Learning Concept Embeddings for Dataless Classification via Efficient Bag of Concepts Densification
Table 8: Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved.
['Method ESA', 'Sport x Politics 90.63', 'Sport x Politics @425', 'Sport x Religion 94.39', 'Sport x Religion @450']
Classification at the top level is easier than the fine-grained level. Nevertheless, as with fine-grained classification, ESA still peaks with a few hundred dimensions of the sparse BoC vector. Both the CRX & CCX models achieve equal performance to ESA at very few dimensions (≤6). Densification using the CRX model embeddings still performs the best on both tasks. Interestingly, the CCX model gives very close F1 scores to the CRX model at less dimensions (@4 with Sport vs. Politics, and @60 with Sport vs. Religion) indicating its competitive advantage when training computational cost is a decisive criteria. The CCX model, still performs better than ESA, WEmax, and WEhung on both tasks. Bootstrapping did not improve the results on this task significantly (if any).