1. A method of identifying attributes in a sentence, the method comprising: representing the sentence as a first feature vector; training a binary classifier a first time with a support vector machine based classification model using a one-against-all mechanism, based upon the first feature vector; obtaining a vector of probability for the sentence, wherein the vector of probability for the sentence stores a probability of the sentence belonging to an attribute category; concatenating the first feature vector with the vector of probability to obtain a second feature vector; and training the binary classifier a second time with the support vector machine based classification model using the one-against-all mechanism, based upon the second feature vector.
1. A method of identifying attributes in a sentence, the method comprising: representing the sentence as a first feature vector; training a binary classifier a first time with a support vector machine based classification model using a one-against-all mechanism, based upon the first feature vector; obtaining a vector of probability for the sentence, wherein the vector of probability for the sentence stores a probability of the sentence belonging to an attribute category; concatenating the first feature vector with the vector of probability to obtain a second feature vector; and training the binary classifier a second time with the support vector machine based classification model using the one-against-all mechanism, based upon the second feature vector. 4. The method as recited in claim 1 , wherein representing the sentence further comprises a norm of the first feature vector being equal to one.