Cross-Domain Labeled LDA for Cross-Domain Text Classification
TABLE VII: Case studies of different numbers of specific topics
['Task', '[ITALIC] TC', '[ITALIC] TSsrc', '[ITALIC] TStgt', 'Accuracy (%)', 'Perplexity']
To better model the different semantics of different domains and improve the model’s representation flexibility, CDL-LDA allows the numbers of topics to be different for different domains. The first rows of each task are the performances of CDL-LDA when the numbers of specific topics in both domains are the same. The second rows are the best results we can obtain by varying the number of specific topics TSsrc and TStgt. The improvements are much clear on perplexities: the perplexities are decreased by 19.0%, 15.5% and 25.2% on Comp vs. Rec, People vs. Places and Comp vs. Rec + Sci vs. Talk, respectively.