Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
Table 12: Results for AESC on Restaurants
['[BOLD] Model', '[BOLD] Emb.', '[BOLD] Tagging F1 single', '[BOLD] Tagging F1 joint', '[BOLD] Classification F1 +', '[BOLD] Classification F1 −', '[BOLD] Classification F1 0']
For this case, we only used the Senna pipeline, as it provided better results for AE. We found that in the baseline RNNs SennaEmbeddings outperformed both other embeddings with average absolute gains of 2.37 (p=7.2×10−4) and 3.36 (p=1.19×10−6) F1-score WikiDeps and GoogleNews, respectively.