Open-domain targeted sentiment analysis aims to detect opinion targets along
with their sentiment polarities from a sentence. Prior work typically
formulates this task as a sequence tagging problem. However, such formulation
suffers from problems such as huge search space and sentiment inconsistency. To
address these problems, we propose a span-based extract-then-classify
framework, where multiple opinion targets are directly extracted from the
sentence under the supervision of target span boundaries, and corresponding
polarities are then classified using their span representations. We further
investigate three approaches under this framework, namely the pipeline, joint,
and collapsed models. Experiments on three benchmark datasets show that our
approach consistently outperforms the sequence tagging baseline. Moreover, we
find that the pipeline model achieves the best performance compared with the
other two models.

提出了一种基于跨度的提取 - 分类框架，其直接从句子中提取多个观点目标，并使用其跨度表示对相应的情感极性进行分类，该方法在三个基准数据集上的实验表明，该方法在情感分析中表现良好且优于以前的序列标注基线。