1. A computer-implemented method for classifying input text data by obtaining information from multiple datasets, the method comprising the steps of: receiving, at a computer, the input text data; accessing, at the computer, a plurality of heterogeneous datasets, the plurality of heterogeneous datasets each including text data; generating, at the computer, a set of features from the plurality of heterogeneous datasets, the set of features including one or more features from each of the plurality of heterogeneous datasets; selecting, at the computer, one or more classification features from the set of features; and generating, at the computer, an augmented input text data by combining the input text data and the one or more classification features; and applying, at the computer, a classifier to the augmented input text data to associate the input text data with a category.
3. The computer-implemented method of claim 1 , wherein generating the set of features from the plurality of heterogeneous datasets comprises: determining whether a dataset is a labeled dataset or an unlabeled dataset; responsive to determining the dataset is a labeled dataset, generating a vector representation of a category of the dataset; generating a reduced vector representation by truncating the vector representation of the category of the dataset; determining a similarity value between the reduced vector representation and the input text data; and responsive to the similarity value exceeding a threshold value, including the reduced vector representation in the set of features.