Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets
Table 2: Experiments for unsupervised ATE. We compare B-LSTM & CRF classifier against the rule-based baseline, an SVM classifier and the baseline of the SemEval-2014 ABSA contest.
['[EMPTY]', '[BOLD] Labels: IOB  [BOLD] P', '[BOLD] Labels: IOB  [BOLD] F1', '[BOLD] Labels: OB  [BOLD] P', '[BOLD] Labels: OB  [BOLD] F1', '[EMPTY]']
In addition, we wish to show the trade-off between precision and recall for different values of qth. We perform experiments for different values of qth and validate that the higher qth the higher the precision and the lower the recall. For example, an SVM classifier trained on an ALD with qth=0.7 achieves an F1=39.63 and P=71.54 We choose to keep qth=0.6 for the restaurant domain because we are interested in a good combination of high precision and F-score. First, the B-LSTM & CRF classifier achieves higher F-score for both domains compared to the rule-based baseline model and the SVM classifier. The F-score relative improvement between the rule-based baseline and the B-LSTM & CRF classifier is 73% and 88% for the laptop and the restaurant domain respectively. At the same time, we preserve high precision and attain values above 80%. Finally, our unsupervised method beats the supervised baseline F-score from SemEval ABSA.