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。

iACOS：通过信息丰富和自适应负例推进隐式情感分析

iACOS: Advancing Implicit Sentiment Extraction with Informative and  Adaptive Negative Examples

Aspect-based sentiment analysis is a long-standing research interest in the
field of opinion mining, and in recent years, researchers have gradually
shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA
tasks. However, the datasets currently used in the research are limited to
individual elements of specific tasks, usually focusing on in-domain settings,
ignoring implicit aspects and opinions, and with a small data scale. To address
these issues, we propose a large-scale Multi-Element Multi-Domain dataset
(MEMD) that covers the four elements across five domains, including nearly
20,000 review sentences and 30,000 quadruples annotated with explicit and
implicit aspects and opinions for ABSA research. Meanwhile, we evaluate
generative and non-generative baselines on multiple ABSA subtasks under the
open domain setting, and the results show that open domain ABSA as well as
mining implicit aspects and opinions remain ongoing challenges to be addressed.
The datasets are publicly released at https://github.com/NUSTM/MEMD-ABSA.

提出了一个大规模的多元素多领域数据集（MEMD），覆盖了包括近 20,000 个评论句子和 30,000 个四元组在内的四个元素的五个领域，评估了开放域 ABSA 的生成和非生成基线，并发现开放域 ABSA 和挖掘隐含方面和观点仍然是需要解决的问题。