Concept-Based Embeddingsfor Natural Language Processing
Table 6.4: System performance on Semeval-2015 dataset
['[EMPTY]', 'Aspect Categorization Strict Acc.', 'Aspect Categorization Macro F1', 'Aspect Categorization Micro F1', 'Sentiment Sentiment Acc.']
-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015.