Semantic Hilbert Space for Text Representation Learning
Table 2. Experimental Results in percentage (%). The best performed value (except for CNN/LSTM) for each dataset is in bold. where † means a significant improvement over FasText.
['[BOLD] Model', '[BOLD] CR', '[BOLD] MPQA', '[BOLD] MR', '[BOLD] SST', '[BOLD] SUBJ', '[BOLD] TREC']
In Tab. The results in Tab. In comparison with more advanced models including BiLSTM and CNN, our model generally performed better than BiLSTM with increased accuracy values on the multi-class classification dataset (TREC) and three binary text classification datasets (MR, SST & SUBJ). However, it under-performed CNN on all 6 datasets with a difference of over 2% on 3 of them (MPQA, SST & TREC), probably because that it uses fewer parameters and simpler structures.