  Attention-based neural models were employed to detect the different aspects
and sentiment polarities of the same target in targeted aspect-based sentiment
analysis (TABSA). However, existing methods do not specifically pre-train
reasonable embeddings for targets and aspects in TABSA. This may result in
targets or aspects having the same vector representations in different contexts
and losing the context-dependent information. To address this problem, we
propose a novel method to refine the embeddings of targets and aspects. Such
pivotal embedding refinement utilizes a sparse coefficient vector to adjust the
embeddings of target and aspect from the context. Hence the embeddings of
targets and aspects can be refined from the highly correlative words instead of
using context-independent or randomly initialized vectors. Experiment results
on two benchmark datasets show that our approach yields the state-of-the-art
performance in TABSA task.
