Aspect based sentiment analysis, predicting sentiment polarity of given
aspects, has drawn extensive attention. Previous attention-based models
emphasize using aspect semantics to help extract opinion features for
classification. However, these works are either not able to capture opinion
spans as a whole, or not able to capture variable-length opinion spans. In this
paper, we present a neat and effective structured attention model by
aggregating multiple linear-chain CRFs. Such a design allows the model to
extract aspect-specific opinion spans and then evaluate sentiment polarity by
exploiting the extracted opinion features. The experimental results on four
datasets demonstrate the effectiveness of the proposed model, and our analysis
demonstrates that our model can capture aspect-specific opinion spans.

本文提出了一种基于多个线性链 CRFs 的详细有效的结构化关注模型，通过聚合多个线性链 CRFs，该设计允许模型提取特定方面的意见跨度，然后利用提取的意见特征评估情感极性，实验证明该模型是有效的。

基于方面特定观点跨度的方面情感分析

Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

This paper focuses on the study of recognizing discontiguous entities.
Motivated by a previous work, we propose to use a novel hypergraph
representation to jointly encode discontiguous entities of unbounded length,
which can overlap with one another. To compare with existing approaches, we
first formally introduce the notion of model ambiguity, which defines the
difficulty level of interpreting the outputs of a model, and then formally
analyze the theoretical advantages of our model over previous existing
approaches based on linear-chain CRFs. Our empirical results also show that our
model is able to achieve significantly better results when evaluated on
standard data with many discontiguous entities.

该研究聚焦于识别不连续实体，通过使用新的超图表示方法来编码不连续且重叠的实体，形成一个独特的模型。研究结果表明，该模型在处理大量不连续实体的数据时显示出较强的优势。