iACOS: Advancing Implicit Sentiment Extraction with Informative and
  Adaptive Negative Examples
Xiancai Xu Jia-Dong Zhang Lei Xiong Zhishang Liu
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.