Topics and Label Propagation:Best of Both Worlds for Weakly SupervisedText Classification
Table 2: Experimental Results
['[EMPTY]', '[ITALIC] τ', 'pc-mac', 'med-space', 'politics-sci', 'politics-rel']
We compare the LPA-TD technique with multiple baselines presented below. For the baselines requiring labelled documents, we provide them with the same number of labelled documents as number of topics to be labelled in LPA-TD. Expectation maximization with Naive Bayes for text classification proposed by Nigam et al. We then run Label Propagation on this graph and obtain the classification results for evaluation. We ensure that the most connected documents for both classes in the graph are labelled in equal proportion to ensure fairness and getting the best from this baseline. (Coherent) along with other baselines. As we can observe from the results, LPA-TD outperforms both the configurations of the OnlyLPA baseline on all datasets. It also performs better than all other baselines on two datasets - PC vs MAC and POLITICS vs SCIENCE.