Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network
Table 2: Performance on aspect specific opinion expression task on Precision, Recall, F1 for different models when initialized using various pre-trained embeddings
['Model', 'Senna P', 'Senna R', 'Senna F', 'word2vec P', 'word2vec R', 'word2vec F', 'Glove P', 'Glove R', 'Glove F']
which shows including the aspect information using the attention is effective even when features based on POS and syntactic tags (phrase chunk units) are included as input. Our complete model outperform LSTM-ATT significantly which shows that adding the CRF layer to capture the dependency among output label is useful. Also interesting to note is that Senna embeddings perform best for aspect specific opinion expression. This is due to the fact that Senna was trained on various NLP task such as NER, POS and SRL whereas other Glove, word2vec were trained on general co-occurence of words.