the positive training examples.

5. A system according to claim 1, wherein the search engine executes the search query against each of the indexes, and, for each of the indexes, chooses the topics that match the search terms by their scores, and the user interface visually presents the indexes and the topics.

6. A system according to claim 1, further comprising:

a scoring module comprising code for execution by the processor that comprises;

an evaluator finding the highest score for the coarse-grained topic models; and

a normalizer normalizing the scores of the remaining coarse-grained topic models against the highest score.

7. A system according to claim 1, wherein a cumulative score for the coarse-grained topic models corresponding to the search terms is determined, and the topics as ordered by their respective search query cumulative scores are returned.

8. A system according to claim 1, further comprising:

a scoring module comprising code for execution by the processor that comprises;

a comparison module, for each topic, comparing the score of each coarse-grained topic model corresponding to one of the search terms to a threshold; and

an evaluator either discounting or discarding the topic when at least one of the scores fails to exceed the threshold.

9. A system according to claim 1, further comprising:

a scoring module comprising code for execution by the processor that comprises;

a score adjuster adjusting the scores of the coarse-grained topic models, comprising one or more of;

an infrequent word evaluator reducing the score for each characteristic word appearing fewer than a minimum number of times in the articles;

a short word evaluator reducing the score for each characteristic word comprising a length of less than a minimum number of characters;

a label word evaluator increasing the score for each characteristic word appearing in an index label of one or more of the articles; and

a neighboring word evaluator increasing the score of each characteristic word either neighboring or appearing adjacent to another characteristic word appearing in an index label of one or more of the articles.

10. A system according to claim 1, further comprising at least one of:

a display module comprising code for execution by the processor that comprises;

a topic emphasizer highlighting the topics presented in the user interface, comprising at least one of emphasizing a top percentile of the articles corresponding to the search query, emphasizing the topics whose matching coarse-grained topic models have a cumulative score exceeding a threshold, and emphasizing the topics comprising top-scoring characteristic words that match the search terms; and

a topic elider eliding the topics presented in the user interface that fall below a set matching score.

11. A computer-implemented method for providing a topic-directed search, comprising:

maintaining articles of digital information and one or more indexes comprising topics that each relate to one or more of the articles;

for each topic, building a coarse-grained topic model comprising the characteristic words comprised in the articles relating to the topic and scores assigned to the characteristic words;

selecting one of the indexes and executing a search query comprising search terms against the index selected;

choosing the topics that match the search terms by their scores; and

presenting the topics that match the chosen coarse-grained topic models and the articles corresponding to the search query through a user interface.

12. A method according to claim 11, further comprising;

selecting a random sampling of the articles;

determining frequencies of occurrence of the characteristic words comprised in the articles in the random sampling and in positive training examples;

identifying a ratio of the frequencies of occurrence for the characteristic words comprised in the random sampling and the positive training examples; and

including the ratios of the characteristic words as the scores of the coarse-grained topic models.

13. A method according to claim 12, further comprising:

monitoring a number of articles comprised in the topics of the index; and

periodically re-determining the frequencies of occurrence of the characteristic words comprised in the articles in the random sampling when the number of articles has changed by a predetermined amount.

14. A method according to claim 12, further comprising:

selecting a sampling of articles matching fine-grained topic models for each topic in lieu of the positive training examples.

15. A method according to claim 11, further comprising:

executing the search query against each of the indexes;

for each of the indexes, choosing the topics that match the search terms by their scores; and

presenting the indexes and the topics through the user interface.

16. A method according to claim 11, further comprising:

finding the highest score for the coarse-grained topic models; and

normalizing the scores of the remaining coarse-grained topic models against the highest score.

17. A method according to claim 11, further comprising;

determining a cumulative score for the coarse-grained topic models corresponding to the search terms; and

returning the topics as ordered by their respective search query cumulative scores.

18. A method according to claim 11, further comprising:

for each topic, comparing the score of each coarse-grained topic model corresponding to one of the search terms to a threshold; and

either discounting or discarding the topic when at least one of the scores fails to exceed the threshold.

19. A method according to claim 11, further comprising:

adjusting the scores of the coarse-grained topic models, comprising one or more of;

reducing the score for each characteristic word appearing fewer than a minimum number of times in the articles;

reducing the score for each characteristic word comprising a length of less than a minimum number of characters;

increasing the score for each characteristic word appearing in an index label of one or more of the articles; and

increasing the score of each characteristic word either neighboring or appearing adjacent to another characteristic word appearing in an index label of one or more of the articles.

20. A method according to claim 11, further comprising at least one of:

highlighting the topics presented in the user interface, comprising at least one of;

emphasizing a top percentile of the articles corresponding to the search query;

emphasizing the topics whose matching coarse-grained topic models have a cumulative score exceeding a threshold; and

emphasizing the topics comprising top-scoring characteristic words that match the search terms; and

eliding the topics presented in the user interface that fall below a set matching score.