7. 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 an unlabeled dataset, generating a plurality of clusters from the dataset; generating a vector representation of a cluster; generating a subset of the vector representation of the cluster; determining a similarity value between the subset of the 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.
7. 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 an unlabeled dataset, generating a plurality of clusters from the dataset; generating a vector representation of a cluster; generating a subset of the vector representation of the cluster; determining a similarity value between the subset of the 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. 11. The computer-implemented method of claim 7 , wherein determining the similarity value between the subset of the vector representation and the input text data comprises: computing a cosine similarity between the subset of the vector representation and the input text data.