Previous works of negation understanding mainly focus on negation cue
detection and scope resolution, without identifying negation subject which is
also significant to the downstream tasks. In this paper, we propose a new
negation triplet extraction (NTE) task which aims to extract negation subject
along with negation cue and scope. To achieve NTE, we devise a novel
Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is
built based on a generative pretrained language model (PLM) {of Encoder-Decoder
architecture} with a multi-task learning framework. Specifically, the given
sentence's syntactic dependency tree is incorporated into the PLM's encoder to
discover the correlations between the negation subject, cue and scope.
Moreover, the semantic consistency between the sentence and the extracted
triplet is ensured by an auxiliary task learning. Furthermore, we have
constructed a high-quality Chinese dataset NegComment based on the users'
reviews from the real-world platform of Meituan, upon which our evaluations
show that SSENE achieves the best NTE performance compared to the baselines.
Our ablation and case studies also demonstrate that incorporating the syntactic
information helps the PLM's recognize the distant dependency between the
subject and cue, and the auxiliary task learning is helpful to extract the
negation triplets with more semantic consistency.

本文提出了一种新的否定三元组抽取（NTE）任务，旨在提取否定主语、否定提示和范围。我们设计了一种基于生成预训练语言模型（PLM）的 Syntax&Semantic-Enhanced Negation Extraction 模型（SSENE），通过多任务学习框架，该模型通过将给定句子的句法依赖树结合到 PLM 的编码器中，来发现否定主语、提示和范围之间的关系，并通过辅助任务学习确保句子与提取的三元组之间的语义一致性。此外，我们还基于美团真实平台上用户评论构建了高质量的中文数据集 NegComment，在该数据集上的评估结果表明，与基准系统相比，SSENE 实现了最佳的 NTE 性能。通过消融实验和案例研究也证明，将句法信息纳入 PLM 有助于识别主语和提示之间的远程依赖关系，辅助任务学习有助于提取具有更多语义一致性的否定三元组。

利用句法依赖和语义一致性进行否定三元组提取

Negation Triplet Extraction with Syntactic Dependency and Semantic  Consistency

Aspect-based sentiment classification (ASC) aims to judge the sentiment
polarity conveyed by the given aspect term in a sentence. The sentiment
polarity is not only determined by the local context but also related to the
words far away from the given aspect term. Most recent efforts related to the
attention-based models can not sufficiently distinguish which words they should
pay more attention to in some cases. Meanwhile, graph-based models are coming
into ASC to encode syntactic dependency tree information. But these models do
not fully leverage syntactic dependency trees as they neglect to incorporate
dependency relation tag information into representation learning effectively.
In this paper, we address these problems by effectively modeling the local and
global features. Firstly, we design a local encoder containing: a Gaussian mask
layer and a covariance self-attention layer. The Gaussian mask layer tends to
adjust the receptive field around aspect terms adaptively to deemphasize the
effects of unrelated words and pay more attention to local information. The
covariance self-attention layer can distinguish the attention weights of
different words more obviously. Furthermore, we propose a dual-level graph
attention network as a global encoder by fully employing dependency tag
information to capture long-distance information effectively. Our model
achieves state-of-the-art performance on both SemEval 2014 and Twitter
datasets.

该研究论文提出了一种基于局部和全局特征的模型，通过设计局部编码器和双级图注意网络，有效地对面向方面的情感分类进行建模，并在 SemEval 2014 和 Twitter 数据集上取得了最先进的性能。