Structured sentiment analysis (SSA) aims to automatically extract people's
opinions from a text in natural language and adequately represent that
information in a graph structure. One of the most accurate methods for
performing SSA was recently proposed and consists of approaching it as a
dependency parsing task. Although we can find in the literature how
transition-based algorithms excel in dependency parsing in terms of accuracy
and efficiency, all proposed attempts to tackle SSA following that approach
were based on graph-based models. In this article, we present the first
transition-based method to address SSA as dependency parsing. Specifically, we
design a transition system that processes the input text in a left-to-right
pass, incrementally generating the graph structure containing all identified
opinions. To effectively implement our final transition-based model, we resort
to a Pointer Network architecture as a backbone. From an extensive evaluation,
we demonstrate that our model offers the best performance to date in
practically all cases among prior dependency-based methods, and surpass recent
task-specific techniques on the most challenging datasets. We additionally
include an in-depth analysis and empirically prove that the overall
time-complexity cost of our approach is quadratic in the sentence length, being
more efficient than top-performing graph-based parsers.

本文提出一种基于转移的方法，使用指针网络架构实现，用于将文本中的观点作为依存句法结构提取并有效地表示。作者进行了详细评估，证明了该方法在多数情况下具有目前最好的表现，并在最具挑战性的数据集上超越了最新的任务特定技术。此外，作者还进行了深入的分析，实验表明本方法的时间复杂度成二次方，比表现最佳的基于图的解析器更高效。

基于转移依存句法分析的结构化情感分析

Structured Sentiment Analysis as Transition-based Dependency Parsing

We propose a transition-based approach that, by training a single model, can
efficiently parse any input sentence with both constituent and dependency
trees, supporting both continuous/projective and discontinuous/non-projective
syntactic structures. To that end, we develop a Pointer Network architecture
with two separate task-specific decoders and a common encoder, and follow a
multitask learning strategy to jointly train them. The resulting quadratic
system, not only becomes the first parser that can jointly produce both
unrestricted constituent and dependency trees from a single model, but also
proves that both syntactic formalisms can benefit from each other during
training, achieving state-of-the-art accuracies in several widely-used
benchmarks such as the continuous English and Chinese Penn Treebanks, as well
as the discontinuous German NEGRA and TIGER datasets.

提出了一种基于转换的方法，通过训练单个模型，可以有效地解析任何输入句子，支持连续 / 投影和不连续 / 非投影的句法结构，并证明了这两种句法形式可以在训练过程中互相受益，提高了在多个基准测试中的准确性，如英语和中文 Penn 树库以及德语 NEGRA 和 TIGER 数据集。