| Challenge: | Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. |
| Approach: | They propose to refine the embeddings of targets and aspects using a sparse coefficient vector . this allows the embeds to be refined from highly correlative words instead of context-independent vectors . |
| Outcome: | Experiments show that the proposed method improves on two benchmark datasets. |
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