Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation
Table 4: F1-scores of ablation experiments on BiDTreeCRF.
['[BOLD] Models', '[BOLD] L-14', '[BOLD] R-14', '[BOLD] R-15', '[BOLD] R-16']
To test the effect of each component of BiDTreeCRF, the following ablation experiments on different layers of BiDTreeCRF#3 are performed: (1) DTree-up: The bottom-up propagation of BiDTree is connected to BiLSTM and the CRF layer. (2) DTree-down: The top-down propagation of BiDTree is connected to BiLSTM and the CRF layer. (3) BiDTree+CRF: BiLSTM layer is not used compared to BiDTreeCRF. The initial word embeddings are the same as before. Comparing BiDTreeCRF with DTree-up and DTree-down, it is obvious that BiDTree is more competitive than any single directional dependency network, which is the original motivation of the proposed BiDTreeCRF. The fact that BiDTreeCRF outperforms BiDTree+CRF indicates the BiLSTM layer is effective in extracting sequential information on top of BiDTree. On the other hand, the fact that BiDTreeCRF outperforms BiLSTM+CRF shows that the dependency syntactic information extracted by BiDTree is extremely useful in the aspect term extraction task. All above improvements are significant (p<0.05) with the statistical t-test.