Aspect-based sentiment analysis (ABSA) have been extensively studied, but
little light has been shed on the quadruple extraction consisting of four
fundamental elements: aspects, categories, opinions and sentiments, especially
with implicit aspects and opinions. In this paper, we propose a new method
iACOS for extracting Implicit Aspects with Categories and Opinions with
Sentiments. First, iACOS appends two implicit tokens at the end of a text to
capture the context-aware representation of all tokens including implicit
aspects and opinions. Second, iACOS develops a sequence labeling model over the
context-aware token representation to co-extract explicit and implicit aspects
and opinions. Third, iACOS devises a multi-label classifier with a specialized
multi-head attention for discovering aspect-opinion pairs and predicting their
categories and sentiments simultaneously. Fourth, iACOS leverages informative
and adaptive negative examples to jointly train the multi-label classifier and
the other two classifiers on categories and sentiments by multi-task learning.
Finally, the experimental results show that iACOS significantly outperforms
other quadruple extraction baselines according to the F1 score on two public
benchmark datasets.

提出了一种新的方法 iACOS 进行提取具有情感的隐式方面和意见，并通过多任务学习联合训练多标签分类器和其他两个分类器。实验结果表明，iACOS 在两个公开基准数据集上根据 F1 分数显著优于其他四元提取基线。