System for classifying words

• US 7,729,901 B2
• Filed: 12/13/2005
• Issued: 06/01/2010
• Est. Priority Date: 12/13/2005
• Status: Expired due to Fees

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Grealish, Paul J., Stipp, Eugene H., Patel, Devesh, Riise, Soren, Richardson-Bunbury, David
• Primary Examiner(s): Sked; Matthew J
• Application Number:
  US11/314,166
• Publication Number:
  US 20070136048A1
• Time in Patent Office: 1,631 Days
• Field of Search: None
• US Class Current: 704/9
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A system is disclosed for determining probable meanings of a word. A prior probability is established of the probable meanings of the word. A context frequency probability is established of the probable meanings of the word. The probability that each meaning is a correct meaning may be provided in accordance with both the prior probability and the context frequency probability.

Claims

1. A method for determining probable meanings of a word, the method comprising:

establishing a prior probability of the probable meanings of the word;

establishing a context frequency probability of the probable meaning of the word;

providing, by a processor, the probable meanings of the word including the probability that each meaning is a correct meaning in accordance with a combination of both the prior probability and the context frequency probability; and

establishing disambiguators in accordance with a text in which the word is used, where the disambiguators modify the combination of the prior probability and the context frequency probability,

wherein establishing disambiguators includes assigning at least one attribute to a context of the word and to places, and applying the context of the word when the attribute of a place in question matches attributes of the context of the word.

2. The method of claim 1, wherein the context frequency probability comprises at least one of:

the meaning of the word in a context of terms immediately preceding or immediately following the word, and

the meaning of the word in a context of terms both immediately preceding and immediately following the word.

3. The method of claim 1, wherein the prior probability comprises at least one of:

a probability that the word refers to a predetermined meaning irregardless of the context in which the word is used, and

a location related probability which includes the probability that the word refers to a location.

4. The method of claim 1, wherein the prior probability and the context frequency probability are combined using combine mathematical function.

5. The method of claim 1 wherein establishing a context frequency probability further comprises calculating a conditional probability that a given context implies a predetermined meaning of the word.

6. The method of claim 5 further comprising performing a training of the meanings of the words to determine an actual probability that a given context implies a predetermined meaning of the word.

7. The method of claim 6 further comprising applying an adjustment value to normalize the probabilities of the meaning of the words, wherein the adjustment value is determined by an iterative mathematical process, not directly.

8. The method of claim 1 wherein the disambiguators comprise at least one of:

specific disambiguators which include the meaning of the word as determined in a context of a text that includes the word, other than the text immediately preceding or following the word; and

general disambiguators which includes a term in a text that uses the word, wherein the term shares an attribute with the word being disambiguated.

9. The method of claim 1 further comprising determining an immediate small relative context to moderate a long context.

10. The method of claim 9 further comprising storing a probability value for the long context only if sufficiently different from the small context.

11. The method of claim 1, further comprising receiving input from a user, the input being indicative of the meaning of the word when the prior probability of the meaning of the word cannot be established.

13. The method of claim 1, wherein determining the prior probability of the meaning of the word further comprises determining whether the word is substantially at the beginning or the end of a sequence of words.

12. The method of claimi, further comprising

ignoring a word in a sequence of words when the established probability of the meaning of the word indicates that the word is a rare word.

14. A system to determine probable meanings of a word comprising:

means for establishing a prior probability of the probable meanings of the word;

means for establishing a context frequency probability of the probable meaning of the word;

means for providing, by a processor, the probable meanings of the word including the probability that each meaning is a correct meaning in accordance with a combination of both the prior probability and the context frequency probability; and

means for establishing disambiguators in accordance with a text in which the word is used, where the disambiguators modify the combination of the prior probability and the context frequency probability,

wherein establishing disambiguators includes assigning at least one attribute to a context of the word and to places, and applying the context of the word when the attribute of a place in question matches attributes of the context of the word.

15. The system of claim 14, wherein the context frequency probability comprises the meaning of the word in a context of terms immediately preceding or immediately following the word.

16. The system of claim 14, wherein the prior probability comprises a probability that the word refers to a predetermined meaning irregardless of the context in which the word is used.

17. The system of claim 14 further comprising means for applying an adjustment value to normalize the probabilities of the meaning of the words, wherein after the adjustment value is applied, all of the probabilities of the possible meanings of the words are summed to equal about one-hundred percent, and the adjustment value is determined by an iterative mathematical process, not directly.

18. The system of claim 14 further comprising means for establishing a disambiguators in accordance with a text in which the word is used.

19. The system of claim 18 wherein the disambiguators comprise specific disambiguators which include the meaning of the word as determined in a context of a text that includes the word, other than the text immediately preceding or following the word.

20. The system of claim 18 wherein the disambiguators comprise general disambiguators which includes a term in a text that uses the word, wherein the term shares an attribute with the word being disambiguated.