IDENTIFICATION OF TOPICS FOR ONLINE DISCUSSIONS BASED ON LANGUAGE PATTERNS

• US 20080313180A1
• Filed: 06/14/2007
• Published: 12/18/2008
• Est. Priority Date: 06/14/2007
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Li, Hua, Chen, Zheng, Wang, Jian, Zeng, Hua-Jun, Hu, Jian, Zhang, Duo
• Granted Patent: US 7,739,261 B2
• Time in Patent Office: Days
• US Class Current: 707/6
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A topic identification system identifies topics of online discussions by iteratively identifying topic words or keywords of the online discussions and identifying language patterns associated with those keywords. The topic identification system starts out with an initial set of keywords and identifies language patterns that each include a keyword. The topic identification system then uses the identified language patterns to identify additional keywords of the online discussion that match the patterns. The topic identification system then again identifies language patterns using the keywords including the newly identified keywords. The topic identification system may repeat the process of identifying language patterns and keywords until a termination criterion is satisfied.

Claims

1. A method in a computing device for identifying keywords from a corpus of sentences of words, the method comprising:

storing an initial set of keywords;

identifying, from sentences of the corpus, patterns of words adjacent to the keywords;

identifying, from the sentences of the corpus, a new set of keywords based on the identified patterns; and

repeating the identifying of patterns and keywords until a termination criterion is satisfied.

2. The method of claim 1 including identifying sequence segments from the corpus of sentences, a sequence segment being a sequence of words adjacent to a keyword, wherein the patterns are identified from the sequence segments.

3. The method of claim 2 wherein the identifying of sequence segments is performed initially using the initial set of keywords and afterwards using the new sets of keywords.

4. The method of claim 2 including substituting a keyword symbol for the keyword of a sequence segment.

5. The method of claim 1 wherein the corpus includes content from online discussions.

6. The method of claim 5 wherein a pattern is identified as a candidate pattern when support for the pattern satisfies a pattern support criterion.

7. The method of claim 6 wherein the support for a pattern is represented by a pattern support score and the pattern support criterion is a minimum pattern support score needed for the pattern to be considered a candidate pattern.

8. The method of claim 1 wherein the identifying of the patterns applies a sequential pattern mining technique.

9. The method of claim 1 including submitting an advertisement for placement with content that relates to an identified keyword.

10. The method of claim 1 including filtering out patterns that do not satisfy a pattern confidence criterion.

11. The method of claim 1 wherein the identifying of keywords identifies as candidate keywords those keywords that satisfy a keyword confidence criterion that is based on pattern confidence scores of patterns from which a keyword was identified.

12. A computing device that identifies keywords from a corpus of sentences of words from online discussions, comprising:

a keyword store containing keywords, the keyword store having an initial set of keywords;

a corpus store containing sentences of the corpus;

a component that identifies sequence segments of the sentences of the corpus, a sequence segment being a sequence of words that includes a keyword of the keyword store;

a component that identifies, from the identified sequence segments, patterns of sequences of words that include a keyword;

a component that identifies, from the sentences of the corpus, keywords within the identified patterns and adds identified keywords to the keyword store; and

a component that determines whether a termination criterion is satisfied so that the iterative identification of sequence segments, patterns, and keywords is terminated.

13. The computing device of claim 12 including a component that substitutes a keyword symbol for the keywords of the sequence segments.

14. The computing device of claim 12 wherein the component that identifies keywords determines whether a keyword satisfies a keyword confidence criterion that is based on pattern confidence scores of patterns that match sequence segments that contain the keyword.

15. The computing device of claim 12 wherein the component that identifies patterns identifies a pattern when support for the pattern satisfies a pattern support criterion.

16. The computing device of claim 15 wherein the support for a pattern is represented by a pattern support score and the pattern support criterion is a minimum pattern support score needed for the pattern to be considered a candidate pattern.

17. The computing device of claim 12 wherein the component that identifies the patterns applies a sequential pattern mining technique.

18. A computer-readable medium containing instructions for controlling a computing device to identify topic information from a corpus of sentences of words, by a method comprising:

storing an initial set of keywords; and

repeating the steps of

identifying sequence segments of the sentences of the corpus, a sequence segment being a sequence of words that includes a keyword of the keyword store;

identifying from the identified sequence segments patterns of sequences of words that include an identified keyword and that satisfy a pattern support criterion; and

identifying from the sentences of the corpus keywords within the identified patterns that satisfy a keyword confidence criterion;

until a termination criterion is satisfied.

19. The computer-readable medium of claim 18 wherein a termination criterion is satisfied when no new patterns are identified.

20. The computer-readable medium of claim 18 wherein an identified pattern further satisfies a pattern confidence criterion.