Similarly, we can get the probability of the end position along with its confidence score by: During training, since each sentence may contain multiple targets, we label the span boundaries for all target entities in the list T. As a result, we can obtain a vector y s 2 R (n+2) , where each element y s i indicates whether the i-th token starts a target, and also get another vector y e 2 R (n+2) for labeling the end positions.
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.