Identification and rejection of meaningless input during natural language classification

1. A method for generating a natural language statistical model comprising:

receiving, at at least one system comprising a combination of hardware and software, a set of training data comprising unigrams identified as being individually meaningless;

assigning, via the at least one system, at least a portion of the unigrams identified as being meaningless to a first n-gram class selected from a plurality of n-gram classes; and

processing the classified training data via the at least one system to generate the natural language statistical model.

2. The method according to claim 1 , wherein assigning the unigrams that are individually meaningless comprises categorizing the unigrams into at least one class selected from the group consisting of a nonsensical class and at least one ambiguous class.

3. The method according to claim 2 , wherein assigning at least a portion of the unigrams comprises assigning unigrams that are categorized as nonsensical into the first n-gram class and assigning unigrams that are categorized as ambiguous into at least a second n-gram class selected from the plurality of n-gram classes.

4. The method according to claim 1 , further comprising:

identifying bigrams that are entirely composed of meaningless unigrams;

determining whether the identified bigrams are individually meaningless; and

assigning at least a portion of the bigrams identified as being individually meaningless to the first n-gram class.

5. The method according to claim 4 , further comprising categorizing the identified bigrams into at least one class selected from the group consisting of a nonsensical class and at least one ambiguous class.

6. The method according to claim 4 , further comprising:

identifying trigrams that are entirely composed of meaningless bigrams;

determining whether the identified trigrams are individually meaningless; and

assigning at least a portion of the trigrams identified as being individually meaningless to the first n-gram class.

7. The method according to claim 6 , further comprising categorizing the identified trigrams into at least one class selected from the group consisting of a nonsensical class and at least one ambiguous class.

8. The method according to claim 1 , wherein the training data further comprise unigrams identified as being individually meaningful, and wherein the method further comprises:

identifying bigrams that comprise at least one of the unigrams identified as being meaningful; and

categorizing the identified bigrams as meaningful.

9. The method according to claim 8 , further comprising assigning at least a portion of the bigrams categorized as meaningful into a second n-gram class to which unigrams comprising the bigrams are assigned.

10. The method according to claim 1 , wherein the training data further comprise bigrams identified as being individually meaningful, the method further comprising:

identifying trigrams that comprise at least one of the bigrams identified as being meaningful; and

categorizing the identified trigrams as meaningful.

11. The method according to claim 10 , further comprising assigning at least a portion of the trigrams categorized as meaningful into a second n-gram class to which bigrams comprising the trigrams are assigned.

12. A method for generating a natural language statistical model comprising:

receiving, at at least one system comprising a combination of hardware and software, a set of training data comprising unigrams identified as being individually meaningless;

assigning, via the at least one system, at least a portion of the unigrams identified as being meaningless to a first n-gram class selected from a plurality of n-gram classes;

identifying bigrams that are entirely composed of meaningless unigrams;

determining whether the identified bigrams are individually meaningless;

assigning, via the at least one system, at least a portion of the bigrams identified as being individually meaningless to the first n-gram class; and

processing the classified training data via the at least one system to generate the natural language statistical model.

13. A machine readable storage having stored thereon a computer program having a plurality of code sections comprising:

code for identifying unigrams that are individually meaningless from a set of training data;

code for assigning at least a portion of the unigrams identified as being meaningless to a first n-gram class selected from a plurality of n-gram classes; and

code for processing the classified training data to generate at least one statistical model.

14. The machine readable storage of claim 13 , wherein the code for assigning the unigrams that are individually meaningless comprises code for categorizing the unigrams into at least one class selected from the group consisting of a nonsensical class and at least one ambiguous class.

15. The machine readable storage of claim 14 , wherein the code for assigning at least a portion of the unigrams comprises code for assigning unigrams that are categorized as nonsensical into the first n-gram class and assigning unigrams that are categorized as ambiguous into at least a second n-gram class selected from the plurality of n-gram classes.

16. The machine readable storage of claim 13 , further comprising:

code for identifying bigrams that are entirely composed of meaningless unigrams;

code for determining whether the identified bigrams are individually meaningless; and

code for assigning at least a portion of the bigrams identified as being individually meaningless to the first n-gram class.

17. The machine readable storage of claim 16 , further comprising code for categorizing the identified bigrams into at least one class selected from the group consisting of a nonsensical class and at least one ambiguous class.

18. The machine readable storage of claim 16 , further comprising:

code for identifying trigrams that are entirely composed of meaningless bigrams;

code for determining whether the identified trigrams are individually meaningless; and

code for assigning at least a portion of the trigrams identified as being individually meaningless to the first n-gram class.

19. The machine readable storage of claim 18 , further comprising code for categorizing the identified trigrams into at least one class selected from the group consisting of a nonsensical class and at least one ambiguous class.