Patent Document ID: 8306962
Application ID: 13342803

Base Claim:
1. A computer-implemented method comprising: constructing, by one or more processors, a classifier based on a plurality of training keywords, wherein: each training keyword comprises one or more words and has a corresponding training keyword document, and constructing the classifier comprises: for each training keyword of the plurality of training keywords, annotating the training keyword with one or more labels of a plurality of labels, each label having a corresponding label document; and for each label annotating the training keyword, determining a correctness of the label; calculating a first index-wise product between a word count vector of the training keyword document corresponding to the training keyword and a word count vector of the label document corresponding to the label; and forming a pair of the correctness and the first index-wise product; and training the classifier using one or more pairs of the correctness and the first index-wise product; and for each keyword of a plurality of keywords, the keyword being annotated with one or more labels of the plurality of labels, for each label annotating the keyword, calculating a second index-wise product between a word count vector of the keyword document corresponding to the keyword and a word count vector of the label document corresponding to the label; and predicting whether the label annotating the keyword is correct using the classifier with the second index-wise product as an input to the classifier.

---

Claim 3:
3. The method of claim 1 , further comprising: for each keyword of the plurality of keywords, for each label annotating the keyword, calculating a confidence score based on the prediction made by the classifier for the keyword and the label; accepting the label annotating the keyword when the confidence score of the keyword and the label is above a first threshold; rejecting the label annotating the keyword when the confidence score of the keyword and the label is below a second threshold; and manually reviewing the label annotating the keyword when the confidence score of the keyword and the label is between the first threshold and the second threshold.