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

It is common that entity mentions can contain other mentions recursively.
This paper introduces a scalable transition-based method to model the nested
structure of mentions. We first map a sentence with nested mentions to a
designated forest where each mention corresponds to a constituent of the
forest. Our shift-reduce based system then learns to construct the forest
structure in a bottom-up manner through an action sequence whose maximal length
is guaranteed to be three times of the sentence length. Based on Stack-LSTM
which is employed to efficiently and effectively represent the states of the
system in a continuous space, our system is further incorporated with a
character-based component to capture letter-level patterns. Our model achieves
the state-of-the-art results on ACE datasets, showing its effectiveness in
detecting nested mentions.

本论文介绍了一种可扩展的过渡算法，用于模拟实体提及可以递归包含其他提及的嵌套结构，其在 ACE 数据集上的最新结果表明了其探测嵌套提及的有效性。