Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
Table 3: Results of our implemented baseline RNN models on the Laptops dataset.
['[BOLD] Model', '[BOLD] Emb.', '| [ITALIC] h|', '[BOLD] feat', '[BOLD] F1', '[BOLD] F1*']
For contrast we include the best F1-scores obtained by Liu et al. (cf. F1 * columns). We observed the CoreNLP pipeline outperformed the Senna pipeline, with an average absolute gain of 2.105%, significant at p=1.29×10−5, and binary features proved useful offering average absolute gains of 1.538% (p=1.29×10−5). Finally, note that the best configurations always use SennaEmbeddings, which outperformed others significantly for each case.