Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: ascertaining, for a set of text posted via the Internet, a set of character N-grams within the set of text; determining feature values for a plurality of features of the set of text based, at least in part, on the set of character N-grams; applying a model to the feature values for the plurality of features to generate a score for the set of text, wherein the model includes a plurality of weights, each of the weights corresponding one of the features; and determining whether the set of text includes abusive language based, at least in part, on the score. 2. The method as recited in claim 1, further comprising: generating the model based, at least in part, on a training set of comments, each comment in the training set having a feature value for each of the features. 3. The method as recited in claim 2, wherein each comment in the training set of comments includes abusive language. 4. The method as recited in claim 1, wherein the plurality of features further comprise syntactic features. 5. The method as recited in claim 4, further comprising: generating a graph or tree representing the set of text; and determining the syntactic features based, at least in part, on the graph or tree. 6. The method as recited in claim 1, wherein the plurality of features further comprise semantic or linguistic features. 7. The method as recited in claim 1, wherein N is less than 10. 8. A non-transitory computer-readable storage medium storing thereon computer-readable instructions configured to: ascertain, for a set of text posted via the Internet, a set of character N-grams within the set of text; determine feature values for a plurality of features of the set of text based, at least in part, on the set of character N-grams; apply a model to the feature values for the plurality of features to generate a score for the set of text, wherein the model includes a plurality of weights, each of the weights corresponding one of the features; and determine whether the set of text includes abusive language based, at least in part, on the score. 9. The non-transitory computer-readable storage medium as recited in claim 8, the computer-readable instructions being further configured to: generate the model based, at least in part, on a training set of comments, each comment in the training set having a feature value for each of the features. 10. The non-transitory computer-readable storage medium as recited in claim 9, wherein each comment in the training set of comments includes abusive language. 11. The non-transitory computer-readable storage medium as recited in claim 8, wherein the plurality of features comprise syntactic features. 12. The non-transitory computer-readable storage medium as recited in claim 11, the instructions being further configured to: generate a graph or tree representing the set of text; and determine the syntactic features based, at least in part, on the graph or tree. 13. The non-transitory computer-readable storage medium as recited in 8, wherein N is less than 10. 14. An apparatus, comprising: a processor; and a memory, at least one of the processor or the memory being configured to: ascertain, for a set of text posted via the Internet, a set of character N-grams within the set of text; determine feature values for a plurality of features of the set of text based, at least in part, on the set of character N-grams; apply a model to the feature values for the plurality of features to generate a score for the set of text, wherein the model includes a plurality of weights, each of the weights corresponding one of the features; and determine whether the set of text includes abusive language based, at least in part, on the score. 15. The apparatus as recited in claim 14, at least one of the processor or memory being further configured to: generate the model based, at least in part, on a training set of comments, each comment in the training set having a feature value for each of the features. 16. The apparatus as recited in claim 15, wherein each comment in the training set of comments includes abusive language. 17. The apparatus as recited in claim 14, wherein the plurality of features comprise syntactic features. 18. The apparatus as recited in claim 17, at least one of the processor or the memory being further configured to: generate a graph or tree representing the set of text; and determine the syntactic features based, at least in part, on the graph or tree. 19. The apparatus as recited in claim 14, wherein the plurality of features further comprise semantic or linguistic features. 20. The apparatus as recited in claim 14, wherein N is less than 10.