System and method for good nearest neighbor clustering of text

• US 20070244874A1
• Filed: 03/27/2006
• Published: 10/18/2007
• Est. Priority Date: 03/27/2006
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Tawde, Vivek
• Granted Patent: US 7,747,083 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification

Abstract

An improved system and method for clustering text or content described by text is provided. Each text in a set of texts may be represented as a dimensional vector of words. Singleton texts that may not be similar to another text may be excluded from the set of texts for clustering. Texts identified as good nearest neighbors may then be grouped in the same cluster. In addition, metadata describing content may be used for clustering items of aggregated content from content feeds. Metadata describing items of content from content feeds may be converted into a set of texts and texts identified as good nearest neighbors may then be clustered. Items of content feeds described by the clustered texts may then be similarly clustered. Any types of items of content that may be described by text may be clustered, including audio, images, video, multimedia content, and so forth.

Claims

1. A computer system for clustering text, comprising:

a clustering engine for clustering a text in a cluster along with one or more other texts determined to be a good nearest neighbor of the text; and

a good nearest neighbor analyzer operably coupled to the clustering engine for determining the one or more other texts to be a good nearest neighbor of the text.

2. The system of claim 1 further comprising a text converter operably coupled to the clustering engine for converting the text into a representation of a dimensional vector of words.

3. The system of claim 1 further comprising a singleton analyzer operably coupled to the clustering engine for identifying a text that may not be similar to another text in the set of texts.

4. A computer-readable medium having computer-executable components comprising the system of claim 1.

5. A computer-implemented method for clustering text, comprising:

determining at least one text in a set of texts to be a good nearest neighbor of an other text in the set of texts;

clustering the other text in a cluster; and

clustering the at least one text determined to be the good nearest neighbor of the other text in the cluster.

6. The method of claim 5 wherein determining the at least one text in the set of texts to be the good nearest neighbor of the other text in the set of texts comprises:

representing the at least one text as a dimensional vector of words;

representing the other text as a dimensional vector of words; and

determining a dot-product of the dimensional vector of the other text and the dimensional vector of the at least one text.

7. The method of claim 6 further comprising:

comparing the dot-product to a threshold; and

if the dot-product exceeds the threshold, determining the at least one text to be the good nearest neighbor of the other text.

8. The method of claim 7 wherein the threshold comprises an upper bound of a value in a range from zero to one that represents a cosine similarity between the other text and the at least one text.

9. The method of claim 7 further comprising:

comparing the dot-product to a similarity threshold;

determining a measure of a number of words used both by the at least one text and the other text;

comparing the measure of the number of words to an overlap threshold; and

if the dot-product exceeds the similarity threshold and the measure of the number of words also exceeds the overlap threshold, determining the at least one text to be the good nearest neighbor of the other text.

10. The method of claim 9 wherein the similarity threshold comprises a lower bound of a value in a range from zero to one that represents a cosine similarity between the other text and the at least one text.

11. The method of claim 9 wherein the overlap threshold comprises a value in a range from zero to one that represents a measure of words used in common by the other text and the at least one text.

12. The method of claim 5 further comprising:

determining a text not to be similar to one or more texts in the set of texts; and

excluding the text determined not to be similar from clustering with the set of texts.

13. The method of claim 12 wherein determining the text not to be similar to the one or more texts in the set of texts comprises:

representing the text as a dimensional vector of words;

representing each of the one or more texts as a dimensional vector of words; and

for each of the one or more texts, determining a dot-product of the dimensional vector of the each of the one or more texts and the dimensional vector of the text.

14. The method of claim 13 further comprising:

comparing the dot-product for each of the one or more texts to a similarity threshold;

for each of the one or more texts, determining a measure of a number of words used both by the text and the each of the one or more texts;

comparing the measure of the number of words for each of the one or more texts to an overlap threshold; and

if the similarity threshold exceeds the dot-product for each of the one or more texts and the overlap threshold exceeds the measure of the number of words for each of the one or more texts, determining the text not to be similar to the one or more texts in the set of texts.

15. The method of claim 14 wherein the similarity threshold comprises a lower bound of a value in a range from zero to one that represents a cosine similarity between the text and the each of the one or more texts.

16. The method of claim 14 wherein the overlap threshold comprises a value in a range from zero to one that represents the measure of words used in common by the text and the each of the one or more texts.

17. The method of claim 5 further comprising determining a similarity matrix of values representing dot-products of normalized dimensional vectors of texts in the set of texts.

18. The method of claim 5 further comprising determining an overlap matrix of values represents measures of words used in common by texts in the set of texts.

19. A computer-readable medium having computer-executable instructions for performing the method of claim 5.

20. A computer system for clustering text, comprising:

means for determining a text not to be similar to one or more texts in a set of texts;

means for excluding the text determined not to be similar from clustering with the set of texts;

means for determining at least one text in the set of texts to be a good nearest neighbor of an other text in the set of texts;

means for clustering the other text in a cluster; and

means for clustering the at least one text determined to be the good nearest neighbor of the other text in the cluster.